diff --git a/Makefile b/Makefile
index 64bb0b57a..c4e77d101 100644
--- a/Makefile
+++ b/Makefile
@@ -1,11 +1,15 @@
 SHELL := /bin/bash
-PYVER := 3.6
-VENV := ./env$(PYVER)
 
 ifndef SPACY_EXTRAS
 override SPACY_EXTRAS = spacy-lookups-data jieba pkuseg==0.0.25 sudachipy sudachidict_core
 endif
 
+ifndef PYVER
+override PYVER = 3.6
+endif
+
+VENV := ./env$(PYVER)
+
 version := $(shell "bin/get-version.sh")
 package := $(shell "bin/get-package.sh")
 
@@ -13,9 +17,15 @@ ifndef SPACY_BIN
 override SPACY_BIN = $(package)-$(version).pex
 endif
 
-dist/$(SPACY_BIN) : wheelhouse/spacy-$(version).stamp
+ifndef WHEELHOUSE
+override WHEELHOUSE = "./wheelhouse"
+endif
+
+
+
+dist/$(SPACY_BIN) : $(WHEELHOUSE)/spacy-$(PYVER)-$(version).stamp
 	$(VENV)/bin/pex \
-		-f ./wheelhouse \
+		-f $(WHEELHOUSE) \
 		--no-index \
 		--disable-cache \
 		-m spacy \
@@ -25,22 +35,23 @@ dist/$(SPACY_BIN) : wheelhouse/spacy-$(version).stamp
 	chmod a+rx $@
 	cp $@ dist/spacy.pex
 
-dist/pytest.pex : wheelhouse/pytest-*.whl
-	$(VENV)/bin/pex -f ./wheelhouse --no-index --disable-cache -m pytest -o $@ pytest pytest-timeout mock
+dist/pytest.pex : $(WHEELHOUSE)/pytest-*.whl
+	$(VENV)/bin/pex -f $(WHEELHOUSE) --no-index --disable-cache -m pytest -o $@ pytest pytest-timeout mock
 	chmod a+rx $@
 
-wheelhouse/spacy-$(version).stamp : $(VENV)/bin/pex setup.py spacy/*.py* spacy/*/*.py*
-	$(VENV)/bin/pip wheel . -w ./wheelhouse
-	$(VENV)/bin/pip wheel $(SPACY_EXTRAS) -w ./wheelhouse
+$(WHEELHOUSE)/spacy-$(PYVER)-$(version).stamp : $(VENV)/bin/pex setup.py spacy/*.py* spacy/*/*.py*
+	$(VENV)/bin/pip wheel . -w $(WHEELHOUSE)
+	$(VENV)/bin/pip wheel $(SPACY_EXTRAS) -w $(WHEELHOUSE)
 
 	touch $@
 
-wheelhouse/pytest-%.whl : $(VENV)/bin/pex
-	$(VENV)/bin/pip wheel pytest pytest-timeout mock -w ./wheelhouse
+$(WHEELHOUSE)/pytest-%.whl : $(VENV)/bin/pex
+	$(VENV)/bin/pip wheel pytest pytest-timeout mock -w $(WHEELHOUSE)
 
 $(VENV)/bin/pex :
 	python$(PYVER) -m venv $(VENV)
 	$(VENV)/bin/pip install -U pip setuptools pex wheel
+	$(VENV)/bin/pip install numpy
 
 .PHONY : clean test
 
@@ -50,6 +61,6 @@ test : dist/spacy-$(version).pex dist/pytest.pex
 
 clean : setup.py
 	rm -rf dist/*
-	rm -rf ./wheelhouse
+	rm -rf $(WHEELHOUSE)/*
 	rm -rf $(VENV)
 	python setup.py clean --all
diff --git a/extra/experiments/onto-joint/defaults.cfg b/extra/experiments/onto-joint/defaults.cfg
index 3ab3ddaba..7954b57b5 100644
--- a/extra/experiments/onto-joint/defaults.cfg
+++ b/extra/experiments/onto-joint/defaults.cfg
@@ -36,7 +36,7 @@ max_length = 0
 limit = 0
 
 [training.batcher]
-@batchers = "batch_by_words.v1"
+@batchers = "spacy.batch_by_words.v1"
 discard_oversize = false
 tolerance = 0.2
 
diff --git a/extra/experiments/ptb-joint-pos-dep/defaults.cfg b/extra/experiments/ptb-joint-pos-dep/defaults.cfg
index fc471ac43..8f9c5666e 100644
--- a/extra/experiments/ptb-joint-pos-dep/defaults.cfg
+++ b/extra/experiments/ptb-joint-pos-dep/defaults.cfg
@@ -35,7 +35,7 @@ max_length = 0
 limit = 0
 
 [training.batcher]
-@batchers = "batch_by_words.v1"
+@batchers = "spacy.batch_by_words.v1"
 discard_oversize = false
 tolerance = 0.2
 
diff --git a/netlify.toml b/netlify.toml
index 2f3e350e6..3c17b876c 100644
--- a/netlify.toml
+++ b/netlify.toml
@@ -24,7 +24,7 @@ redirects = [
     {from = "/docs/usage/customizing-tokenizer", to = "/usage/linguistic-features#tokenization", force = true},
     {from = "/docs/usage/language-processing-pipeline", to = "/usage/processing-pipelines", force = true},
     {from = "/docs/usage/customizing-pipeline", to = "/usage/processing-pipelines", force = true},
-    {from = "/docs/usage/training-ner", to = "/usage/training#ner", force = true},
+    {from = "/docs/usage/training-ner", to = "/usage/training", force = true},
     {from = "/docs/usage/tutorials", to = "/usage/examples", force = true},
     {from = "/docs/usage/data-model", to = "/api", force = true},
     {from = "/docs/usage/cli", to = "/api/cli", force = true},
diff --git a/spacy/about.py b/spacy/about.py
index 418e44c1d..3fe720dbc 100644
--- a/spacy/about.py
+++ b/spacy/about.py
@@ -1,6 +1,6 @@
 # fmt: off
 __title__ = "spacy-nightly"
-__version__ = "3.0.0a12"
+__version__ = "3.0.0a13"
 __release__ = True
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py
index b47c1c16b..92cb76971 100644
--- a/spacy/cli/__init__.py
+++ b/spacy/cli/__init__.py
@@ -29,9 +29,9 @@ from .project.document import project_document  # noqa: F401
 
 @app.command("link", no_args_is_help=True, deprecated=True, hidden=True)
 def link(*args, **kwargs):
-    """As of spaCy v3.0, model symlinks are deprecated. You can load models
-    using their full names or from a directory path."""
+    """As of spaCy v3.0, symlinks like "en" are deprecated. You can load trained
+    pipeline packages using their full names or from a directory path."""
     msg.warn(
-        "As of spaCy v3.0, model symlinks are deprecated. You can load models "
-        "using their full names or from a directory path."
+        "As of spaCy v3.0, model symlinks are deprecated. You can load trained "
+        "pipeline packages using their full names or from a directory path."
     )
diff --git a/spacy/cli/_util.py b/spacy/cli/_util.py
index 16e257ce2..0ecb5ad8f 100644
--- a/spacy/cli/_util.py
+++ b/spacy/cli/_util.py
@@ -25,7 +25,7 @@ COMMAND = "python -m spacy"
 NAME = "spacy"
 HELP = """spaCy Command-line Interface
 
-DOCS: https://spacy.io/api/cli
+DOCS: https://nightly.spacy.io/api/cli
 """
 PROJECT_HELP = f"""Command-line interface for spaCy projects and templates.
 You'd typically start by cloning a project template to a local directory and
@@ -36,7 +36,7 @@ DEBUG_HELP = """Suite of helpful commands for debugging and profiling. Includes
 commands to check and validate your config files, training and evaluation data,
 and custom model implementations.
 """
-INIT_HELP = """Commands for initializing configs and models."""
+INIT_HELP = """Commands for initializing configs and pipeline packages."""
 
 # Wrappers for Typer's annotations. Initially created to set defaults and to
 # keep the names short, but not needed at the moment.
@@ -297,9 +297,7 @@ def ensure_pathy(path):
     return Pathy(path)
 
 
-def git_sparse_checkout(
-    repo: str, subpath: str, dest: Path, *, branch: Optional[str] = None
-):
+def git_sparse_checkout(repo: str, subpath: str, dest: Path, *, branch: str = "master"):
     if dest.exists():
         msg.fail("Destination of checkout must not exist", exits=1)
     if not dest.parent.exists():
@@ -323,21 +321,30 @@ def git_sparse_checkout(
         # This is the "clone, but don't download anything" part.
         cmd = (
             f"git clone {repo} {tmp_dir} --no-checkout --depth 1 "
-            "--filter=blob:none"  # <-- The key bit
+            f"--filter=blob:none "  # <-- The key bit
+            f"-b {branch}"
         )
-        if branch is not None:
-            cmd = f"{cmd} -b {branch}"
         run_command(cmd, capture=True)
         # Now we need to find the missing filenames for the subpath we want.
         # Looking for this 'rev-list' command in the git --help? Hah.
         cmd = f"git -C {tmp_dir} rev-list --objects --all --missing=print -- {subpath}"
         ret = run_command(cmd, capture=True)
-        missings = "\n".join([x[1:] for x in ret.stdout.split() if x.startswith("?")])
+        repo = _from_http_to_git(repo)
         # Now pass those missings into another bit of git internals
-        run_command(
-            f"git -C {tmp_dir} fetch-pack --stdin {repo}", capture=True, stdin=missings
-        )
+        missings = " ".join([x[1:] for x in ret.stdout.split() if x.startswith("?")])
+        cmd = f"git -C {tmp_dir} fetch-pack {repo} {missings}"
+        run_command(cmd, capture=True)
         # And finally, we can checkout our subpath
-        run_command(f"git -C {tmp_dir} checkout {branch} {subpath}")
+        cmd = f"git -C {tmp_dir} checkout {branch} {subpath}"
+        run_command(cmd)
         # We need Path(name) to make sure we also support subdirectories
         shutil.move(str(tmp_dir / Path(subpath)), str(dest))
+
+
+def _from_http_to_git(repo):
+    if repo.startswith("http://"):
+        repo = repo.replace(r"http://", r"https://")
+    if repo.startswith(r"https://"):
+        repo = repo.replace("https://", "git@").replace("/", ":", 1)
+        repo = f"{repo}.git"
+    return repo
diff --git a/spacy/cli/convert.py b/spacy/cli/convert.py
index f73c2f2c0..ade5a3ad4 100644
--- a/spacy/cli/convert.py
+++ b/spacy/cli/convert.py
@@ -44,7 +44,7 @@ def convert_cli(
     file_type: FileTypes = Opt("spacy", "--file-type", "-t", help="Type of data to produce"),
     n_sents: int = Opt(1, "--n-sents", "-n", help="Number of sentences per doc (0 to disable)"),
     seg_sents: bool = Opt(False, "--seg-sents", "-s", help="Segment sentences (for -c ner)"),
-    model: Optional[str] = Opt(None, "--model", "-b", help="Model for sentence segmentation (for -s)"),
+    model: Optional[str] = Opt(None, "--model", "--base", "-b", help="Trained spaCy pipeline for sentence segmentation to use as base (for --seg-sents)"),
     morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
     merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
     converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
@@ -61,6 +61,8 @@ def convert_cli(
     If no output_dir is specified and the output format is JSON, the data
     is written to stdout, so you can pipe them forward to a JSON file:
     $ spacy convert some_file.conllu --file-type json > some_file.json
+
+    DOCS: https://nightly.spacy.io/api/cli#convert
     """
     if isinstance(file_type, FileTypes):
         # We get an instance of the FileTypes from the CLI so we need its string value
@@ -261,6 +263,6 @@ def _get_converter(msg, converter, input_path):
             msg.warn(
                 "Can't automatically detect NER format. "
                 "Conversion may not succeed. "
-                "See https://spacy.io/api/cli#convert"
+                "See https://nightly.spacy.io/api/cli#convert"
             )
     return converter
diff --git a/spacy/cli/debug_config.py b/spacy/cli/debug_config.py
index 2944cd364..7930d0674 100644
--- a/spacy/cli/debug_config.py
+++ b/spacy/cli/debug_config.py
@@ -31,6 +31,8 @@ def debug_config_cli(
     Similar as with the 'train' command, you can override settings from the config
     as command line options. For instance, --training.batch_size 128 overrides
     the value of "batch_size" in the block "[training]".
+
+    DOCS: https://nightly.spacy.io/api/cli#debug-config
     """
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
diff --git a/spacy/cli/debug_data.py b/spacy/cli/debug_data.py
index 2f48a29cd..75a81e6f5 100644
--- a/spacy/cli/debug_data.py
+++ b/spacy/cli/debug_data.py
@@ -18,7 +18,7 @@ from .. import util
 NEW_LABEL_THRESHOLD = 50
 # Minimum number of expected occurrences of dependency labels
 DEP_LABEL_THRESHOLD = 20
-# Minimum number of expected examples to train a blank model
+# Minimum number of expected examples to train a new pipeline
 BLANK_MODEL_MIN_THRESHOLD = 100
 BLANK_MODEL_THRESHOLD = 2000
 
@@ -47,6 +47,8 @@ def debug_data_cli(
     Analyze, debug and validate your training and development data. Outputs
     useful stats, and can help you find problems like invalid entity annotations,
     cyclic dependencies, low data labels and more.
+
+    DOCS: https://nightly.spacy.io/api/cli#debug-data
     """
     if ctx.command.name == "debug-data":
         msg.warn(
@@ -148,7 +150,7 @@ def debug_data(
     msg.text(f"Language: {config['nlp']['lang']}")
     msg.text(f"Training pipeline: {', '.join(pipeline)}")
     if resume_components:
-        msg.text(f"Components from other models: {', '.join(resume_components)}")
+        msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
     if frozen_components:
         msg.text(f"Frozen components: {', '.join(frozen_components)}")
     msg.text(f"{len(train_dataset)} training docs")
@@ -164,9 +166,7 @@ def debug_data(
     # TODO: make this feedback more fine-grained and report on updated
     # components vs. blank components
     if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
-        text = (
-            f"Low number of examples to train from a blank model ({len(train_dataset)})"
-        )
+        text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
         if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
             msg.fail(text)
         else:
@@ -214,7 +214,7 @@ def debug_data(
             show=verbose,
         )
     else:
-        msg.info("No word vectors present in the model")
+        msg.info("No word vectors present in the package")
 
     if "ner" in factory_names:
         # Get all unique NER labels present in the data
diff --git a/spacy/cli/debug_model.py b/spacy/cli/debug_model.py
index ed8d54655..5bd4e008f 100644
--- a/spacy/cli/debug_model.py
+++ b/spacy/cli/debug_model.py
@@ -30,6 +30,8 @@ def debug_model_cli(
     """
     Analyze a Thinc model implementation. Includes checks for internal structure
     and activations during training.
+
+    DOCS: https://nightly.spacy.io/api/cli#debug-model
     """
     if use_gpu >= 0:
         msg.info("Using GPU")
diff --git a/spacy/cli/download.py b/spacy/cli/download.py
index e55e6e40e..036aeab17 100644
--- a/spacy/cli/download.py
+++ b/spacy/cli/download.py
@@ -17,16 +17,19 @@ from ..errors import OLD_MODEL_SHORTCUTS
 def download_cli(
     # fmt: off
     ctx: typer.Context,
-    model: str = Arg(..., help="Name of model to download"),
+    model: str = Arg(..., help="Name of pipeline package to download"),
     direct: bool = Opt(False, "--direct", "-d", "-D", help="Force direct download of name + version"),
     # fmt: on
 ):
     """
-    Download compatible model from default download path using pip. If --direct
-    flag is set, the command expects the full model name with version.
-    For direct downloads, the compatibility check will be skipped. All
+    Download compatible trained pipeline from the default download path using
+    pip. If --direct flag is set, the command expects the full package name with
+    version. For direct downloads, the compatibility check will be skipped. All
     additional arguments provided to this command will be passed to `pip install`
-    on model installation.
+    on package installation.
+
+    DOCS: https://nightly.spacy.io/api/cli#download
+    AVAILABLE PACKAGES: https://spacy.io/models
     """
     download(model, direct, *ctx.args)
 
@@ -34,11 +37,11 @@ def download_cli(
 def download(model: str, direct: bool = False, *pip_args) -> None:
     if not is_package("spacy") and "--no-deps" not in pip_args:
         msg.warn(
-            "Skipping model package dependencies and setting `--no-deps`. "
+            "Skipping pipeline package dependencies and setting `--no-deps`. "
             "You don't seem to have the spaCy package itself installed "
             "(maybe because you've built from source?), so installing the "
-            "model dependencies would cause spaCy to be downloaded, which "
-            "probably isn't what you want. If the model package has other "
+            "package dependencies would cause spaCy to be downloaded, which "
+            "probably isn't what you want. If the pipeline package has other "
             "dependencies, you'll have to install them manually."
         )
         pip_args = pip_args + ("--no-deps",)
@@ -53,7 +56,7 @@ def download(model: str, direct: bool = False, *pip_args) -> None:
         if model in OLD_MODEL_SHORTCUTS:
             msg.warn(
                 f"As of spaCy v3.0, shortcuts like '{model}' are deprecated. Please"
-                f"use the full model name '{OLD_MODEL_SHORTCUTS[model]}' instead."
+                f"use the full pipeline package name '{OLD_MODEL_SHORTCUTS[model]}' instead."
             )
             model_name = OLD_MODEL_SHORTCUTS[model]
         compatibility = get_compatibility()
@@ -61,7 +64,7 @@ def download(model: str, direct: bool = False, *pip_args) -> None:
         download_model(dl_tpl.format(m=model_name, v=version), pip_args)
     msg.good(
         "Download and installation successful",
-        f"You can now load the model via spacy.load('{model_name}')",
+        f"You can now load the package via spacy.load('{model_name}')",
     )
 
 
@@ -71,16 +74,16 @@ def get_compatibility() -> dict:
     if r.status_code != 200:
         msg.fail(
             f"Server error ({r.status_code})",
-            f"Couldn't fetch compatibility table. Please find a model for your spaCy "
+            f"Couldn't fetch compatibility table. Please find a package for your spaCy "
             f"installation (v{about.__version__}), and download it manually. "
             f"For more details, see the documentation: "
-            f"https://spacy.io/usage/models",
+            f"https://nightly.spacy.io/usage/models",
             exits=1,
         )
     comp_table = r.json()
     comp = comp_table["spacy"]
     if version not in comp:
-        msg.fail(f"No compatible models found for v{version} of spaCy", exits=1)
+        msg.fail(f"No compatible packages found for v{version} of spaCy", exits=1)
     return comp[version]
 
 
@@ -88,7 +91,7 @@ def get_version(model: str, comp: dict) -> str:
     model = get_base_version(model)
     if model not in comp:
         msg.fail(
-            f"No compatible model found for '{model}' (spaCy v{about.__version__})",
+            f"No compatible package found for '{model}' (spaCy v{about.__version__})",
             exits=1,
         )
     return comp[model][0]
diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index 3847c74f3..c5cbab09a 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -26,13 +26,16 @@ def evaluate_cli(
     # fmt: on
 ):
     """
-    Evaluate a model. Expects a loadable spaCy model and evaluation data in the
-    binary .spacy format. The --gold-preproc option sets up the evaluation
-    examples with gold-standard sentences and tokens for the predictions. Gold
-    preprocessing helps the annotations align to the tokenization, and may
-    result in sequences of more consistent length. However, it may reduce
-    runtime accuracy due to train/test skew. To render a sample of dependency
-    parses in a HTML file, set as output directory as the displacy_path argument.
+    Evaluate a trained pipeline. Expects a loadable spaCy pipeline and evaluation
+    data in the binary .spacy format. The --gold-preproc option sets up the
+    evaluation examples with gold-standard sentences and tokens for the
+    predictions. Gold preprocessing helps the annotations align to the
+    tokenization, and may result in sequences of more consistent length. However,
+    it may reduce runtime accuracy due to train/test skew. To render a sample of
+    dependency parses in a HTML file, set as output directory as the
+    displacy_path argument.
+
+    DOCS: https://nightly.spacy.io/api/cli#evaluate
     """
     evaluate(
         model,
diff --git a/spacy/cli/info.py b/spacy/cli/info.py
index ca082b939..2b87163c2 100644
--- a/spacy/cli/info.py
+++ b/spacy/cli/info.py
@@ -12,15 +12,17 @@ from .. import about
 @app.command("info")
 def info_cli(
     # fmt: off
-    model: Optional[str] = Arg(None, help="Optional model name"),
+    model: Optional[str] = Arg(None, help="Optional loadable spaCy pipeline"),
     markdown: bool = Opt(False, "--markdown", "-md", help="Generate Markdown for GitHub issues"),
     silent: bool = Opt(False, "--silent", "-s", "-S", help="Don't print anything (just return)"),
     # fmt: on
 ):
     """
-    Print info about spaCy installation. If a model is speficied as an argument,
-    print model information. Flag --markdown prints details in Markdown for easy
+    Print info about spaCy installation. If a pipeline is speficied as an argument,
+    print its meta information. Flag --markdown prints details in Markdown for easy
     copy-pasting to GitHub issues.
+
+    DOCS: https://nightly.spacy.io/api/cli#info
     """
     info(model, markdown=markdown, silent=silent)
 
@@ -30,14 +32,16 @@ def info(
 ) -> Union[str, dict]:
     msg = Printer(no_print=silent, pretty=not silent)
     if model:
-        title = f"Info about model '{model}'"
+        title = f"Info about pipeline '{model}'"
         data = info_model(model, silent=silent)
     else:
         title = "Info about spaCy"
         data = info_spacy()
     raw_data = {k.lower().replace(" ", "_"): v for k, v in data.items()}
-    if "Models" in data and isinstance(data["Models"], dict):
-        data["Models"] = ", ".join(f"{n} ({v})" for n, v in data["Models"].items())
+    if "Pipelines" in data and isinstance(data["Pipelines"], dict):
+        data["Pipelines"] = ", ".join(
+            f"{n} ({v})" for n, v in data["Pipelines"].items()
+        )
     markdown_data = get_markdown(data, title=title)
     if markdown:
         if not silent:
@@ -63,7 +67,7 @@ def info_spacy() -> Dict[str, any]:
         "Location": str(Path(__file__).parent.parent),
         "Platform": platform.platform(),
         "Python version": platform.python_version(),
-        "Models": all_models,
+        "Pipelines": all_models,
     }
 
 
@@ -81,7 +85,7 @@ def info_model(model: str, *, silent: bool = True) -> Dict[str, Any]:
         model_path = model
     meta_path = model_path / "meta.json"
     if not meta_path.is_file():
-        msg.fail("Can't find model meta.json", meta_path, exits=1)
+        msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
     meta = srsly.read_json(meta_path)
     if model_path.resolve() != model_path:
         meta["source"] = str(model_path.resolve())
diff --git a/spacy/cli/init_config.py b/spacy/cli/init_config.py
index b5335df51..584ca7f64 100644
--- a/spacy/cli/init_config.py
+++ b/spacy/cli/init_config.py
@@ -27,7 +27,7 @@ def init_config_cli(
     # fmt: off
     output_file: Path = Arg(..., help="File to save config.cfg to or - for stdout (will only output config and no additional logging info)", allow_dash=True),
     lang: Optional[str] = Opt("en", "--lang", "-l", help="Two-letter code of the language to use"),
-    pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include in the model (without 'tok2vec' or 'transformer')"),
+    pipeline: Optional[str] = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of trainable pipeline components to include (without 'tok2vec' or 'transformer')"),
     optimize: Optimizations = Opt(Optimizations.efficiency.value, "--optimize", "-o", help="Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters."),
     cpu: bool = Opt(False, "--cpu", "-C", help="Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters."),
     # fmt: on
@@ -37,6 +37,8 @@ def init_config_cli(
     specified via the CLI arguments, this command generates a config with the
     optimal settings for you use case. This includes the choice of architecture,
     pretrained weights and related hyperparameters.
+
+    DOCS: https://nightly.spacy.io/api/cli#init-config
     """
     if isinstance(optimize, Optimizations):  # instance of enum from the CLI
         optimize = optimize.value
@@ -59,15 +61,23 @@ def init_fill_config_cli(
     functions for their default values and update the base config. This command
     can be used with a config generated via the training quickstart widget:
     https://nightly.spacy.io/usage/training#quickstart
+
+    DOCS: https://nightly.spacy.io/api/cli#init-fill-config
     """
     fill_config(output_file, base_path, pretraining=pretraining, diff=diff)
 
 
 def fill_config(
-    output_file: Path, base_path: Path, *, pretraining: bool = False, diff: bool = False
+    output_file: Path,
+    base_path: Path,
+    *,
+    pretraining: bool = False,
+    diff: bool = False,
+    silent: bool = False,
 ) -> Tuple[Config, Config]:
     is_stdout = str(output_file) == "-"
-    msg = Printer(no_print=is_stdout)
+    no_print = is_stdout or silent
+    msg = Printer(no_print=no_print)
     with show_validation_error(hint_fill=False):
         config = util.load_config(base_path)
         nlp, _ = util.load_model_from_config(config, auto_fill=True, validate=False)
@@ -85,7 +95,7 @@ def fill_config(
         msg.warn("Nothing to auto-fill: base config is already complete")
     else:
         msg.good("Auto-filled config with all values")
-    if diff and not is_stdout:
+    if diff and not no_print:
         if before == after:
             msg.warn("No diff to show: nothing was auto-filled")
         else:
@@ -94,7 +104,8 @@ def fill_config(
             print(diff_strings(before, after))
             msg.divider("END CONFIG DIFF")
             print("")
-    save_config(filled, output_file, is_stdout=is_stdout)
+    save_config(filled, output_file, is_stdout=is_stdout, silent=silent)
+    return config, filled
 
 
 def init_config(
@@ -149,8 +160,11 @@ def init_config(
     save_config(nlp.config, output_file, is_stdout=is_stdout)
 
 
-def save_config(config: Config, output_file: Path, is_stdout: bool = False) -> None:
-    msg = Printer(no_print=is_stdout)
+def save_config(
+    config: Config, output_file: Path, is_stdout: bool = False, silent: bool = False
+) -> None:
+    no_print = is_stdout or silent
+    msg = Printer(no_print=no_print)
     if is_stdout:
         print(config.to_str())
     else:
@@ -158,9 +172,10 @@ def save_config(config: Config, output_file: Path, is_stdout: bool = False) -> N
             output_file.parent.mkdir(parents=True)
         config.to_disk(output_file, interpolate=False)
         msg.good("Saved config", output_file)
-        msg.text("You can now add your data and train your model:")
+        msg.text("You can now add your data and train your pipeline:")
         variables = ["--paths.train ./train.spacy", "--paths.dev ./dev.spacy"]
-        print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}")
+        if not no_print:
+            print(f"{COMMAND} train {output_file.parts[-1]} {' '.join(variables)}")
 
 
 def has_spacy_transformers() -> bool:
diff --git a/spacy/cli/init_model.py b/spacy/cli/init_model.py
index 4fdd2bbbc..5f06fd895 100644
--- a/spacy/cli/init_model.py
+++ b/spacy/cli/init_model.py
@@ -28,7 +28,7 @@ except ImportError:
 DEFAULT_OOV_PROB = -20
 
 
-@init_cli.command("model")
+@init_cli.command("vocab")
 @app.command(
     "init-model",
     context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
@@ -37,8 +37,8 @@ DEFAULT_OOV_PROB = -20
 def init_model_cli(
     # fmt: off
     ctx: typer.Context,  # This is only used to read additional arguments
-    lang: str = Arg(..., help="Model language"),
-    output_dir: Path = Arg(..., help="Model output directory"),
+    lang: str = Arg(..., help="Pipeline language"),
+    output_dir: Path = Arg(..., help="Pipeline output directory"),
     freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
     clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
     jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
@@ -46,19 +46,22 @@ def init_model_cli(
     prune_vectors: int = Opt(-1, "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
     truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
     vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
-    model_name: Optional[str] = Opt(None, "--model-name", "-mn", help="Optional name for the model meta"),
-    base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Base model (for languages with custom tokenizers)")
+    model_name: Optional[str] = Opt(None, "--meta-name", "-mn", help="Optional name of the package for the pipeline meta"),
+    base_model: Optional[str] = Opt(None, "--base", "-b", help="Name of or path to base pipeline to start with (mostly relevant for pipelines with custom tokenizers)")
     # fmt: on
 ):
     """
-    Create a new model from raw data. If vectors are provided in Word2Vec format,
-    they can be either a .txt or zipped as a .zip or .tar.gz.
+    Create a new blank pipeline directory with vocab and vectors from raw data.
+    If vectors are provided in Word2Vec format, they can be either a .txt or
+    zipped as a .zip or .tar.gz.
+
+    DOCS: https://nightly.spacy.io/api/cli#init-vocab
     """
     if ctx.command.name == "init-model":
         msg.warn(
-            "The init-model command is now available via the 'init model' "
-            "subcommand (without the hyphen). You can run python -m spacy init "
-            "--help for an overview of the other available initialization commands."
+            "The init-model command is now called 'init vocab'. You can run "
+            "'python -m spacy init --help' for an overview of the other "
+            "available initialization commands."
         )
     init_model(
         lang,
@@ -115,10 +118,10 @@ def init_model(
             msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
         lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)
 
-    with msg.loading("Creating model..."):
+    with msg.loading("Creating blank pipeline..."):
         nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
 
-    msg.good("Successfully created model")
+    msg.good("Successfully created blank pipeline")
     if vectors_loc is not None:
         add_vectors(
             msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
@@ -242,7 +245,8 @@ def add_vectors(
         if vectors_data is not None:
             nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
     if name is None:
-        nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors"
+        # TODO: Is this correct? Does this matter?
+        nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
     else:
         nlp.vocab.vectors.name = name
     nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
diff --git a/spacy/cli/package.py b/spacy/cli/package.py
index 4e5038951..c457b3e17 100644
--- a/spacy/cli/package.py
+++ b/spacy/cli/package.py
@@ -14,23 +14,25 @@ from .. import about
 @app.command("package")
 def package_cli(
     # fmt: off
-    input_dir: Path = Arg(..., help="Directory with model data", exists=True, file_okay=False),
+    input_dir: Path = Arg(..., help="Directory with pipeline data", exists=True, file_okay=False),
     output_dir: Path = Arg(..., help="Output parent directory", exists=True, file_okay=False),
     meta_path: Optional[Path] = Opt(None, "--meta-path", "--meta", "-m", help="Path to meta.json", exists=True, dir_okay=False),
     create_meta: bool = Opt(False, "--create-meta", "-c", "-C", help="Create meta.json, even if one exists"),
     version: Optional[str] = Opt(None, "--version", "-v", help="Package version to override meta"),
     no_sdist: bool = Opt(False, "--no-sdist", "-NS", help="Don't build .tar.gz sdist, can be set if you want to run this step manually"),
-    force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing model in output directory"),
+    force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing data in output directory"),
     # fmt: on
 ):
     """
-    Generate an installable Python package for a model. Includes model data,
+    Generate an installable Python package for a pipeline. Includes binary data,
     meta and required installation files. A new directory will be created in the
-    specified output directory, and model data will be copied over. If
+    specified output directory, and the data will be copied over. If
     --create-meta is set and a meta.json already exists in the output directory,
     the existing values will be used as the defaults in the command-line prompt.
     After packaging, "python setup.py sdist" is run in the package directory,
     which will create a .tar.gz archive that can be installed via "pip install".
+
+    DOCS: https://nightly.spacy.io/api/cli#package
     """
     package(
         input_dir,
@@ -59,14 +61,14 @@ def package(
     output_path = util.ensure_path(output_dir)
     meta_path = util.ensure_path(meta_path)
     if not input_path or not input_path.exists():
-        msg.fail("Can't locate model data", input_path, exits=1)
+        msg.fail("Can't locate pipeline data", input_path, exits=1)
     if not output_path or not output_path.exists():
         msg.fail("Output directory not found", output_path, exits=1)
     if meta_path and not meta_path.exists():
-        msg.fail("Can't find model meta.json", meta_path, exits=1)
+        msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
     meta_path = meta_path or input_dir / "meta.json"
     if not meta_path.exists() or not meta_path.is_file():
-        msg.fail("Can't load model meta.json", meta_path, exits=1)
+        msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
     meta = srsly.read_json(meta_path)
     meta = get_meta(input_dir, meta)
     if version is not None:
@@ -77,7 +79,7 @@ def package(
         meta = generate_meta(meta, msg)
     errors = validate(ModelMetaSchema, meta)
     if errors:
-        msg.fail("Invalid model meta.json")
+        msg.fail("Invalid pipeline meta.json")
         print("\n".join(errors))
         sys.exit(1)
     model_name = meta["lang"] + "_" + meta["name"]
@@ -118,7 +120,7 @@ def get_meta(
 ) -> Dict[str, Any]:
     meta = {
         "lang": "en",
-        "name": "model",
+        "name": "pipeline",
         "version": "0.0.0",
         "description": "",
         "author": "",
@@ -143,10 +145,10 @@ def get_meta(
 def generate_meta(existing_meta: Dict[str, Any], msg: Printer) -> Dict[str, Any]:
     meta = existing_meta or {}
     settings = [
-        ("lang", "Model language", meta.get("lang", "en")),
-        ("name", "Model name", meta.get("name", "model")),
-        ("version", "Model version", meta.get("version", "0.0.0")),
-        ("description", "Model description", meta.get("description", None)),
+        ("lang", "Pipeline language", meta.get("lang", "en")),
+        ("name", "Pipeline name", meta.get("name", "pipeline")),
+        ("version", "Package version", meta.get("version", "0.0.0")),
+        ("description", "Package description", meta.get("description", None)),
         ("author", "Author", meta.get("author", None)),
         ("email", "Author email", meta.get("email", None)),
         ("url", "Author website", meta.get("url", None)),
@@ -154,8 +156,8 @@ def generate_meta(existing_meta: Dict[str, Any], msg: Printer) -> Dict[str, Any]
     ]
     msg.divider("Generating meta.json")
     msg.text(
-        "Enter the package settings for your model. The following information "
-        "will be read from your model data: pipeline, vectors."
+        "Enter the package settings for your pipeline. The following information "
+        "will be read from your pipeline data: pipeline, vectors."
     )
     for setting, desc, default in settings:
         response = get_raw_input(desc, default)
diff --git a/spacy/cli/pretrain.py b/spacy/cli/pretrain.py
index 5f20773e1..828e5f08e 100644
--- a/spacy/cli/pretrain.py
+++ b/spacy/cli/pretrain.py
@@ -31,7 +31,7 @@ def pretrain_cli(
     # fmt: off
     ctx: typer.Context,  # This is only used to read additional arguments
     texts_loc: Path = Arg(..., help="Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", exists=True),
-    output_dir: Path = Arg(..., help="Directory to write models to on each epoch"),
+    output_dir: Path = Arg(..., help="Directory to write weights to on each epoch"),
     config_path: Path = Arg(..., help="Path to config file", exists=True, dir_okay=False),
     code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
@@ -57,6 +57,8 @@ def pretrain_cli(
     To load the weights back in during 'spacy train', you need to ensure
     all settings are the same between pretraining and training. Ideally,
     this is done by using the same config file for both commands.
+
+    DOCS: https://nightly.spacy.io/api/cli#pretrain
     """
     overrides = parse_config_overrides(ctx.args)
     import_code(code_path)
@@ -376,10 +378,9 @@ def verify_cli_args(texts_loc, output_dir, config_path, resume_path, epoch_resum
     if output_dir.exists() and [p for p in output_dir.iterdir()]:
         if resume_path:
             msg.warn(
-                "Output directory is not empty. ",
-                "If you're resuming a run from a previous model in this directory, "
-                "the old models for the consecutive epochs will be overwritten "
-                "with the new ones.",
+                "Output directory is not empty.",
+                "If you're resuming a run in this directory, the old weights "
+                "for the consecutive epochs will be overwritten with the new ones.",
             )
         else:
             msg.warn(
diff --git a/spacy/cli/profile.py b/spacy/cli/profile.py
index 14d8435fe..43226730d 100644
--- a/spacy/cli/profile.py
+++ b/spacy/cli/profile.py
@@ -19,7 +19,7 @@ from ..util import load_model
 def profile_cli(
     # fmt: off
     ctx: typer.Context,  # This is only used to read current calling context
-    model: str = Arg(..., help="Model to load"),
+    model: str = Arg(..., help="Trained pipeline to load"),
     inputs: Optional[Path] = Arg(None, help="Location of input file. '-' for stdin.", exists=True, allow_dash=True),
     n_texts: int = Opt(10000, "--n-texts", "-n", help="Maximum number of texts to use if available"),
     # fmt: on
@@ -29,6 +29,8 @@ def profile_cli(
     Input should be formatted as one JSON object per line with a key "text".
     It can either be provided as a JSONL file, or be read from sys.sytdin.
     If no input file is specified, the IMDB dataset is loaded via Thinc.
+
+    DOCS: https://nightly.spacy.io/api/cli#debug-profile
     """
     if ctx.parent.command.name == NAME:  # called as top-level command
         msg.warn(
@@ -60,9 +62,9 @@ def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) ->
             inputs, _ = zip(*imdb_train)
         msg.info(f"Loaded IMDB dataset and using {n_inputs} examples")
         inputs = inputs[:n_inputs]
-    with msg.loading(f"Loading model '{model}'..."):
+    with msg.loading(f"Loading pipeline '{model}'..."):
         nlp = load_model(model)
-    msg.good(f"Loaded model '{model}'")
+    msg.good(f"Loaded pipeline '{model}'")
     texts = list(itertools.islice(inputs, n_texts))
     cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
     s = pstats.Stats("Profile.prof")
diff --git a/spacy/cli/project/assets.py b/spacy/cli/project/assets.py
index e33a82acc..2b623675d 100644
--- a/spacy/cli/project/assets.py
+++ b/spacy/cli/project/assets.py
@@ -20,6 +20,8 @@ def project_assets_cli(
     defined in the "assets" section of the project.yml. If a checksum is
     provided in the project.yml, the file is only downloaded if no local file
     with the same checksum exists.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-assets
     """
     project_assets(project_dir)
 
diff --git a/spacy/cli/project/clone.py b/spacy/cli/project/clone.py
index 7f9a46a46..a419feb0f 100644
--- a/spacy/cli/project/clone.py
+++ b/spacy/cli/project/clone.py
@@ -22,6 +22,8 @@ def project_clone_cli(
     only download the files from the given subdirectory. The GitHub repo
     defaults to the official spaCy template repo, but can be customized
     (including using a private repo).
+
+    DOCS: https://nightly.spacy.io/api/cli#project-clone
     """
     if dest is None:
         dest = Path.cwd() / name
@@ -43,7 +45,7 @@ def project_clone(name: str, dest: Path, *, repo: str = about.__projects__) -> N
         git_sparse_checkout(repo, name, dest)
     except subprocess.CalledProcessError:
         err = f"Could not clone '{name}' from repo '{repo_name}'"
-        msg.fail(err)
+        msg.fail(err, exits=1)
     msg.good(f"Cloned '{name}' from {repo_name}", project_dir)
     if not (project_dir / PROJECT_FILE).exists():
         msg.warn(f"No {PROJECT_FILE} found in directory")
@@ -78,6 +80,7 @@ def check_clone(name: str, dest: Path, repo: str) -> None:
     if not dest.parent.exists():
         # We're not creating parents, parent dir should exist
         msg.fail(
-            f"Can't clone project, parent directory doesn't exist: {dest.parent}",
+            f"Can't clone project, parent directory doesn't exist: {dest.parent}. "
+            f"Create the necessary folder(s) first before continuing.",
             exits=1,
         )
diff --git a/spacy/cli/project/document.py b/spacy/cli/project/document.py
index ffa77b2d5..d0265029a 100644
--- a/spacy/cli/project/document.py
+++ b/spacy/cli/project/document.py
@@ -6,6 +6,9 @@ from .._util import project_cli, Arg, Opt, PROJECT_FILE, load_project_config
 
 
 DOCS_URL = "https://nightly.spacy.io"
+INTRO = f"""> ⚠️ This project template uses the new [**spaCy v3.0**]({DOCS_URL}), which
+> is currently available as a nightly pre-release. You can install it from pip as `spacy-nightly`:
+> `pip install spacy-nightly`. Make sure to use a fresh virtual environment."""
 INTRO_PROJECT = f"""The [`{PROJECT_FILE}`]({PROJECT_FILE}) defines the data assets required by the
 project, as well as the available commands and workflows. For details, see the
 [spaCy projects documentation]({DOCS_URL}/usage/projects)."""
@@ -21,8 +24,10 @@ be fetched by running [`spacy project assets`]({DOCS_URL}/api/cli#project-assets
 in the project directory."""
 # These markers are added to the Markdown and can be used to update the file in
 # place if it already exists. Only the auto-generated part will be replaced.
-MARKER_START = "<!-- AUTO-GENERATED DOCS START (do not remove) -->"
-MARKER_END = "<!-- AUTO-GENERATED DOCS END (do not remove) -->"
+MARKER_START = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS START (do not remove) -->"
+MARKER_END = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS END (do not remove) -->"
+# If this marker is used in an existing README, it's ignored and not replaced
+MARKER_IGNORE = "<!-- SPACY PROJECT: IGNORE -->"
 
 
 @project_cli.command("document")
@@ -38,6 +43,8 @@ def project_document_cli(
     hidden markers are added so you can add custom content before or after the
     auto-generated section and only the auto-generated docs will be replaced
     when you re-run the command.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-document
     """
     project_document(project_dir, output_file, no_emoji=no_emoji)
 
@@ -52,6 +59,7 @@ def project_document(
     title = config.get("title")
     description = config.get("description")
     md.add(md.title(1, f"spaCy Project{f': {title}' if title else ''}", "🪐"))
+    md.add(INTRO)
     if description:
         md.add(description)
     md.add(md.title(2, PROJECT_FILE, "📋"))
@@ -96,13 +104,16 @@ def project_document(
         if output_file.exists():
             with output_file.open("r", encoding="utf8") as f:
                 existing = f.read()
+            if MARKER_IGNORE in existing:
+                msg.warn("Found ignore marker in existing file: skipping", output_file)
+                return
             if MARKER_START in existing and MARKER_END in existing:
                 msg.info("Found existing file: only replacing auto-generated docs")
                 before = existing.split(MARKER_START)[0]
                 after = existing.split(MARKER_END)[1]
                 content = f"{before}{content}{after}"
             else:
-                msg.info("Replacing existing file")
+                msg.warn("Replacing existing file")
         with output_file.open("w") as f:
             f.write(content)
         msg.good("Saved project documentation", output_file)
diff --git a/spacy/cli/project/dvc.py b/spacy/cli/project/dvc.py
index de0480bad..541253234 100644
--- a/spacy/cli/project/dvc.py
+++ b/spacy/cli/project/dvc.py
@@ -31,7 +31,10 @@ def project_update_dvc_cli(
     """Auto-generate Data Version Control (DVC) config. A DVC
     project can only define one pipeline, so you need to specify one workflow
     defined in the project.yml. If no workflow is specified, the first defined
-    workflow is used. The DVC config will only be updated if the project.yml changed.
+    workflow is used. The DVC config will only be updated if the project.yml
+    changed.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-dvc
     """
     project_update_dvc(project_dir, workflow, verbose=verbose, force=force)
 
diff --git a/spacy/cli/project/pull.py b/spacy/cli/project/pull.py
index 6c0f32171..655e2f459 100644
--- a/spacy/cli/project/pull.py
+++ b/spacy/cli/project/pull.py
@@ -17,7 +17,9 @@ def project_pull_cli(
     """Retrieve available precomputed outputs from a remote storage.
     You can alias remotes in your project.yml by mapping them to storage paths.
     A storage can be anything that the smart-open library can upload to, e.g.
-    gcs, aws, ssh, local directories etc
+    AWS, Google Cloud Storage, SSH, local directories etc.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-pull
     """
     for url, output_path in project_pull(project_dir, remote):
         if url is not None:
@@ -38,5 +40,5 @@ def project_pull(project_dir: Path, remote: str, *, verbose: bool = False):
             url = storage.pull(output_path, command_hash=cmd_hash)
             yield url, output_path
 
-        if cmd.get("outptus") and all(loc.exists() for loc in cmd["outputs"]):
+        if cmd.get("outputs") and all(loc.exists() for loc in cmd["outputs"]):
             update_lockfile(project_dir, cmd)
diff --git a/spacy/cli/project/push.py b/spacy/cli/project/push.py
index e09ee6e1a..fcee2231a 100644
--- a/spacy/cli/project/push.py
+++ b/spacy/cli/project/push.py
@@ -13,9 +13,12 @@ def project_push_cli(
     project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
     # fmt: on
 ):
-    """Persist outputs to a remote storage. You can alias remotes in your project.yml
-    by mapping them to storage paths. A storage can be anything that the smart-open
-    library can upload to, e.g. gcs, aws, ssh, local directories etc
+    """Persist outputs to a remote storage. You can alias remotes in your
+    project.yml by mapping them to storage paths. A storage can be anything that
+    the smart-open library can upload to, e.g. AWS, Google Cloud Storage, SSH,
+    local directories etc.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-push
     """
     for output_path, url in project_push(project_dir, remote):
         if url is None:
diff --git a/spacy/cli/project/run.py b/spacy/cli/project/run.py
index bacd7f04b..eb7b8cc5b 100644
--- a/spacy/cli/project/run.py
+++ b/spacy/cli/project/run.py
@@ -24,6 +24,8 @@ def project_run_cli(
     name is specified, all commands in the workflow are run, in order. If
     commands define dependencies and/or outputs, they will only be re-run if
     state has changed.
+
+    DOCS: https://nightly.spacy.io/api/cli#project-run
     """
     if show_help or not subcommand:
         print_run_help(project_dir, subcommand)
diff --git a/spacy/cli/templates/quickstart_training.jinja b/spacy/cli/templates/quickstart_training.jinja
index 0071f1b1a..43c852d13 100644
--- a/spacy/cli/templates/quickstart_training.jinja
+++ b/spacy/cli/templates/quickstart_training.jinja
@@ -29,7 +29,7 @@ name = "{{ transformer["name"] }}"
 tokenizer_config = {"use_fast": true}
 
 [components.transformer.model.get_spans]
-@span_getters = "strided_spans.v1"
+@span_getters = "spacy-transformers.strided_spans.v1"
 window = 128
 stride = 96
 
@@ -42,7 +42,7 @@ factory = "tagger"
 nO = null
 
 [components.tagger.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.tagger.model.tok2vec.pooling]
@@ -62,7 +62,7 @@ use_upper = false
 nO = null
 
 [components.parser.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.parser.model.tok2vec.pooling]
@@ -82,7 +82,7 @@ use_upper = false
 nO = null
 
 [components.ner.model.tok2vec]
-@architectures = "spacy-transformers.Tok2VecListener.v1"
+@architectures = "spacy-transformers.TransformerListener.v1"
 grad_factor = 1.0
 
 [components.ner.model.tok2vec.pooling]
@@ -204,13 +204,13 @@ max_length = 0
 
 {% if use_transformer %}
 [training.batcher]
-@batchers = "batch_by_padded.v1"
+@batchers = "spacy.batch_by_padded.v1"
 discard_oversize = true
 size = 2000
 buffer = 256
 {%- else %}
 [training.batcher]
-@batchers = "batch_by_words.v1"
+@batchers = "spacy.batch_by_words.v1"
 discard_oversize = false
 tolerance = 0.2
 
diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index d9ab8eca5..6be47fa39 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -26,7 +26,7 @@ def train_cli(
     # fmt: off
     ctx: typer.Context,  # This is only used to read additional arguments
     config_path: Path = Arg(..., help="Path to config file", exists=True),
-    output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store model in"),
+    output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"),
     code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
     verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
     use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
@@ -34,7 +34,7 @@ def train_cli(
     # fmt: on
 ):
     """
-    Train or update a spaCy model. Requires data in spaCy's binary format. To
+    Train or update a spaCy pipeline. Requires data in spaCy's binary format. To
     convert data from other formats, use the `spacy convert` command. The
     config file includes all settings and hyperparameters used during traing.
     To override settings in the config, e.g. settings that point to local
@@ -44,6 +44,8 @@ def train_cli(
     lets you pass in a Python file that's imported before training. It can be
     used to register custom functions and architectures that can then be
     referenced in the config.
+
+    DOCS: https://nightly.spacy.io/api/cli#train
     """
     util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
     verify_cli_args(config_path, output_path)
@@ -77,6 +79,9 @@ def train(
         )
     if config.get("training", {}).get("seed") is not None:
         fix_random_seed(config["training"]["seed"])
+    if config.get("system", {}).get("use_pytorch_for_gpu_memory"):
+        # It feels kind of weird to not have a default for this.
+        use_pytorch_for_gpu_memory()
     # Use original config here before it's resolved to functions
     sourced_components = get_sourced_components(config)
     with show_validation_error(config_path):
@@ -85,9 +90,6 @@ def train(
         util.load_vectors_into_model(nlp, config["training"]["vectors"])
     verify_config(nlp)
     raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
-    if config.get("system", {}).get("use_pytorch_for_gpu_memory"):
-        # It feels kind of weird to not have a default for this.
-        use_pytorch_for_gpu_memory()
     T_cfg = config["training"]
     optimizer = T_cfg["optimizer"]
     train_corpus = T_cfg["train_corpus"]
@@ -113,12 +115,12 @@ def train(
         # Load morph rules
         nlp.vocab.morphology.load_morph_exceptions(morph_rules)
 
-    # Load a pretrained tok2vec model - cf. CLI command 'pretrain'
+    # Load pretrained tok2vec weights - cf. CLI command 'pretrain'
     if weights_data is not None:
         tok2vec_path = config["pretraining"].get("tok2vec_model", None)
         if tok2vec_path is None:
             msg.fail(
-                f"To use a pretrained tok2vec model, the config needs to specify which "
+                f"To pretrained tok2vec weights, the config needs to specify which "
                 f"tok2vec layer to load in the setting [pretraining.tok2vec_model].",
                 exits=1,
             )
@@ -159,7 +161,8 @@ def train(
                 print_row(info)
                 if is_best_checkpoint and output_path is not None:
                     update_meta(T_cfg, nlp, info)
-                    nlp.to_disk(output_path / "model-best")
+                    with nlp.use_params(optimizer.averages):
+                        nlp.to_disk(output_path / "model-best")
                 progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
                 progress.set_description(f"Epoch {info['epoch']}")
     except Exception as e:
@@ -182,22 +185,16 @@ def train(
                     nlp.to_disk(final_model_path)
             else:
                 nlp.to_disk(final_model_path)
-            msg.good(f"Saved model to output directory {final_model_path}")
+            msg.good(f"Saved pipeline to output directory {final_model_path}")
 
 
 def create_train_batches(iterator, batcher, max_epochs: int):
-    epoch = 1
-    examples = []
-    # Stream the first epoch, so we start training faster and support
-    # infinite streams.
-    for batch in batcher(iterator):
-        yield epoch, batch
-        if max_epochs != 1:
-            examples.extend(batch)
+    epoch = 0
+    examples = list(iterator)
     if not examples:
         # Raise error if no data
         raise ValueError(Errors.E986)
-    while epoch != max_epochs:
+    while max_epochs < 1 or epoch != max_epochs:
         random.shuffle(examples)
         for batch in batcher(examples):
             yield epoch, batch
@@ -270,9 +267,9 @@ def train_while_improving(
 
         epoch (int): How many passes over the data have been completed.
         step (int): How many steps have been completed.
-        score (float): The main score form the last evaluation.
+        score (float): The main score from the last evaluation.
         other_scores: : The other scores from the last evaluation.
-        loss: The accumulated losses throughout training.
+        losses: The accumulated losses throughout training.
         checkpoints: A list of previous results, where each result is a
             (score, step, epoch) tuple.
     """
diff --git a/spacy/cli/validate.py b/spacy/cli/validate.py
index e6ba284df..9a75ed6f3 100644
--- a/spacy/cli/validate.py
+++ b/spacy/cli/validate.py
@@ -13,9 +13,11 @@ from ..util import get_package_path, get_model_meta, is_compatible_version
 @app.command("validate")
 def validate_cli():
     """
-    Validate the currently installed models and spaCy version. Checks if the
-    installed models are compatible and shows upgrade instructions if available.
-    Should be run after `pip install -U spacy`.
+    Validate the currently installed pipeline packages and spaCy version. Checks
+    if the installed packages are compatible and shows upgrade instructions if
+    available. Should be run after `pip install -U spacy`.
+
+    DOCS: https://nightly.spacy.io/api/cli#validate
     """
     validate()
 
@@ -25,13 +27,13 @@ def validate() -> None:
     spacy_version = get_base_version(about.__version__)
     current_compat = compat.get(spacy_version, {})
     if not current_compat:
-        msg.warn(f"No compatible models found for v{spacy_version} of spaCy")
+        msg.warn(f"No compatible packages found for v{spacy_version} of spaCy")
     incompat_models = {d["name"] for _, d in model_pkgs.items() if not d["compat"]}
     na_models = [m for m in incompat_models if m not in current_compat]
     update_models = [m for m in incompat_models if m in current_compat]
     spacy_dir = Path(__file__).parent.parent
 
-    msg.divider(f"Installed models (spaCy v{about.__version__})")
+    msg.divider(f"Installed pipeline packages (spaCy v{about.__version__})")
     msg.info(f"spaCy installation: {spacy_dir}")
 
     if model_pkgs:
@@ -47,15 +49,15 @@ def validate() -> None:
             rows.append((data["name"], data["spacy"], version, comp))
         msg.table(rows, header=header)
     else:
-        msg.text("No models found in your current environment.", exits=0)
+        msg.text("No pipeline packages found in your current environment.", exits=0)
     if update_models:
         msg.divider("Install updates")
-        msg.text("Use the following commands to update the model packages:")
+        msg.text("Use the following commands to update the packages:")
         cmd = "python -m spacy download {}"
         print("\n".join([cmd.format(pkg) for pkg in update_models]) + "\n")
     if na_models:
         msg.info(
-            f"The following models are custom spaCy models or not "
+            f"The following packages are custom spaCy pipelines or not "
             f"available for spaCy v{about.__version__}:",
             ", ".join(na_models),
         )
diff --git a/spacy/default_config.cfg b/spacy/default_config.cfg
index d76ef630d..9507f0f0a 100644
--- a/spacy/default_config.cfg
+++ b/spacy/default_config.cfg
@@ -69,7 +69,7 @@ max_length = 2000
 limit = 0
 
 [training.batcher]
-@batchers = "batch_by_words.v1"
+@batchers = "spacy.batch_by_words.v1"
 discard_oversize = false
 tolerance = 0.2
 
diff --git a/spacy/displacy/__init__.py b/spacy/displacy/__init__.py
index 2df2bd61c..0e80c3b5f 100644
--- a/spacy/displacy/__init__.py
+++ b/spacy/displacy/__init__.py
@@ -1,8 +1,8 @@
 """
 spaCy's built in visualization suite for dependencies and named entities.
 
-DOCS: https://spacy.io/api/top-level#displacy
-USAGE: https://spacy.io/usage/visualizers
+DOCS: https://nightly.spacy.io/api/top-level#displacy
+USAGE: https://nightly.spacy.io/usage/visualizers
 """
 from typing import Union, Iterable, Optional, Dict, Any, Callable
 import warnings
@@ -37,8 +37,8 @@ def render(
     manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
     RETURNS (str): Rendered HTML markup.
 
-    DOCS: https://spacy.io/api/top-level#displacy.render
-    USAGE: https://spacy.io/usage/visualizers
+    DOCS: https://nightly.spacy.io/api/top-level#displacy.render
+    USAGE: https://nightly.spacy.io/usage/visualizers
     """
     factories = {
         "dep": (DependencyRenderer, parse_deps),
@@ -88,8 +88,8 @@ def serve(
     port (int): Port to serve visualisation.
     host (str): Host to serve visualisation.
 
-    DOCS: https://spacy.io/api/top-level#displacy.serve
-    USAGE: https://spacy.io/usage/visualizers
+    DOCS: https://nightly.spacy.io/api/top-level#displacy.serve
+    USAGE: https://nightly.spacy.io/usage/visualizers
     """
     from wsgiref import simple_server
 
diff --git a/spacy/displacy/render.py b/spacy/displacy/render.py
index 07550f9aa..984971812 100644
--- a/spacy/displacy/render.py
+++ b/spacy/displacy/render.py
@@ -249,6 +249,12 @@ class EntityRenderer:
         colors = dict(DEFAULT_LABEL_COLORS)
         user_colors = registry.displacy_colors.get_all()
         for user_color in user_colors.values():
+            if callable(user_color):
+                # Since this comes from the function registry, we want to make
+                # sure we support functions that *return* a dict of colors
+                user_color = user_color()
+            if not isinstance(user_color, dict):
+                raise ValueError(Errors.E925.format(obj=type(user_color)))
             colors.update(user_color)
         colors.update(options.get("colors", {}))
         self.default_color = DEFAULT_ENTITY_COLOR
diff --git a/spacy/errors.py b/spacy/errors.py
index 38c89c479..f3058d2b4 100644
--- a/spacy/errors.py
+++ b/spacy/errors.py
@@ -22,7 +22,7 @@ class Warnings:
             "generate a dependency visualization for it. Make sure the Doc "
             "was processed with a model that supports dependency parsing, and "
             "not just a language class like `English()`. For more info, see "
-            "the docs:\nhttps://spacy.io/usage/models")
+            "the docs:\nhttps://nightly.spacy.io/usage/models")
     W006 = ("No entities to visualize found in Doc object. If this is "
             "surprising to you, make sure the Doc was processed using a model "
             "that supports named entity recognition, and check the `doc.ents` "
@@ -112,6 +112,9 @@ class Warnings:
             "word segmenters: {supported}. Defaulting to {default}.")
     W104 = ("Skipping modifications for '{target}' segmenter. The current "
             "segmenter is '{current}'.")
+    W105 = ("As of spaCy v3.0, the {matcher}.pipe method is deprecated. If you "
+            "need to match on a stream of documents, you can use nlp.pipe and "
+            "call the {matcher} on each Doc object.")
 
 
 @add_codes
@@ -144,7 +147,7 @@ class Errors:
     E010 = ("Word vectors set to length 0. This may be because you don't have "
             "a model installed or loaded, or because your model doesn't "
             "include word vectors. For more info, see the docs:\n"
-            "https://spacy.io/usage/models")
+            "https://nightly.spacy.io/usage/models")
     E011 = ("Unknown operator: '{op}'. Options: {opts}")
     E012 = ("Cannot add pattern for zero tokens to matcher.\nKey: {key}")
     E014 = ("Unknown tag ID: {tag}")
@@ -178,7 +181,7 @@ class Errors:
             "list of (unicode, bool) tuples. Got bytes instance: {value}")
     E029 = ("noun_chunks requires the dependency parse, which requires a "
             "statistical model to be installed and loaded. For more info, see "
-            "the documentation:\nhttps://spacy.io/usage/models")
+            "the documentation:\nhttps://nightly.spacy.io/usage/models")
     E030 = ("Sentence boundaries unset. You can add the 'sentencizer' "
             "component to the pipeline with: "
             "nlp.add_pipe('sentencizer'). "
@@ -291,7 +294,7 @@ class Errors:
     E102 = ("Can't merge non-disjoint spans. '{token}' is already part of "
             "tokens to merge. If you want to find the longest non-overlapping "
             "spans, you can use the util.filter_spans helper:\n"
-            "https://spacy.io/api/top-level#util.filter_spans")
+            "https://nightly.spacy.io/api/top-level#util.filter_spans")
     E103 = ("Trying to set conflicting doc.ents: '{span1}' and '{span2}'. A "
             "token can only be part of one entity, so make sure the entities "
             "you're setting don't overlap.")
@@ -361,10 +364,10 @@ class Errors:
     E137 = ("Expected 'dict' type, but got '{type}' from '{line}'. Make sure "
             "to provide a valid JSON object as input with either the `text` "
             "or `tokens` key. For more info, see the docs:\n"
-            "https://spacy.io/api/cli#pretrain-jsonl")
+            "https://nightly.spacy.io/api/cli#pretrain-jsonl")
     E138 = ("Invalid JSONL format for raw text '{text}'. Make sure the input "
             "includes either the `text` or `tokens` key. For more info, see "
-            "the docs:\nhttps://spacy.io/api/cli#pretrain-jsonl")
+            "the docs:\nhttps://nightly.spacy.io/api/cli#pretrain-jsonl")
     E139 = ("Knowledge Base for component '{name}' is empty. Use the methods "
             "kb.add_entity and kb.add_alias to add entries.")
     E140 = ("The list of entities, prior probabilities and entity vectors "
@@ -473,6 +476,8 @@ class Errors:
     E199 = ("Unable to merge 0-length span at doc[{start}:{end}].")
 
     # TODO: fix numbering after merging develop into master
+    E925 = ("Invalid color values for displaCy visualizer: expected dictionary "
+            "mapping label names to colors but got: {obj}")
     E926 = ("It looks like you're trying to modify nlp.{attr} directly. This "
             "doesn't work because it's an immutable computed property. If you "
             "need to modify the pipeline, use the built-in methods like "
@@ -645,6 +650,10 @@ class Errors:
              "Required tables '{tables}', found '{found}'. If you are not "
              "providing custom lookups, make sure you have the package "
              "spacy-lookups-data installed.")
+    E1005 = ("Unable to set attribute '{attr}' in tokenizer exception for "
+             "'{chunk}'. Tokenizer exceptions are only allowed to specify "
+             "`ORTH` and `NORM`.")
+    E1006 = ("Unable to initialize {name} model with 0 labels.")
 
 
 @add_codes
diff --git a/spacy/gold/batchers.py b/spacy/gold/batchers.py
index ec1f35815..c54242eae 100644
--- a/spacy/gold/batchers.py
+++ b/spacy/gold/batchers.py
@@ -11,7 +11,7 @@ ItemT = TypeVar("ItemT")
 BatcherT = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
 
 
-@registry.batchers("batch_by_padded.v1")
+@registry.batchers("spacy.batch_by_padded.v1")
 def configure_minibatch_by_padded_size(
     *,
     size: Sizing,
@@ -46,7 +46,7 @@ def configure_minibatch_by_padded_size(
     )
 
 
-@registry.batchers("batch_by_words.v1")
+@registry.batchers("spacy.batch_by_words.v1")
 def configure_minibatch_by_words(
     *,
     size: Sizing,
@@ -70,7 +70,7 @@ def configure_minibatch_by_words(
     )
 
 
-@registry.batchers("batch_by_sequence.v1")
+@registry.batchers("spacy.batch_by_sequence.v1")
 def configure_minibatch(
     size: Sizing, get_length: Optional[Callable[[ItemT], int]] = None
 ) -> BatcherT:
diff --git a/spacy/gold/converters/conll_ner2docs.py b/spacy/gold/converters/conll_ner2docs.py
index 0b348142a..c04a77f07 100644
--- a/spacy/gold/converters/conll_ner2docs.py
+++ b/spacy/gold/converters/conll_ner2docs.py
@@ -106,7 +106,7 @@ def conll_ner2docs(
                 raise ValueError(
                     "The token-per-line NER file is not formatted correctly. "
                     "Try checking whitespace and delimiters. See "
-                    "https://spacy.io/api/cli#convert"
+                    "https://nightly.spacy.io/api/cli#convert"
                 )
             length = len(cols[0])
             words.extend(cols[0])
diff --git a/spacy/gold/converters/iob2docs.py b/spacy/gold/converters/iob2docs.py
index c7e243397..eebf1266b 100644
--- a/spacy/gold/converters/iob2docs.py
+++ b/spacy/gold/converters/iob2docs.py
@@ -44,7 +44,7 @@ def read_iob(raw_sents, vocab, n_sents):
                 sent_tags = ["-"] * len(sent_words)
             else:
                 raise ValueError(
-                    "The sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convert"
+                    "The sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://nightly.spacy.io/api/cli#convert"
                 )
             words.extend(sent_words)
             tags.extend(sent_tags)
diff --git a/spacy/gold/corpus.py b/spacy/gold/corpus.py
index 1046da1e6..545f01eaa 100644
--- a/spacy/gold/corpus.py
+++ b/spacy/gold/corpus.py
@@ -38,7 +38,7 @@ class Corpus:
     limit (int): Limit corpus to a subset of examples, e.g. for debugging.
         Defaults to 0, which indicates no limit.
 
-    DOCS: https://spacy.io/api/corpus
+    DOCS: https://nightly.spacy.io/api/corpus
     """
 
     def __init__(
@@ -83,7 +83,7 @@ class Corpus:
         nlp (Language): The current nlp object.
         YIELDS (Example): The examples.
 
-        DOCS: https://spacy.io/api/corpus#call
+        DOCS: https://nightly.spacy.io/api/corpus#call
         """
         ref_docs = self.read_docbin(nlp.vocab, self.walk_corpus(self.path))
         if self.gold_preproc:
diff --git a/spacy/kb.pyx b/spacy/kb.pyx
index 3b8017a0c..b24ed3a20 100644
--- a/spacy/kb.pyx
+++ b/spacy/kb.pyx
@@ -21,7 +21,7 @@ cdef class Candidate:
     algorithm which will disambiguate the various candidates to the correct one.
     Each candidate (alias, entity) pair is assigned to a certain prior probability.
 
-    DOCS: https://spacy.io/api/kb/#candidate_init
+    DOCS: https://nightly.spacy.io/api/kb/#candidate_init
     """
 
     def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
@@ -79,7 +79,7 @@ cdef class KnowledgeBase:
     """A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
     to support entity linking of named entities to real-world concepts.
 
-    DOCS: https://spacy.io/api/kb
+    DOCS: https://nightly.spacy.io/api/kb
     """
 
     def __init__(self, Vocab vocab, entity_vector_length):
diff --git a/spacy/lang/hu/punctuation.py b/spacy/lang/hu/punctuation.py
index 597f01b65..f827cd677 100644
--- a/spacy/lang/hu/punctuation.py
+++ b/spacy/lang/hu/punctuation.py
@@ -7,6 +7,7 @@ _concat_icons = CONCAT_ICONS.replace("\u00B0", "")
 
 _currency = r"\$¢£€¥฿"
 _quotes = CONCAT_QUOTES.replace("'", "")
+_units = UNITS.replace("%", "")
 
 _prefixes = (
     LIST_PUNCT
@@ -26,7 +27,7 @@ _suffixes = (
         r"(?<=[0-9])\+",
         r"(?<=°[FfCcKk])\.",
         r"(?<=[0-9])(?:[{c}])".format(c=_currency),
-        r"(?<=[0-9])(?:{u})".format(u=UNITS),
+        r"(?<=[0-9])(?:{u})".format(u=_units),
         r"(?<=[{al}{e}{q}(?:{c})])\.".format(
             al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, c=_currency
         ),
diff --git a/spacy/language.py b/spacy/language.py
index e20bbdd80..17ca020ca 100644
--- a/spacy/language.py
+++ b/spacy/language.py
@@ -1,5 +1,5 @@
 from typing import Optional, Any, Dict, Callable, Iterable, Union, List, Pattern
-from typing import Tuple, Iterator
+from typing import Tuple, Iterator, Optional
 from dataclasses import dataclass
 import random
 import itertools
@@ -95,7 +95,7 @@ class Language:
         object and processing pipeline.
     lang (str): Two-letter language ID, i.e. ISO code.
 
-    DOCS: https://spacy.io/api/language
+    DOCS: https://nightly.spacy.io/api/language
     """
 
     Defaults = BaseDefaults
@@ -130,7 +130,7 @@ class Language:
         create_tokenizer (Callable): Function that takes the nlp object and
             returns a tokenizer.
 
-        DOCS: https://spacy.io/api/language#init
+        DOCS: https://nightly.spacy.io/api/language#init
         """
         # We're only calling this to import all factories provided via entry
         # points. The factory decorator applied to these functions takes care
@@ -185,14 +185,14 @@ class Language:
 
         RETURNS (Dict[str, Any]): The meta.
 
-        DOCS: https://spacy.io/api/language#meta
+        DOCS: https://nightly.spacy.io/api/language#meta
         """
         spacy_version = util.get_model_version_range(about.__version__)
         if self.vocab.lang:
             self._meta.setdefault("lang", self.vocab.lang)
         else:
             self._meta.setdefault("lang", self.lang)
-        self._meta.setdefault("name", "model")
+        self._meta.setdefault("name", "pipeline")
         self._meta.setdefault("version", "0.0.0")
         self._meta.setdefault("spacy_version", spacy_version)
         self._meta.setdefault("description", "")
@@ -225,7 +225,7 @@ class Language:
 
         RETURNS (thinc.api.Config): The config.
 
-        DOCS: https://spacy.io/api/language#config
+        DOCS: https://nightly.spacy.io/api/language#config
         """
         self._config.setdefault("nlp", {})
         self._config.setdefault("training", {})
@@ -433,7 +433,7 @@ class Language:
             will be combined and normalized for the whole pipeline.
         func (Optional[Callable]): Factory function if not used as a decorator.
 
-        DOCS: https://spacy.io/api/language#factory
+        DOCS: https://nightly.spacy.io/api/language#factory
         """
         if not isinstance(name, str):
             raise ValueError(Errors.E963.format(decorator="factory"))
@@ -513,7 +513,7 @@ class Language:
             Used for pipeline analysis.
         func (Optional[Callable]): Factory function if not used as a decorator.
 
-        DOCS: https://spacy.io/api/language#component
+        DOCS: https://nightly.spacy.io/api/language#component
         """
         if name is not None and not isinstance(name, str):
             raise ValueError(Errors.E963.format(decorator="component"))
@@ -579,7 +579,7 @@ class Language:
         name (str): Name of pipeline component to get.
         RETURNS (callable): The pipeline component.
 
-        DOCS: https://spacy.io/api/language#get_pipe
+        DOCS: https://nightly.spacy.io/api/language#get_pipe
         """
         for pipe_name, component in self._components:
             if pipe_name == name:
@@ -608,7 +608,7 @@ class Language:
             arguments and types expected by the factory.
         RETURNS (Callable[[Doc], Doc]): The pipeline component.
 
-        DOCS: https://spacy.io/api/language#create_pipe
+        DOCS: https://nightly.spacy.io/api/language#create_pipe
         """
         name = name if name is not None else factory_name
         if not isinstance(config, dict):
@@ -722,7 +722,7 @@ class Language:
             arguments and types expected by the factory.
         RETURNS (Callable[[Doc], Doc]): The pipeline component.
 
-        DOCS: https://spacy.io/api/language#add_pipe
+        DOCS: https://nightly.spacy.io/api/language#add_pipe
         """
         if not isinstance(factory_name, str):
             bad_val = repr(factory_name)
@@ -820,7 +820,7 @@ class Language:
         name (str): Name of the component.
         RETURNS (bool): Whether a component of the name exists in the pipeline.
 
-        DOCS: https://spacy.io/api/language#has_pipe
+        DOCS: https://nightly.spacy.io/api/language#has_pipe
         """
         return name in self.pipe_names
 
@@ -841,7 +841,7 @@ class Language:
         validate (bool): Whether to validate the component config against the
             arguments and types expected by the factory.
 
-        DOCS: https://spacy.io/api/language#replace_pipe
+        DOCS: https://nightly.spacy.io/api/language#replace_pipe
         """
         if name not in self.pipe_names:
             raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
@@ -870,7 +870,7 @@ class Language:
         old_name (str): Name of the component to rename.
         new_name (str): New name of the component.
 
-        DOCS: https://spacy.io/api/language#rename_pipe
+        DOCS: https://nightly.spacy.io/api/language#rename_pipe
         """
         if old_name not in self.component_names:
             raise ValueError(
@@ -891,7 +891,7 @@ class Language:
         name (str): Name of the component to remove.
         RETURNS (tuple): A `(name, component)` tuple of the removed component.
 
-        DOCS: https://spacy.io/api/language#remove_pipe
+        DOCS: https://nightly.spacy.io/api/language#remove_pipe
         """
         if name not in self.component_names:
             raise ValueError(Errors.E001.format(name=name, opts=self.component_names))
@@ -944,7 +944,7 @@ class Language:
             keyword arguments for specific components.
         RETURNS (Doc): A container for accessing the annotations.
 
-        DOCS: https://spacy.io/api/language#call
+        DOCS: https://nightly.spacy.io/api/language#call
         """
         if len(text) > self.max_length:
             raise ValueError(
@@ -993,7 +993,7 @@ class Language:
         disable (str or iterable): The name(s) of the pipes to disable
         enable (str or iterable): The name(s) of the pipes to enable - all others will be disabled
 
-        DOCS: https://spacy.io/api/language#select_pipes
+        DOCS: https://nightly.spacy.io/api/language#select_pipes
         """
         if enable is None and disable is None:
             raise ValueError(Errors.E991)
@@ -1044,7 +1044,7 @@ class Language:
         exclude (Iterable[str]): Names of components that shouldn't be updated.
         RETURNS (Dict[str, float]): The updated losses dictionary
 
-        DOCS: https://spacy.io/api/language#update
+        DOCS: https://nightly.spacy.io/api/language#update
         """
         if _ is not None:
             raise ValueError(Errors.E989)
@@ -1106,7 +1106,7 @@ class Language:
             >>>     raw_batch = [Example.from_dict(nlp.make_doc(text), {}) for text in next(raw_text_batches)]
             >>>     nlp.rehearse(raw_batch)
 
-        DOCS: https://spacy.io/api/language#rehearse
+        DOCS: https://nightly.spacy.io/api/language#rehearse
         """
         if len(examples) == 0:
             return
@@ -1153,7 +1153,7 @@ class Language:
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/language#begin_training
+        DOCS: https://nightly.spacy.io/api/language#begin_training
         """
         # TODO: throw warning when get_gold_tuples is provided instead of get_examples
         if get_examples is None:
@@ -1200,7 +1200,7 @@ class Language:
         sgd (Optional[Optimizer]): An optimizer.
         RETURNS (Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/language#resume_training
+        DOCS: https://nightly.spacy.io/api/language#resume_training
         """
         if device >= 0:  # TODO: do we need this here?
             require_gpu(device)
@@ -1236,7 +1236,7 @@ class Language:
             for the scorer.
         RETURNS (Scorer): The scorer containing the evaluation results.
 
-        DOCS: https://spacy.io/api/language#evaluate
+        DOCS: https://nightly.spacy.io/api/language#evaluate
         """
         validate_examples(examples, "Language.evaluate")
         if component_cfg is None:
@@ -1275,7 +1275,7 @@ class Language:
         return results
 
     @contextmanager
-    def use_params(self, params: dict):
+    def use_params(self, params: Optional[dict]):
         """Replace weights of models in the pipeline with those provided in the
         params dictionary. Can be used as a contextmanager, in which case,
         models go back to their original weights after the block.
@@ -1286,26 +1286,29 @@ class Language:
             >>> with nlp.use_params(optimizer.averages):
             >>>     nlp.to_disk("/tmp/checkpoint")
 
-        DOCS: https://spacy.io/api/language#use_params
+        DOCS: https://nightly.spacy.io/api/language#use_params
         """
-        contexts = [
-            pipe.use_params(params)
-            for name, pipe in self.pipeline
-            if hasattr(pipe, "use_params") and hasattr(pipe, "model")
-        ]
-        # TODO: Having trouble with contextlib
-        # Workaround: these aren't actually context managers atm.
-        for context in contexts:
-            try:
-                next(context)
-            except StopIteration:
-                pass
-        yield
-        for context in contexts:
-            try:
-                next(context)
-            except StopIteration:
-                pass
+        if not params:
+            yield
+        else:
+            contexts = [
+                pipe.use_params(params)
+                for name, pipe in self.pipeline
+                if hasattr(pipe, "use_params") and hasattr(pipe, "model")
+            ]
+            # TODO: Having trouble with contextlib
+            # Workaround: these aren't actually context managers atm.
+            for context in contexts:
+                try:
+                    next(context)
+                except StopIteration:
+                    pass
+            yield
+            for context in contexts:
+                try:
+                    next(context)
+                except StopIteration:
+                    pass
 
     def pipe(
         self,
@@ -1314,7 +1317,6 @@ class Language:
         as_tuples: bool = False,
         batch_size: int = 1000,
         disable: Iterable[str] = SimpleFrozenList(),
-        cleanup: bool = False,
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = None,
         n_process: int = 1,
     ):
@@ -1326,14 +1328,12 @@ class Language:
             (doc, context) tuples. Defaults to False.
         batch_size (int): The number of texts to buffer.
         disable (List[str]): Names of the pipeline components to disable.
-        cleanup (bool): If True, unneeded strings are freed to control memory
-            use. Experimental.
         component_cfg (Dict[str, Dict]): An optional dictionary with extra keyword
             arguments for specific components.
         n_process (int): Number of processors to process texts. If -1, set `multiprocessing.cpu_count()`.
         YIELDS (Doc): Documents in the order of the original text.
 
-        DOCS: https://spacy.io/api/language#pipe
+        DOCS: https://nightly.spacy.io/api/language#pipe
         """
         if n_process == -1:
             n_process = mp.cpu_count()
@@ -1378,35 +1378,9 @@ class Language:
             for pipe in pipes:
                 docs = pipe(docs)
 
-        # Track weakrefs of "recent" documents, so that we can see when they
-        # expire from memory. When they do, we know we don't need old strings.
-        # This way, we avoid maintaining an unbounded growth in string entries
-        # in the string store.
-        recent_refs = weakref.WeakSet()
-        old_refs = weakref.WeakSet()
-        # Keep track of the original string data, so that if we flush old strings,
-        # we can recover the original ones. However, we only want to do this if we're
-        # really adding strings, to save up-front costs.
-        original_strings_data = None
         nr_seen = 0
         for doc in docs:
             yield doc
-            if cleanup:
-                recent_refs.add(doc)
-                if nr_seen < 10000:
-                    old_refs.add(doc)
-                    nr_seen += 1
-                elif len(old_refs) == 0:
-                    old_refs, recent_refs = recent_refs, old_refs
-                    if original_strings_data is None:
-                        original_strings_data = list(self.vocab.strings)
-                    else:
-                        keys, strings = self.vocab.strings._cleanup_stale_strings(
-                            original_strings_data
-                        )
-                        self.vocab._reset_cache(keys, strings)
-                        self.tokenizer._reset_cache(keys)
-                    nr_seen = 0
 
     def _multiprocessing_pipe(
         self,
@@ -1495,7 +1469,7 @@ class Language:
             the types expected by the factory.
         RETURNS (Language): The initialized Language class.
 
-        DOCS: https://spacy.io/api/language#from_config
+        DOCS: https://nightly.spacy.io/api/language#from_config
         """
         if auto_fill:
             config = Config(
@@ -1608,7 +1582,7 @@ class Language:
             it doesn't exist.
         exclude (list): Names of components or serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/language#to_disk
+        DOCS: https://nightly.spacy.io/api/language#to_disk
         """
         path = util.ensure_path(path)
         serializers = {}
@@ -1637,7 +1611,7 @@ class Language:
         exclude (list): Names of components or serialization fields to exclude.
         RETURNS (Language): The modified `Language` object.
 
-        DOCS: https://spacy.io/api/language#from_disk
+        DOCS: https://nightly.spacy.io/api/language#from_disk
         """
 
         def deserialize_meta(path: Path) -> None:
@@ -1685,7 +1659,7 @@ class Language:
         exclude (list): Names of components or serialization fields to exclude.
         RETURNS (bytes): The serialized form of the `Language` object.
 
-        DOCS: https://spacy.io/api/language#to_bytes
+        DOCS: https://nightly.spacy.io/api/language#to_bytes
         """
         serializers = {}
         serializers["vocab"] = lambda: self.vocab.to_bytes()
@@ -1709,7 +1683,7 @@ class Language:
         exclude (list): Names of components or serialization fields to exclude.
         RETURNS (Language): The `Language` object.
 
-        DOCS: https://spacy.io/api/language#from_bytes
+        DOCS: https://nightly.spacy.io/api/language#from_bytes
         """
 
         def deserialize_meta(b):
diff --git a/spacy/lexeme.pyx b/spacy/lexeme.pyx
index 25461b4b7..17ce574ce 100644
--- a/spacy/lexeme.pyx
+++ b/spacy/lexeme.pyx
@@ -30,7 +30,7 @@ cdef class Lexeme:
     tag, dependency parse, or lemma (lemmatization depends on the
     part-of-speech tag).
 
-    DOCS: https://spacy.io/api/lexeme
+    DOCS: https://nightly.spacy.io/api/lexeme
     """
     def __init__(self, Vocab vocab, attr_t orth):
         """Create a Lexeme object.
diff --git a/spacy/lookups.py b/spacy/lookups.py
index d79a5b950..fb5e3d748 100644
--- a/spacy/lookups.py
+++ b/spacy/lookups.py
@@ -57,7 +57,7 @@ class Table(OrderedDict):
         data (dict): The dictionary.
         name (str): Optional table name for reference.
 
-        DOCS: https://spacy.io/api/lookups#table.from_dict
+        DOCS: https://nightly.spacy.io/api/lookups#table.from_dict
         """
         self = cls(name=name)
         self.update(data)
@@ -69,7 +69,7 @@ class Table(OrderedDict):
         name (str): Optional table name for reference.
         data (dict): Initial data, used to hint Bloom Filter.
 
-        DOCS: https://spacy.io/api/lookups#table.init
+        DOCS: https://nightly.spacy.io/api/lookups#table.init
         """
         OrderedDict.__init__(self)
         self.name = name
@@ -135,7 +135,7 @@ class Table(OrderedDict):
 
         RETURNS (bytes): The serialized table.
 
-        DOCS: https://spacy.io/api/lookups#table.to_bytes
+        DOCS: https://nightly.spacy.io/api/lookups#table.to_bytes
         """
         data = {
             "name": self.name,
@@ -150,7 +150,7 @@ class Table(OrderedDict):
         bytes_data (bytes): The data to load.
         RETURNS (Table): The loaded table.
 
-        DOCS: https://spacy.io/api/lookups#table.from_bytes
+        DOCS: https://nightly.spacy.io/api/lookups#table.from_bytes
         """
         loaded = srsly.msgpack_loads(bytes_data)
         data = loaded.get("dict", {})
@@ -172,7 +172,7 @@ class Lookups:
     def __init__(self) -> None:
         """Initialize the Lookups object.
 
-        DOCS: https://spacy.io/api/lookups#init
+        DOCS: https://nightly.spacy.io/api/lookups#init
         """
         self._tables = {}
 
@@ -201,7 +201,7 @@ class Lookups:
         data (dict): Optional data to add to the table.
         RETURNS (Table): The newly added table.
 
-        DOCS: https://spacy.io/api/lookups#add_table
+        DOCS: https://nightly.spacy.io/api/lookups#add_table
         """
         if name in self.tables:
             raise ValueError(Errors.E158.format(name=name))
@@ -215,7 +215,7 @@ class Lookups:
         name (str): Name of the table to set.
         table (Table): The Table to set.
 
-        DOCS: https://spacy.io/api/lookups#set_table
+        DOCS: https://nightly.spacy.io/api/lookups#set_table
         """
         self._tables[name] = table
 
@@ -227,7 +227,7 @@ class Lookups:
         default (Any): Optional default value to return if table doesn't exist.
         RETURNS (Table): The table.
 
-        DOCS: https://spacy.io/api/lookups#get_table
+        DOCS: https://nightly.spacy.io/api/lookups#get_table
         """
         if name not in self._tables:
             if default == UNSET:
@@ -241,7 +241,7 @@ class Lookups:
         name (str): Name of the table to remove.
         RETURNS (Table): The removed table.
 
-        DOCS: https://spacy.io/api/lookups#remove_table
+        DOCS: https://nightly.spacy.io/api/lookups#remove_table
         """
         if name not in self._tables:
             raise KeyError(Errors.E159.format(name=name, tables=self.tables))
@@ -253,7 +253,7 @@ class Lookups:
         name (str): Name of the table.
         RETURNS (bool): Whether a table of that name exists.
 
-        DOCS: https://spacy.io/api/lookups#has_table
+        DOCS: https://nightly.spacy.io/api/lookups#has_table
         """
         return name in self._tables
 
@@ -262,7 +262,7 @@ class Lookups:
 
         RETURNS (bytes): The serialized Lookups.
 
-        DOCS: https://spacy.io/api/lookups#to_bytes
+        DOCS: https://nightly.spacy.io/api/lookups#to_bytes
         """
         return srsly.msgpack_dumps(self._tables)
 
@@ -272,7 +272,7 @@ class Lookups:
         bytes_data (bytes): The data to load.
         RETURNS (Lookups): The loaded Lookups.
 
-        DOCS: https://spacy.io/api/lookups#from_bytes
+        DOCS: https://nightly.spacy.io/api/lookups#from_bytes
         """
         self._tables = {}
         for key, value in srsly.msgpack_loads(bytes_data).items():
@@ -287,7 +287,7 @@ class Lookups:
 
         path (str / Path): The file path.
 
-        DOCS: https://spacy.io/api/lookups#to_disk
+        DOCS: https://nightly.spacy.io/api/lookups#to_disk
         """
         if len(self._tables):
             path = ensure_path(path)
@@ -306,7 +306,7 @@ class Lookups:
         path (str / Path): The directory path.
         RETURNS (Lookups): The loaded lookups.
 
-        DOCS: https://spacy.io/api/lookups#from_disk
+        DOCS: https://nightly.spacy.io/api/lookups#from_disk
         """
         path = ensure_path(path)
         filepath = path / filename
diff --git a/spacy/matcher/matcher.pyx b/spacy/matcher/matcher.pyx
index 16ab73735..a170c7a6b 100644
--- a/spacy/matcher/matcher.pyx
+++ b/spacy/matcher/matcher.pyx
@@ -31,8 +31,8 @@ DEF PADDING = 5
 cdef class Matcher:
     """Match sequences of tokens, based on pattern rules.
 
-    DOCS: https://spacy.io/api/matcher
-    USAGE: https://spacy.io/usage/rule-based-matching
+    DOCS: https://nightly.spacy.io/api/matcher
+    USAGE: https://nightly.spacy.io/usage/rule-based-matching
     """
 
     def __init__(self, vocab, validate=True):
@@ -176,18 +176,10 @@ cdef class Matcher:
         return (self._callbacks[key], self._patterns[key])
 
     def pipe(self, docs, batch_size=1000, return_matches=False, as_tuples=False):
-        """Match a stream of documents, yielding them in turn.
-
-        docs (Iterable[Union[Doc, Span]]): A stream of documents or spans.
-        batch_size (int): Number of documents to accumulate into a working set.
-        return_matches (bool): Yield the match lists along with the docs, making
-            results (doc, matches) tuples.
-        as_tuples (bool): Interpret the input stream as (doc, context) tuples,
-            and yield (result, context) tuples out.
-            If both return_matches and as_tuples are True, the output will
-            be a sequence of ((doc, matches), context) tuples.
-        YIELDS (Doc): Documents, in order.
+        """Match a stream of documents, yielding them in turn. Deprecated as of
+        spaCy v3.0.
         """
+        warnings.warn(Warnings.W105.format(matcher="Matcher"), DeprecationWarning)
         if as_tuples:
             for doc, context in docs:
                 matches = self(doc)
@@ -203,13 +195,16 @@ cdef class Matcher:
                 else:
                     yield doc
 
-    def __call__(self, object doclike):
+    def __call__(self, object doclike, *, as_spans=False):
         """Find all token sequences matching the supplied pattern.
 
         doclike (Doc or Span): The document to match over.
-        RETURNS (list): A list of `(key, start, end)` tuples,
+        as_spans (bool): Return Span objects with labels instead of (match_id,
+            start, end) tuples.
+        RETURNS (list): A list of `(match_id, start, end)` tuples,
             describing the matches. A match tuple describes a span
-            `doc[start:end]`. The `label_id` and `key` are both integers.
+            `doc[start:end]`. The `match_id` is an integer. If as_spans is set
+            to True, a list of Span objects is returned.
         """
         if isinstance(doclike, Doc):
             doc = doclike
@@ -262,7 +257,10 @@ cdef class Matcher:
             on_match = self._callbacks.get(key, None)
             if on_match is not None:
                 on_match(self, doc, i, final_matches)
-        return final_matches
+        if as_spans:
+            return [Span(doc, start, end, label=key) for key, start, end in final_matches]
+        else:
+            return final_matches
 
     def _normalize_key(self, key):
         if isinstance(key, basestring):
diff --git a/spacy/matcher/phrasematcher.pyx b/spacy/matcher/phrasematcher.pyx
index 060c4d37f..fae513367 100644
--- a/spacy/matcher/phrasematcher.pyx
+++ b/spacy/matcher/phrasematcher.pyx
@@ -7,6 +7,7 @@ import warnings
 from ..attrs cimport ORTH, POS, TAG, DEP, LEMMA
 from ..structs cimport TokenC
 from ..tokens.token cimport Token
+from ..tokens.span cimport Span
 from ..typedefs cimport attr_t
 
 from ..schemas import TokenPattern
@@ -18,8 +19,8 @@ cdef class PhraseMatcher:
     sequences based on lists of token descriptions, the `PhraseMatcher` accepts
     match patterns in the form of `Doc` objects.
 
-    DOCS: https://spacy.io/api/phrasematcher
-    USAGE: https://spacy.io/usage/rule-based-matching#phrasematcher
+    DOCS: https://nightly.spacy.io/api/phrasematcher
+    USAGE: https://nightly.spacy.io/usage/rule-based-matching#phrasematcher
 
     Adapted from FlashText: https://github.com/vi3k6i5/flashtext
     MIT License (see `LICENSE`)
@@ -33,7 +34,7 @@ cdef class PhraseMatcher:
         attr (int / str): Token attribute to match on.
         validate (bool): Perform additional validation when patterns are added.
 
-        DOCS: https://spacy.io/api/phrasematcher#init
+        DOCS: https://nightly.spacy.io/api/phrasematcher#init
         """
         self.vocab = vocab
         self._callbacks = {}
@@ -60,7 +61,7 @@ cdef class PhraseMatcher:
 
         RETURNS (int): The number of rules.
 
-        DOCS: https://spacy.io/api/phrasematcher#len
+        DOCS: https://nightly.spacy.io/api/phrasematcher#len
         """
         return len(self._callbacks)
 
@@ -70,7 +71,7 @@ cdef class PhraseMatcher:
         key (str): The match ID.
         RETURNS (bool): Whether the matcher contains rules for this match ID.
 
-        DOCS: https://spacy.io/api/phrasematcher#contains
+        DOCS: https://nightly.spacy.io/api/phrasematcher#contains
         """
         return key in self._callbacks
 
@@ -84,7 +85,7 @@ cdef class PhraseMatcher:
 
         key (str): The match ID.
 
-        DOCS: https://spacy.io/api/phrasematcher#remove
+        DOCS: https://nightly.spacy.io/api/phrasematcher#remove
         """
         if key not in self._docs:
             raise KeyError(key)
@@ -163,7 +164,7 @@ cdef class PhraseMatcher:
             as variable arguments. Will be ignored if a list of patterns is
             provided as the second argument.
 
-        DOCS: https://spacy.io/api/phrasematcher#add
+        DOCS: https://nightly.spacy.io/api/phrasematcher#add
         """
         if docs is None or hasattr(docs, "__call__"):  # old API
             on_match = docs
@@ -216,15 +217,18 @@ cdef class PhraseMatcher:
                 result = internal_node
             map_set(self.mem, <MapStruct*>result, self.vocab.strings[key], NULL)
 
-    def __call__(self, doc):
+    def __call__(self, doc, *, as_spans=False):
         """Find all sequences matching the supplied patterns on the `Doc`.
 
         doc (Doc): The document to match over.
-        RETURNS (list): A list of `(key, start, end)` tuples,
+        as_spans (bool): Return Span objects with labels instead of (match_id,
+            start, end) tuples.
+        RETURNS (list): A list of `(match_id, start, end)` tuples,
             describing the matches. A match tuple describes a span
-            `doc[start:end]`. The `label_id` and `key` are both integers.
+            `doc[start:end]`. The `match_id` is an integer. If as_spans is set
+            to True, a list of Span objects is returned.
 
-        DOCS: https://spacy.io/api/phrasematcher#call
+        DOCS: https://nightly.spacy.io/api/phrasematcher#call
         """
         matches = []
         if doc is None or len(doc) == 0:
@@ -239,7 +243,10 @@ cdef class PhraseMatcher:
             on_match = self._callbacks.get(self.vocab.strings[ent_id])
             if on_match is not None:
                 on_match(self, doc, i, matches)
-        return matches
+        if as_spans:
+            return [Span(doc, start, end, label=key) for key, start, end in matches]
+        else:
+            return matches
 
     cdef void find_matches(self, Doc doc, vector[SpanC] *matches) nogil:
         cdef MapStruct* current_node = self.c_map
@@ -285,20 +292,10 @@ cdef class PhraseMatcher:
             idx += 1
 
     def pipe(self, stream, batch_size=1000, return_matches=False, as_tuples=False):
-        """Match a stream of documents, yielding them in turn.
-
-        docs (iterable): A stream of documents.
-        batch_size (int): Number of documents to accumulate into a working set.
-        return_matches (bool): Yield the match lists along with the docs, making
-            results (doc, matches) tuples.
-        as_tuples (bool): Interpret the input stream as (doc, context) tuples,
-            and yield (result, context) tuples out.
-            If both return_matches and as_tuples are True, the output will
-            be a sequence of ((doc, matches), context) tuples.
-        YIELDS (Doc): Documents, in order.
-
-        DOCS: https://spacy.io/api/phrasematcher#pipe
+        """Match a stream of documents, yielding them in turn. Deprecated as of
+        spaCy v3.0.
         """
+        warnings.warn(Warnings.W105.format(matcher="PhraseMatcher"), DeprecationWarning)
         if as_tuples:
             for doc, context in stream:
                 matches = self(doc)
diff --git a/spacy/ml/models/entity_linker.py b/spacy/ml/models/entity_linker.py
index 6792f3e59..d945e5fba 100644
--- a/spacy/ml/models/entity_linker.py
+++ b/spacy/ml/models/entity_linker.py
@@ -24,7 +24,7 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
     return model
 
 
-@registry.assets.register("spacy.KBFromFile.v1")
+@registry.misc.register("spacy.KBFromFile.v1")
 def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
     def kb_from_file(vocab):
         kb = KnowledgeBase(vocab, entity_vector_length=1)
@@ -34,7 +34,7 @@ def load_kb(kb_path: str) -> Callable[[Vocab], KnowledgeBase]:
     return kb_from_file
 
 
-@registry.assets.register("spacy.EmptyKB.v1")
+@registry.misc.register("spacy.EmptyKB.v1")
 def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
     def empty_kb_factory(vocab):
         return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
@@ -42,6 +42,6 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
     return empty_kb_factory
 
 
-@registry.assets.register("spacy.CandidateGenerator.v1")
+@registry.misc.register("spacy.CandidateGenerator.v1")
 def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
     return get_candidates
diff --git a/spacy/pipeline/attributeruler.py b/spacy/pipeline/attributeruler.py
index 85a425e29..406112681 100644
--- a/spacy/pipeline/attributeruler.py
+++ b/spacy/pipeline/attributeruler.py
@@ -38,7 +38,7 @@ class AttributeRuler(Pipe):
     """Set token-level attributes for tokens matched by Matcher patterns.
     Additionally supports importing patterns from tag maps and morph rules.
 
-    DOCS: https://spacy.io/api/attributeruler
+    DOCS: https://nightly.spacy.io/api/attributeruler
     """
 
     def __init__(
@@ -59,7 +59,7 @@ class AttributeRuler(Pipe):
 
         RETURNS (AttributeRuler): The AttributeRuler component.
 
-        DOCS: https://spacy.io/api/attributeruler#init
+        DOCS: https://nightly.spacy.io/api/attributeruler#init
         """
         self.name = name
         self.vocab = vocab
@@ -77,7 +77,7 @@ class AttributeRuler(Pipe):
         doc (Doc): The document to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/attributeruler#call
+        DOCS: https://nightly.spacy.io/api/attributeruler#call
         """
         matches = sorted(self.matcher(doc))
 
@@ -121,7 +121,7 @@ class AttributeRuler(Pipe):
         tag_map (dict): The tag map that maps fine-grained tags to
             coarse-grained tags and morphological features.
 
-        DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
+        DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
         """
         for tag, attrs in tag_map.items():
             pattern = [{"TAG": tag}]
@@ -139,7 +139,7 @@ class AttributeRuler(Pipe):
             fine-grained tags to coarse-grained tags, lemmas and morphological
             features.
 
-        DOCS: https://spacy.io/api/attributeruler#load_from_morph_rules
+        DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
         """
         for tag in morph_rules:
             for word in morph_rules[tag]:
@@ -163,7 +163,7 @@ class AttributeRuler(Pipe):
         index (int): The index of the token in the matched span to modify. May
             be negative to index from the end of the span. Defaults to 0.
 
-        DOCS: https://spacy.io/api/attributeruler#add
+        DOCS: https://nightly.spacy.io/api/attributeruler#add
         """
         self.matcher.add(len(self.attrs), patterns)
         self._attrs_unnormed.append(attrs)
@@ -178,7 +178,7 @@ class AttributeRuler(Pipe):
             as the arguments to AttributeRuler.add (patterns/attrs/index) to
             add as patterns.
 
-        DOCS: https://spacy.io/api/attributeruler#add_patterns
+        DOCS: https://nightly.spacy.io/api/attributeruler#add_patterns
         """
         for p in pattern_dicts:
             self.add(**p)
@@ -203,7 +203,7 @@ class AttributeRuler(Pipe):
             Scorer.score_token_attr for the attributes "tag", "pos", "morph"
             and "lemma" for the target token attributes.
 
-        DOCS: https://spacy.io/api/tagger#score
+        DOCS: https://nightly.spacy.io/api/tagger#score
         """
         validate_examples(examples, "AttributeRuler.score")
         results = {}
@@ -227,7 +227,7 @@ class AttributeRuler(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/attributeruler#to_bytes
+        DOCS: https://nightly.spacy.io/api/attributeruler#to_bytes
         """
         serialize = {}
         serialize["vocab"] = self.vocab.to_bytes
@@ -243,7 +243,7 @@ class AttributeRuler(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         returns (AttributeRuler): The loaded object.
 
-        DOCS: https://spacy.io/api/attributeruler#from_bytes
+        DOCS: https://nightly.spacy.io/api/attributeruler#from_bytes
         """
 
         def load_patterns(b):
@@ -264,7 +264,7 @@ class AttributeRuler(Pipe):
 
         path (Union[Path, str]): A path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
-        DOCS: https://spacy.io/api/attributeruler#to_disk
+        DOCS: https://nightly.spacy.io/api/attributeruler#to_disk
         """
         serialize = {
             "vocab": lambda p: self.vocab.to_disk(p),
@@ -279,7 +279,7 @@ class AttributeRuler(Pipe):
 
         path (Union[Path, str]): A path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
-        DOCS: https://spacy.io/api/attributeruler#from_disk
+        DOCS: https://nightly.spacy.io/api/attributeruler#from_disk
         """
 
         def load_patterns(p):
diff --git a/spacy/pipeline/dep_parser.pyx b/spacy/pipeline/dep_parser.pyx
index 76f58df58..e001920a6 100644
--- a/spacy/pipeline/dep_parser.pyx
+++ b/spacy/pipeline/dep_parser.pyx
@@ -105,7 +105,7 @@ def make_parser(
 cdef class DependencyParser(Parser):
     """Pipeline component for dependency parsing.
 
-    DOCS: https://spacy.io/api/dependencyparser
+    DOCS: https://nightly.spacy.io/api/dependencyparser
     """
     TransitionSystem = ArcEager
 
@@ -146,7 +146,7 @@ cdef class DependencyParser(Parser):
         RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
             and Scorer.score_deps.
 
-        DOCS: https://spacy.io/api/dependencyparser#score
+        DOCS: https://nightly.spacy.io/api/dependencyparser#score
         """
         validate_examples(examples, "DependencyParser.score")
         def dep_getter(token, attr):
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index c45cdce75..d4f1e6b56 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -39,12 +39,12 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
     requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
     assigns=["token.ent_kb_id"],
     default_config={
-        "kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64},
+        "kb_loader": {"@misc": "spacy.EmptyKB.v1", "entity_vector_length": 64},
         "model": DEFAULT_NEL_MODEL,
         "labels_discard": [],
         "incl_prior": True,
         "incl_context": True,
-        "get_candidates": {"@assets": "spacy.CandidateGenerator.v1"},
+        "get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
     },
 )
 def make_entity_linker(
@@ -83,7 +83,7 @@ def make_entity_linker(
 class EntityLinker(Pipe):
     """Pipeline component for named entity linking.
 
-    DOCS: https://spacy.io/api/entitylinker
+    DOCS: https://nightly.spacy.io/api/entitylinker
     """
 
     NIL = "NIL"  # string used to refer to a non-existing link
@@ -111,7 +111,7 @@ class EntityLinker(Pipe):
         incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
         incl_context (bool): Whether or not to include the local context in the model.
 
-        DOCS: https://spacy.io/api/entitylinker#init
+        DOCS: https://nightly.spacy.io/api/entitylinker#init
         """
         self.vocab = vocab
         self.model = model
@@ -151,7 +151,7 @@ class EntityLinker(Pipe):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/entitylinker#begin_training
+        DOCS: https://nightly.spacy.io/api/entitylinker#begin_training
         """
         self.require_kb()
         nO = self.kb.entity_vector_length
@@ -182,7 +182,7 @@ class EntityLinker(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/entitylinker#update
+        DOCS: https://nightly.spacy.io/api/entitylinker#update
         """
         self.require_kb()
         if losses is None:
@@ -264,7 +264,7 @@ class EntityLinker(Pipe):
         doc (Doc): The document to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/entitylinker#call
+        DOCS: https://nightly.spacy.io/api/entitylinker#call
         """
         kb_ids = self.predict([doc])
         self.set_annotations([doc], kb_ids)
@@ -279,7 +279,7 @@ class EntityLinker(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/entitylinker#pipe
+        DOCS: https://nightly.spacy.io/api/entitylinker#pipe
         """
         for docs in util.minibatch(stream, size=batch_size):
             kb_ids = self.predict(docs)
@@ -294,7 +294,7 @@ class EntityLinker(Pipe):
         docs (Iterable[Doc]): The documents to predict.
         RETURNS (List[int]): The models prediction for each document.
 
-        DOCS: https://spacy.io/api/entitylinker#predict
+        DOCS: https://nightly.spacy.io/api/entitylinker#predict
         """
         self.require_kb()
         entity_count = 0
@@ -391,7 +391,7 @@ class EntityLinker(Pipe):
         docs (Iterable[Doc]): The documents to modify.
         kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
 
-        DOCS: https://spacy.io/api/entitylinker#set_annotations
+        DOCS: https://nightly.spacy.io/api/entitylinker#set_annotations
         """
         count_ents = len([ent for doc in docs for ent in doc.ents])
         if count_ents != len(kb_ids):
@@ -412,7 +412,7 @@ class EntityLinker(Pipe):
         path (str / Path): Path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/entitylinker#to_disk
+        DOCS: https://nightly.spacy.io/api/entitylinker#to_disk
         """
         serialize = {}
         serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
@@ -430,7 +430,7 @@ class EntityLinker(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (EntityLinker): The modified EntityLinker object.
 
-        DOCS: https://spacy.io/api/entitylinker#from_disk
+        DOCS: https://nightly.spacy.io/api/entitylinker#from_disk
         """
 
         def load_model(p):
diff --git a/spacy/pipeline/entityruler.py b/spacy/pipeline/entityruler.py
index 5137dfec2..9a87c8589 100644
--- a/spacy/pipeline/entityruler.py
+++ b/spacy/pipeline/entityruler.py
@@ -53,8 +53,8 @@ class EntityRuler:
     purely rule-based entity recognition system. After initialization, the
     component is typically added to the pipeline using `nlp.add_pipe`.
 
-    DOCS: https://spacy.io/api/entityruler
-    USAGE: https://spacy.io/usage/rule-based-matching#entityruler
+    DOCS: https://nightly.spacy.io/api/entityruler
+    USAGE: https://nightly.spacy.io/usage/rule-based-matching#entityruler
     """
 
     def __init__(
@@ -88,7 +88,7 @@ class EntityRuler:
             added by the model, overwrite them by matches if necessary.
         ent_id_sep (str): Separator used internally for entity IDs.
 
-        DOCS: https://spacy.io/api/entityruler#init
+        DOCS: https://nightly.spacy.io/api/entityruler#init
         """
         self.nlp = nlp
         self.name = name
@@ -127,7 +127,7 @@ class EntityRuler:
         doc (Doc): The Doc object in the pipeline.
         RETURNS (Doc): The Doc with added entities, if available.
 
-        DOCS: https://spacy.io/api/entityruler#call
+        DOCS: https://nightly.spacy.io/api/entityruler#call
         """
         matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc))
         matches = set(
@@ -165,7 +165,7 @@ class EntityRuler:
 
         RETURNS (set): The string labels.
 
-        DOCS: https://spacy.io/api/entityruler#labels
+        DOCS: https://nightly.spacy.io/api/entityruler#labels
         """
         keys = set(self.token_patterns.keys())
         keys.update(self.phrase_patterns.keys())
@@ -185,7 +185,7 @@ class EntityRuler:
 
         RETURNS (set): The string entity ids.
 
-        DOCS: https://spacy.io/api/entityruler#ent_ids
+        DOCS: https://nightly.spacy.io/api/entityruler#ent_ids
         """
         keys = set(self.token_patterns.keys())
         keys.update(self.phrase_patterns.keys())
@@ -203,7 +203,7 @@ class EntityRuler:
 
         RETURNS (list): The original patterns, one dictionary per pattern.
 
-        DOCS: https://spacy.io/api/entityruler#patterns
+        DOCS: https://nightly.spacy.io/api/entityruler#patterns
         """
         all_patterns = []
         for label, patterns in self.token_patterns.items():
@@ -230,7 +230,7 @@ class EntityRuler:
 
         patterns (list): The patterns to add.
 
-        DOCS: https://spacy.io/api/entityruler#add_patterns
+        DOCS: https://nightly.spacy.io/api/entityruler#add_patterns
         """
 
         # disable the nlp components after this one in case they hadn't been initialized / deserialised yet
@@ -324,7 +324,7 @@ class EntityRuler:
         patterns_bytes (bytes): The bytestring to load.
         RETURNS (EntityRuler): The loaded entity ruler.
 
-        DOCS: https://spacy.io/api/entityruler#from_bytes
+        DOCS: https://nightly.spacy.io/api/entityruler#from_bytes
         """
         cfg = srsly.msgpack_loads(patterns_bytes)
         self.clear()
@@ -346,7 +346,7 @@ class EntityRuler:
 
         RETURNS (bytes): The serialized patterns.
 
-        DOCS: https://spacy.io/api/entityruler#to_bytes
+        DOCS: https://nightly.spacy.io/api/entityruler#to_bytes
         """
         serial = {
             "overwrite": self.overwrite,
@@ -365,7 +365,7 @@ class EntityRuler:
         path (str / Path): The JSONL file to load.
         RETURNS (EntityRuler): The loaded entity ruler.
 
-        DOCS: https://spacy.io/api/entityruler#from_disk
+        DOCS: https://nightly.spacy.io/api/entityruler#from_disk
         """
         path = ensure_path(path)
         self.clear()
@@ -401,7 +401,7 @@ class EntityRuler:
 
         path (str / Path): The JSONL file to save.
 
-        DOCS: https://spacy.io/api/entityruler#to_disk
+        DOCS: https://nightly.spacy.io/api/entityruler#to_disk
         """
         path = ensure_path(path)
         cfg = {
diff --git a/spacy/pipeline/functions.py b/spacy/pipeline/functions.py
index 501884873..7e68ea369 100644
--- a/spacy/pipeline/functions.py
+++ b/spacy/pipeline/functions.py
@@ -15,7 +15,7 @@ def merge_noun_chunks(doc: Doc) -> Doc:
     doc (Doc): The Doc object.
     RETURNS (Doc): The Doc object with merged noun chunks.
 
-    DOCS: https://spacy.io/api/pipeline-functions#merge_noun_chunks
+    DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_noun_chunks
     """
     if not doc.is_parsed:
         return doc
@@ -37,7 +37,7 @@ def merge_entities(doc: Doc):
     doc (Doc): The Doc object.
     RETURNS (Doc): The Doc object with merged entities.
 
-    DOCS: https://spacy.io/api/pipeline-functions#merge_entities
+    DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_entities
     """
     with doc.retokenize() as retokenizer:
         for ent in doc.ents:
@@ -54,7 +54,7 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
     label (str): The subtoken dependency label.
     RETURNS (Doc): The Doc object with merged subtokens.
 
-    DOCS: https://spacy.io/api/pipeline-functions#merge_subtokens
+    DOCS: https://nightly.spacy.io/api/pipeline-functions#merge_subtokens
     """
     # TODO: make stateful component with "label" config
     merger = Matcher(doc.vocab)
diff --git a/spacy/pipeline/lemmatizer.py b/spacy/pipeline/lemmatizer.py
index 6cea65fec..3f3e387b7 100644
--- a/spacy/pipeline/lemmatizer.py
+++ b/spacy/pipeline/lemmatizer.py
@@ -43,7 +43,7 @@ class Lemmatizer(Pipe):
     The Lemmatizer supports simple part-of-speech-sensitive suffix rules and
     lookup tables.
 
-    DOCS: https://spacy.io/api/lemmatizer
+    DOCS: https://nightly.spacy.io/api/lemmatizer
     """
 
     @classmethod
@@ -54,7 +54,7 @@ class Lemmatizer(Pipe):
         mode (str): The lemmatizer mode.
         RETURNS (dict): The lookups configuration settings for this mode.
 
-        DOCS: https://spacy.io/api/lemmatizer#get_lookups_config
+        DOCS: https://nightly.spacy.io/api/lemmatizer#get_lookups_config
         """
         if mode == "lookup":
             return {
@@ -80,7 +80,7 @@ class Lemmatizer(Pipe):
             lookups should be loaded.
         RETURNS (Lookups): The Lookups object.
 
-        DOCS: https://spacy.io/api/lemmatizer#get_lookups_config
+        DOCS: https://nightly.spacy.io/api/lemmatizer#get_lookups_config
         """
         config = cls.get_lookups_config(mode)
         required_tables = config.get("required_tables", [])
@@ -123,7 +123,7 @@ class Lemmatizer(Pipe):
         overwrite (bool): Whether to overwrite existing lemmas. Defaults to
             `False`.
 
-        DOCS: https://spacy.io/api/lemmatizer#init
+        DOCS: https://nightly.spacy.io/api/lemmatizer#init
         """
         self.vocab = vocab
         self.model = model
@@ -152,7 +152,7 @@ class Lemmatizer(Pipe):
         doc (Doc): The Doc to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/lemmatizer#call
+        DOCS: https://nightly.spacy.io/api/lemmatizer#call
         """
         for token in doc:
             if self.overwrite or token.lemma == 0:
@@ -168,7 +168,7 @@ class Lemmatizer(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/lemmatizer#pipe
+        DOCS: https://nightly.spacy.io/api/lemmatizer#pipe
         """
         for doc in stream:
             doc = self(doc)
@@ -180,7 +180,7 @@ class Lemmatizer(Pipe):
         token (Token): The token to lemmatize.
         RETURNS (list): The available lemmas for the string.
 
-        DOCS: https://spacy.io/api/lemmatizer#lookup_lemmatize
+        DOCS: https://nightly.spacy.io/api/lemmatizer#lookup_lemmatize
         """
         lookup_table = self.lookups.get_table("lemma_lookup", {})
         result = lookup_table.get(token.text, token.text)
@@ -194,7 +194,7 @@ class Lemmatizer(Pipe):
         token (Token): The token to lemmatize.
         RETURNS (list): The available lemmas for the string.
 
-        DOCS: https://spacy.io/api/lemmatizer#rule_lemmatize
+        DOCS: https://nightly.spacy.io/api/lemmatizer#rule_lemmatize
         """
         cache_key = (token.orth, token.pos, token.morph)
         if cache_key in self.cache:
@@ -260,7 +260,7 @@ class Lemmatizer(Pipe):
         token (Token): The token.
         RETURNS (bool): Whether the token is a base form.
 
-        DOCS: https://spacy.io/api/lemmatizer#is_base_form
+        DOCS: https://nightly.spacy.io/api/lemmatizer#is_base_form
         """
         return False
 
@@ -270,7 +270,7 @@ class Lemmatizer(Pipe):
         examples (Iterable[Example]): The examples to score.
         RETURNS (Dict[str, Any]): The scores.
 
-        DOCS: https://spacy.io/api/lemmatizer#score
+        DOCS: https://nightly.spacy.io/api/lemmatizer#score
         """
         validate_examples(examples, "Lemmatizer.score")
         return Scorer.score_token_attr(examples, "lemma", **kwargs)
@@ -282,7 +282,7 @@ class Lemmatizer(Pipe):
             it doesn't exist.
         exclude (list): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/vocab#to_disk
+        DOCS: https://nightly.spacy.io/api/vocab#to_disk
         """
         serialize = {}
         serialize["vocab"] = lambda p: self.vocab.to_disk(p)
@@ -297,7 +297,7 @@ class Lemmatizer(Pipe):
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Vocab): The modified `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#to_disk
+        DOCS: https://nightly.spacy.io/api/vocab#to_disk
         """
         deserialize = {}
         deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
@@ -310,7 +310,7 @@ class Lemmatizer(Pipe):
         exclude (list): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized form of the `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#to_bytes
+        DOCS: https://nightly.spacy.io/api/vocab#to_bytes
         """
         serialize = {}
         serialize["vocab"] = self.vocab.to_bytes
@@ -324,7 +324,7 @@ class Lemmatizer(Pipe):
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Vocab): The `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#from_bytes
+        DOCS: https://nightly.spacy.io/api/vocab#from_bytes
         """
         deserialize = {}
         deserialize["vocab"] = lambda b: self.vocab.from_bytes(b)
diff --git a/spacy/pipeline/morphologizer.pyx b/spacy/pipeline/morphologizer.pyx
index 329a05f90..bcb555b90 100644
--- a/spacy/pipeline/morphologizer.pyx
+++ b/spacy/pipeline/morphologizer.pyx
@@ -79,7 +79,7 @@ class Morphologizer(Tagger):
         labels_morph (dict): Mapping of morph + POS tags to morph labels.
         labels_pos (dict): Mapping of morph + POS tags to POS tags.
 
-        DOCS: https://spacy.io/api/morphologizer#init
+        DOCS: https://nightly.spacy.io/api/morphologizer#init
         """
         self.vocab = vocab
         self.model = model
@@ -106,7 +106,7 @@ class Morphologizer(Tagger):
         label (str): The label to add.
         RETURNS (int): 0 if label is already present, otherwise 1.
 
-        DOCS: https://spacy.io/api/morphologizer#add_label
+        DOCS: https://nightly.spacy.io/api/morphologizer#add_label
         """
         if not isinstance(label, str):
             raise ValueError(Errors.E187)
@@ -139,7 +139,7 @@ class Morphologizer(Tagger):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/morphologizer#begin_training
+        DOCS: https://nightly.spacy.io/api/morphologizer#begin_training
         """
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="Morphologizer", obj=type(get_examples))
@@ -169,7 +169,7 @@ class Morphologizer(Tagger):
         docs (Iterable[Doc]): The documents to modify.
         batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
 
-        DOCS: https://spacy.io/api/morphologizer#set_annotations
+        DOCS: https://nightly.spacy.io/api/morphologizer#set_annotations
         """
         if isinstance(docs, Doc):
             docs = [docs]
@@ -194,7 +194,7 @@ class Morphologizer(Tagger):
         scores: Scores representing the model's predictions.
         RETUTNRS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/morphologizer#get_loss
+        DOCS: https://nightly.spacy.io/api/morphologizer#get_loss
         """
         validate_examples(examples, "Morphologizer.get_loss")
         loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
@@ -231,7 +231,7 @@ class Morphologizer(Tagger):
             Scorer.score_token_attr for the attributes "pos" and "morph" and
             Scorer.score_token_attr_per_feat for the attribute "morph".
 
-        DOCS: https://spacy.io/api/morphologizer#score
+        DOCS: https://nightly.spacy.io/api/morphologizer#score
         """
         validate_examples(examples, "Morphologizer.score")
         results = {}
@@ -247,7 +247,7 @@ class Morphologizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/morphologizer#to_bytes
+        DOCS: https://nightly.spacy.io/api/morphologizer#to_bytes
         """
         serialize = {}
         serialize["model"] = self.model.to_bytes
@@ -262,7 +262,7 @@ class Morphologizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Morphologizer): The loaded Morphologizer.
 
-        DOCS: https://spacy.io/api/morphologizer#from_bytes
+        DOCS: https://nightly.spacy.io/api/morphologizer#from_bytes
         """
         def load_model(b):
             try:
@@ -284,7 +284,7 @@ class Morphologizer(Tagger):
         path (str / Path): Path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/morphologizer#to_disk
+        DOCS: https://nightly.spacy.io/api/morphologizer#to_disk
         """
         serialize = {
             "vocab": lambda p: self.vocab.to_disk(p),
@@ -300,7 +300,7 @@ class Morphologizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Morphologizer): The modified Morphologizer object.
 
-        DOCS: https://spacy.io/api/morphologizer#from_disk
+        DOCS: https://nightly.spacy.io/api/morphologizer#from_disk
         """
         def load_model(p):
             with p.open("rb") as file_:
diff --git a/spacy/pipeline/ner.pyx b/spacy/pipeline/ner.pyx
index 631b5ae72..d9f33ccb4 100644
--- a/spacy/pipeline/ner.pyx
+++ b/spacy/pipeline/ner.pyx
@@ -88,7 +88,7 @@ def make_ner(
 cdef class EntityRecognizer(Parser):
     """Pipeline component for named entity recognition.
 
-    DOCS: https://spacy.io/api/entityrecognizer
+    DOCS: https://nightly.spacy.io/api/entityrecognizer
     """
     TransitionSystem = BiluoPushDown
 
@@ -119,7 +119,7 @@ cdef class EntityRecognizer(Parser):
         examples (Iterable[Example]): The examples to score.
         RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
 
-        DOCS: https://spacy.io/api/entityrecognizer#score
+        DOCS: https://nightly.spacy.io/api/entityrecognizer#score
         """
         validate_examples(examples, "EntityRecognizer.score")
         return Scorer.score_spans(examples, "ents", **kwargs)
diff --git a/spacy/pipeline/pipe.pyx b/spacy/pipeline/pipe.pyx
index 51251dacc..2518ebad3 100644
--- a/spacy/pipeline/pipe.pyx
+++ b/spacy/pipeline/pipe.pyx
@@ -15,7 +15,7 @@ cdef class Pipe:
     from it and it defines the interface that components should follow to
     function as trainable components in a spaCy pipeline.
 
-    DOCS: https://spacy.io/api/pipe
+    DOCS: https://nightly.spacy.io/api/pipe
     """
     def __init__(self, vocab, model, name, **cfg):
         """Initialize a pipeline component.
@@ -25,7 +25,7 @@ cdef class Pipe:
         name (str): The component instance name.
         **cfg: Additonal settings and config parameters.
 
-        DOCS: https://spacy.io/api/pipe#init
+        DOCS: https://nightly.spacy.io/api/pipe#init
         """
         self.vocab = vocab
         self.model = model
@@ -37,10 +37,10 @@ cdef class Pipe:
         and returned. This usually happens under the hood when the nlp object
         is called on a text and all components are applied to the Doc.
 
-        docs (Doc): The Doc to preocess.
+        docs (Doc): The Doc to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/pipe#call
+        DOCS: https://nightly.spacy.io/api/pipe#call
         """
         scores = self.predict([doc])
         self.set_annotations([doc], scores)
@@ -55,7 +55,7 @@ cdef class Pipe:
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/pipe#pipe
+        DOCS: https://nightly.spacy.io/api/pipe#pipe
         """
         for docs in util.minibatch(stream, size=batch_size):
             scores = self.predict(docs)
@@ -69,7 +69,7 @@ cdef class Pipe:
         docs (Iterable[Doc]): The documents to predict.
         RETURNS: Vector representations for each token in the documents.
 
-        DOCS: https://spacy.io/api/pipe#predict
+        DOCS: https://nightly.spacy.io/api/pipe#predict
         """
         raise NotImplementedError(Errors.E931.format(method="predict", name=self.name))
 
@@ -79,7 +79,7 @@ cdef class Pipe:
         docs (Iterable[Doc]): The documents to modify.
         scores: The scores to assign.
 
-        DOCS: https://spacy.io/api/pipe#set_annotations
+        DOCS: https://nightly.spacy.io/api/pipe#set_annotations
         """
         raise NotImplementedError(Errors.E931.format(method="set_annotations", name=self.name))
 
@@ -96,7 +96,7 @@ cdef class Pipe:
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/pipe#update
+        DOCS: https://nightly.spacy.io/api/pipe#update
         """
         if losses is None:
             losses = {}
@@ -132,7 +132,7 @@ cdef class Pipe:
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/pipe#rehearse
+        DOCS: https://nightly.spacy.io/api/pipe#rehearse
         """
         pass
 
@@ -144,7 +144,7 @@ cdef class Pipe:
         scores: Scores representing the model's predictions.
         RETUTNRS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/pipe#get_loss
+        DOCS: https://nightly.spacy.io/api/pipe#get_loss
         """
         raise NotImplementedError(Errors.E931.format(method="get_loss", name=self.name))
 
@@ -156,7 +156,7 @@ cdef class Pipe:
         label (str): The label to add.
         RETURNS (int): 0 if label is already present, otherwise 1.
 
-        DOCS: https://spacy.io/api/pipe#add_label
+        DOCS: https://nightly.spacy.io/api/pipe#add_label
         """
         raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
 
@@ -165,7 +165,7 @@ cdef class Pipe:
 
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/pipe#create_optimizer
+        DOCS: https://nightly.spacy.io/api/pipe#create_optimizer
         """
         return util.create_default_optimizer()
 
@@ -181,7 +181,7 @@ cdef class Pipe:
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/pipe#begin_training
+        DOCS: https://nightly.spacy.io/api/pipe#begin_training
         """
         self.model.initialize()
         if sgd is None:
@@ -200,7 +200,7 @@ cdef class Pipe:
 
         params (dict): The parameter values to use in the model.
 
-        DOCS: https://spacy.io/api/pipe#use_params
+        DOCS: https://nightly.spacy.io/api/pipe#use_params
         """
         with self.model.use_params(params):
             yield
@@ -211,7 +211,7 @@ cdef class Pipe:
         examples (Iterable[Example]): The examples to score.
         RETURNS (Dict[str, Any]): The scores.
 
-        DOCS: https://spacy.io/api/pipe#score
+        DOCS: https://nightly.spacy.io/api/pipe#score
         """
         return {}
 
@@ -221,7 +221,7 @@ cdef class Pipe:
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/pipe#to_bytes
+        DOCS: https://nightly.spacy.io/api/pipe#to_bytes
         """
         serialize = {}
         serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
@@ -236,7 +236,7 @@ cdef class Pipe:
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Pipe): The loaded object.
 
-        DOCS: https://spacy.io/api/pipe#from_bytes
+        DOCS: https://nightly.spacy.io/api/pipe#from_bytes
         """
 
         def load_model(b):
@@ -259,7 +259,7 @@ cdef class Pipe:
         path (str / Path): Path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/pipe#to_disk
+        DOCS: https://nightly.spacy.io/api/pipe#to_disk
         """
         serialize = {}
         serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
@@ -274,7 +274,7 @@ cdef class Pipe:
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Pipe): The loaded object.
 
-        DOCS: https://spacy.io/api/pipe#from_disk
+        DOCS: https://nightly.spacy.io/api/pipe#from_disk
         """
 
         def load_model(p):
diff --git a/spacy/pipeline/sentencizer.pyx b/spacy/pipeline/sentencizer.pyx
index 46d599497..aaf08d594 100644
--- a/spacy/pipeline/sentencizer.pyx
+++ b/spacy/pipeline/sentencizer.pyx
@@ -29,7 +29,7 @@ def make_sentencizer(
 class Sentencizer(Pipe):
     """Segment the Doc into sentences using a rule-based strategy.
 
-    DOCS: https://spacy.io/api/sentencizer
+    DOCS: https://nightly.spacy.io/api/sentencizer
     """
 
     default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
@@ -51,7 +51,7 @@ class Sentencizer(Pipe):
             serialized with the nlp object.
         RETURNS (Sentencizer): The sentencizer component.
 
-        DOCS: https://spacy.io/api/sentencizer#init
+        DOCS: https://nightly.spacy.io/api/sentencizer#init
         """
         self.name = name
         if punct_chars:
@@ -68,7 +68,7 @@ class Sentencizer(Pipe):
         doc (Doc): The document to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/sentencizer#call
+        DOCS: https://nightly.spacy.io/api/sentencizer#call
         """
         start = 0
         seen_period = False
@@ -94,7 +94,7 @@ class Sentencizer(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/sentencizer#pipe
+        DOCS: https://nightly.spacy.io/api/sentencizer#pipe
         """
         for docs in util.minibatch(stream, size=batch_size):
             predictions = self.predict(docs)
@@ -157,7 +157,7 @@ class Sentencizer(Pipe):
         examples (Iterable[Example]): The examples to score.
         RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
 
-        DOCS: https://spacy.io/api/sentencizer#score
+        DOCS: https://nightly.spacy.io/api/sentencizer#score
         """
         validate_examples(examples, "Sentencizer.score")
         results = Scorer.score_spans(examples, "sents", **kwargs)
@@ -169,7 +169,7 @@ class Sentencizer(Pipe):
 
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/sentencizer#to_bytes
+        DOCS: https://nightly.spacy.io/api/sentencizer#to_bytes
         """
         return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)})
 
@@ -179,7 +179,7 @@ class Sentencizer(Pipe):
         bytes_data (bytes): The data to load.
         returns (Sentencizer): The loaded object.
 
-        DOCS: https://spacy.io/api/sentencizer#from_bytes
+        DOCS: https://nightly.spacy.io/api/sentencizer#from_bytes
         """
         cfg = srsly.msgpack_loads(bytes_data)
         self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars))
@@ -188,7 +188,7 @@ class Sentencizer(Pipe):
     def to_disk(self, path, *, exclude=tuple()):
         """Serialize the sentencizer to disk.
 
-        DOCS: https://spacy.io/api/sentencizer#to_disk
+        DOCS: https://nightly.spacy.io/api/sentencizer#to_disk
         """
         path = util.ensure_path(path)
         path = path.with_suffix(".json")
@@ -198,7 +198,7 @@ class Sentencizer(Pipe):
     def from_disk(self, path, *, exclude=tuple()):
         """Load the sentencizer from disk.
 
-        DOCS: https://spacy.io/api/sentencizer#from_disk
+        DOCS: https://nightly.spacy.io/api/sentencizer#from_disk
         """
         path = util.ensure_path(path)
         path = path.with_suffix(".json")
diff --git a/spacy/pipeline/senter.pyx b/spacy/pipeline/senter.pyx
index e82225d27..b78be44f8 100644
--- a/spacy/pipeline/senter.pyx
+++ b/spacy/pipeline/senter.pyx
@@ -44,7 +44,7 @@ def make_senter(nlp: Language, name: str, model: Model):
 class SentenceRecognizer(Tagger):
     """Pipeline component for sentence segmentation.
 
-    DOCS: https://spacy.io/api/sentencerecognizer
+    DOCS: https://nightly.spacy.io/api/sentencerecognizer
     """
     def __init__(self, vocab, model, name="senter"):
         """Initialize a sentence recognizer.
@@ -54,7 +54,7 @@ class SentenceRecognizer(Tagger):
         name (str): The component instance name, used to add entries to the
             losses during training.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#init
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#init
         """
         self.vocab = vocab
         self.model = model
@@ -76,7 +76,7 @@ class SentenceRecognizer(Tagger):
         docs (Iterable[Doc]): The documents to modify.
         batch_tag_ids: The IDs to set, produced by SentenceRecognizer.predict.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#set_annotations
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#set_annotations
         """
         if isinstance(docs, Doc):
             docs = [docs]
@@ -101,7 +101,7 @@ class SentenceRecognizer(Tagger):
         scores: Scores representing the model's predictions.
         RETUTNRS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#get_loss
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#get_loss
         """
         validate_examples(examples, "SentenceRecognizer.get_loss")
         labels = self.labels
@@ -135,7 +135,7 @@ class SentenceRecognizer(Tagger):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#begin_training
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#begin_training
         """
         self.set_output(len(self.labels))
         self.model.initialize()
@@ -151,7 +151,7 @@ class SentenceRecognizer(Tagger):
 
         examples (Iterable[Example]): The examples to score.
         RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans.
-        DOCS: https://spacy.io/api/sentencerecognizer#score
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#score
         """
         validate_examples(examples, "SentenceRecognizer.score")
         results = Scorer.score_spans(examples, "sents", **kwargs)
@@ -164,7 +164,7 @@ class SentenceRecognizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#to_bytes
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#to_bytes
         """
         serialize = {}
         serialize["model"] = self.model.to_bytes
@@ -179,7 +179,7 @@ class SentenceRecognizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Tagger): The loaded SentenceRecognizer.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#from_bytes
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#from_bytes
         """
         def load_model(b):
             try:
@@ -201,7 +201,7 @@ class SentenceRecognizer(Tagger):
         path (str / Path): Path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#to_disk
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#to_disk
         """
         serialize = {
             "vocab": lambda p: self.vocab.to_disk(p),
@@ -217,7 +217,7 @@ class SentenceRecognizer(Tagger):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Tagger): The modified SentenceRecognizer object.
 
-        DOCS: https://spacy.io/api/sentencerecognizer#from_disk
+        DOCS: https://nightly.spacy.io/api/sentencerecognizer#from_disk
         """
         def load_model(p):
             with p.open("rb") as file_:
diff --git a/spacy/pipeline/simple_ner.py b/spacy/pipeline/simple_ner.py
index 5f3addbd7..c55edb067 100644
--- a/spacy/pipeline/simple_ner.py
+++ b/spacy/pipeline/simple_ner.py
@@ -78,7 +78,7 @@ class SimpleNER(Pipe):
     def add_label(self, label: str) -> None:
         """Add a new label to the pipe.
         label (str): The label to add.
-        DOCS: https://spacy.io/api/simplener#add_label
+        DOCS: https://nightly.spacy.io/api/simplener#add_label
         """
         if not isinstance(label, str):
             raise ValueError(Errors.E187)
diff --git a/spacy/pipeline/tagger.pyx b/spacy/pipeline/tagger.pyx
index af24bf336..2b760c878 100644
--- a/spacy/pipeline/tagger.pyx
+++ b/spacy/pipeline/tagger.pyx
@@ -58,7 +58,7 @@ def make_tagger(nlp: Language, name: str, model: Model):
 class Tagger(Pipe):
     """Pipeline component for part-of-speech tagging.
 
-    DOCS: https://spacy.io/api/tagger
+    DOCS: https://nightly.spacy.io/api/tagger
     """
     def __init__(self, vocab, model, name="tagger", *, labels=None):
         """Initialize a part-of-speech tagger.
@@ -69,7 +69,7 @@ class Tagger(Pipe):
             losses during training.
         labels (List): The set of labels. Defaults to None.
 
-        DOCS: https://spacy.io/api/tagger#init
+        DOCS: https://nightly.spacy.io/api/tagger#init
         """
         self.vocab = vocab
         self.model = model
@@ -86,7 +86,7 @@ class Tagger(Pipe):
 
         RETURNS (Tuple[str]): The labels.
 
-        DOCS: https://spacy.io/api/tagger#labels
+        DOCS: https://nightly.spacy.io/api/tagger#labels
         """
         return tuple(self.cfg["labels"])
 
@@ -96,7 +96,7 @@ class Tagger(Pipe):
         doc (Doc): The document to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/tagger#call
+        DOCS: https://nightly.spacy.io/api/tagger#call
         """
         tags = self.predict([doc])
         self.set_annotations([doc], tags)
@@ -111,7 +111,7 @@ class Tagger(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/tagger#pipe
+        DOCS: https://nightly.spacy.io/api/tagger#pipe
         """
         for docs in util.minibatch(stream, size=batch_size):
             tag_ids = self.predict(docs)
@@ -124,7 +124,7 @@ class Tagger(Pipe):
         docs (Iterable[Doc]): The documents to predict.
         RETURNS: The models prediction for each document.
 
-        DOCS: https://spacy.io/api/tagger#predict
+        DOCS: https://nightly.spacy.io/api/tagger#predict
         """
         if not any(len(doc) for doc in docs):
             # Handle cases where there are no tokens in any docs.
@@ -153,7 +153,7 @@ class Tagger(Pipe):
         docs (Iterable[Doc]): The documents to modify.
         batch_tag_ids: The IDs to set, produced by Tagger.predict.
 
-        DOCS: https://spacy.io/api/tagger#set_annotations
+        DOCS: https://nightly.spacy.io/api/tagger#set_annotations
         """
         if isinstance(docs, Doc):
             docs = [docs]
@@ -182,7 +182,7 @@ class Tagger(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/tagger#update
+        DOCS: https://nightly.spacy.io/api/tagger#update
         """
         if losses is None:
             losses = {}
@@ -220,7 +220,7 @@ class Tagger(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/tagger#rehearse
+        DOCS: https://nightly.spacy.io/api/tagger#rehearse
         """
         validate_examples(examples, "Tagger.rehearse")
         docs = [eg.predicted for eg in examples]
@@ -247,7 +247,7 @@ class Tagger(Pipe):
         scores: Scores representing the model's predictions.
         RETUTNRS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/tagger#get_loss
+        DOCS: https://nightly.spacy.io/api/tagger#get_loss
         """
         validate_examples(examples, "Tagger.get_loss")
         loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
@@ -269,7 +269,7 @@ class Tagger(Pipe):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/tagger#begin_training
+        DOCS: https://nightly.spacy.io/api/tagger#begin_training
         """
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="Tagger", obj=type(get_examples))
@@ -285,9 +285,16 @@ class Tagger(Pipe):
             doc_sample.append(Doc(self.vocab, words=["hello"]))
         for tag in sorted(tags):
             self.add_label(tag)
+        if len(self.labels) == 0:
+            err = Errors.E1006.format(name="Tagger")
+            raise ValueError(err)
         self.set_output(len(self.labels))
-        if self.labels:
-            self.model.initialize(X=doc_sample)
+        if doc_sample:
+            label_sample = [
+                self.model.ops.alloc2f(len(doc), len(self.labels))
+                for doc in doc_sample
+            ]
+            self.model.initialize(X=doc_sample, Y=label_sample)
         else:
             self.model.initialize()
         if sgd is None:
@@ -300,7 +307,7 @@ class Tagger(Pipe):
         label (str): The label to add.
         RETURNS (int): 0 if label is already present, otherwise 1.
 
-        DOCS: https://spacy.io/api/tagger#add_label
+        DOCS: https://nightly.spacy.io/api/tagger#add_label
         """
         if not isinstance(label, str):
             raise ValueError(Errors.E187)
@@ -317,7 +324,7 @@ class Tagger(Pipe):
         RETURNS (Dict[str, Any]): The scores, produced by
             Scorer.score_token_attr for the attributes "tag".
 
-        DOCS: https://spacy.io/api/tagger#score
+        DOCS: https://nightly.spacy.io/api/tagger#score
         """
         validate_examples(examples, "Tagger.score")
         return Scorer.score_token_attr(examples, "tag", **kwargs)
@@ -328,7 +335,7 @@ class Tagger(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized object.
 
-        DOCS: https://spacy.io/api/tagger#to_bytes
+        DOCS: https://nightly.spacy.io/api/tagger#to_bytes
         """
         serialize = {}
         serialize["model"] = self.model.to_bytes
@@ -343,7 +350,7 @@ class Tagger(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Tagger): The loaded Tagger.
 
-        DOCS: https://spacy.io/api/tagger#from_bytes
+        DOCS: https://nightly.spacy.io/api/tagger#from_bytes
         """
         def load_model(b):
             try:
@@ -365,7 +372,7 @@ class Tagger(Pipe):
         path (str / Path): Path to a directory.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/tagger#to_disk
+        DOCS: https://nightly.spacy.io/api/tagger#to_disk
         """
         serialize = {
             "vocab": lambda p: self.vocab.to_disk(p),
@@ -381,7 +388,7 @@ class Tagger(Pipe):
         exclude (Iterable[str]): String names of serialization fields to exclude.
         RETURNS (Tagger): The modified Tagger object.
 
-        DOCS: https://spacy.io/api/tagger#from_disk
+        DOCS: https://nightly.spacy.io/api/tagger#from_disk
         """
         def load_model(p):
             with p.open("rb") as file_:
diff --git a/spacy/pipeline/textcat.py b/spacy/pipeline/textcat.py
index ce4f286e5..d6efb4348 100644
--- a/spacy/pipeline/textcat.py
+++ b/spacy/pipeline/textcat.py
@@ -92,7 +92,7 @@ def make_textcat(
 class TextCategorizer(Pipe):
     """Pipeline component for text classification.
 
-    DOCS: https://spacy.io/api/textcategorizer
+    DOCS: https://nightly.spacy.io/api/textcategorizer
     """
 
     def __init__(
@@ -111,7 +111,7 @@ class TextCategorizer(Pipe):
             losses during training.
         labels (Iterable[str]): The labels to use.
 
-        DOCS: https://spacy.io/api/textcategorizer#init
+        DOCS: https://nightly.spacy.io/api/textcategorizer#init
         """
         self.vocab = vocab
         self.model = model
@@ -124,7 +124,7 @@ class TextCategorizer(Pipe):
     def labels(self) -> Tuple[str]:
         """RETURNS (Tuple[str]): The labels currently added to the component.
 
-        DOCS: https://spacy.io/api/textcategorizer#labels
+        DOCS: https://nightly.spacy.io/api/textcategorizer#labels
         """
         return tuple(self.cfg.setdefault("labels", []))
 
@@ -146,7 +146,7 @@ class TextCategorizer(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/textcategorizer#pipe
+        DOCS: https://nightly.spacy.io/api/textcategorizer#pipe
         """
         for docs in util.minibatch(stream, size=batch_size):
             scores = self.predict(docs)
@@ -159,7 +159,7 @@ class TextCategorizer(Pipe):
         docs (Iterable[Doc]): The documents to predict.
         RETURNS: The models prediction for each document.
 
-        DOCS: https://spacy.io/api/textcategorizer#predict
+        DOCS: https://nightly.spacy.io/api/textcategorizer#predict
         """
         tensors = [doc.tensor for doc in docs]
         if not any(len(doc) for doc in docs):
@@ -177,7 +177,7 @@ class TextCategorizer(Pipe):
         docs (Iterable[Doc]): The documents to modify.
         scores: The scores to set, produced by TextCategorizer.predict.
 
-        DOCS: https://spacy.io/api/textcategorizer#set_annotations
+        DOCS: https://nightly.spacy.io/api/textcategorizer#set_annotations
         """
         for i, doc in enumerate(docs):
             for j, label in enumerate(self.labels):
@@ -204,7 +204,7 @@ class TextCategorizer(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/textcategorizer#update
+        DOCS: https://nightly.spacy.io/api/textcategorizer#update
         """
         if losses is None:
             losses = {}
@@ -245,7 +245,7 @@ class TextCategorizer(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/textcategorizer#rehearse
+        DOCS: https://nightly.spacy.io/api/textcategorizer#rehearse
         """
         if losses is not None:
             losses.setdefault(self.name, 0.0)
@@ -289,7 +289,7 @@ class TextCategorizer(Pipe):
         scores: Scores representing the model's predictions.
         RETUTNRS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/textcategorizer#get_loss
+        DOCS: https://nightly.spacy.io/api/textcategorizer#get_loss
         """
         validate_examples(examples, "TextCategorizer.get_loss")
         truths, not_missing = self._examples_to_truth(examples)
@@ -305,7 +305,7 @@ class TextCategorizer(Pipe):
         label (str): The label to add.
         RETURNS (int): 0 if label is already present, otherwise 1.
 
-        DOCS: https://spacy.io/api/textcategorizer#add_label
+        DOCS: https://nightly.spacy.io/api/textcategorizer#add_label
         """
         if not isinstance(label, str):
             raise ValueError(Errors.E187)
@@ -343,7 +343,7 @@ class TextCategorizer(Pipe):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/textcategorizer#begin_training
+        DOCS: https://nightly.spacy.io/api/textcategorizer#begin_training
         """
         if not hasattr(get_examples, "__call__"):
             err = Errors.E930.format(name="TextCategorizer", obj=type(get_examples))
@@ -378,7 +378,7 @@ class TextCategorizer(Pipe):
         positive_label (str): Optional positive label.
         RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_cats.
 
-        DOCS: https://spacy.io/api/textcategorizer#score
+        DOCS: https://nightly.spacy.io/api/textcategorizer#score
         """
         validate_examples(examples, "TextCategorizer.score")
         return Scorer.score_cats(
diff --git a/spacy/pipeline/tok2vec.py b/spacy/pipeline/tok2vec.py
index dad66ddb3..5657d687d 100644
--- a/spacy/pipeline/tok2vec.py
+++ b/spacy/pipeline/tok2vec.py
@@ -56,7 +56,7 @@ class Tok2Vec(Pipe):
             a list of Doc objects as input, and output a list of 2d float arrays.
         name (str): The component instance name.
 
-        DOCS: https://spacy.io/api/tok2vec#init
+        DOCS: https://nightly.spacy.io/api/tok2vec#init
         """
         self.vocab = vocab
         self.model = model
@@ -88,10 +88,10 @@ class Tok2Vec(Pipe):
         """Add context-sensitive embeddings to the Doc.tensor attribute, allowing
         them to be used as features by downstream components.
 
-        docs (Doc): The Doc to preocess.
+        docs (Doc): The Doc to process.
         RETURNS (Doc): The processed Doc.
 
-        DOCS: https://spacy.io/api/tok2vec#call
+        DOCS: https://nightly.spacy.io/api/tok2vec#call
         """
         tokvecses = self.predict([doc])
         self.set_annotations([doc], tokvecses)
@@ -106,7 +106,7 @@ class Tok2Vec(Pipe):
         batch_size (int): The number of documents to buffer.
         YIELDS (Doc): Processed documents in order.
 
-        DOCS: https://spacy.io/api/tok2vec#pipe
+        DOCS: https://nightly.spacy.io/api/tok2vec#pipe
         """
         for docs in minibatch(stream, batch_size):
             docs = list(docs)
@@ -121,7 +121,7 @@ class Tok2Vec(Pipe):
         docs (Iterable[Doc]): The documents to predict.
         RETURNS: Vector representations for each token in the documents.
 
-        DOCS: https://spacy.io/api/tok2vec#predict
+        DOCS: https://nightly.spacy.io/api/tok2vec#predict
         """
         tokvecs = self.model.predict(docs)
         batch_id = Tok2VecListener.get_batch_id(docs)
@@ -135,7 +135,7 @@ class Tok2Vec(Pipe):
         docs (Iterable[Doc]): The documents to modify.
         tokvecses: The tensors to set, produced by Tok2Vec.predict.
 
-        DOCS: https://spacy.io/api/tok2vec#set_annotations
+        DOCS: https://nightly.spacy.io/api/tok2vec#set_annotations
         """
         for doc, tokvecs in zip(docs, tokvecses):
             assert tokvecs.shape[0] == len(doc)
@@ -162,7 +162,7 @@ class Tok2Vec(Pipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/tok2vec#update
+        DOCS: https://nightly.spacy.io/api/tok2vec#update
         """
         if losses is None:
             losses = {}
@@ -220,7 +220,7 @@ class Tok2Vec(Pipe):
             create_optimizer if it doesn't exist.
         RETURNS (thinc.api.Optimizer): The optimizer.
 
-        DOCS: https://spacy.io/api/tok2vec#begin_training
+        DOCS: https://nightly.spacy.io/api/tok2vec#begin_training
         """
         docs = [Doc(self.vocab, words=["hello"])]
         self.model.initialize(X=docs)
diff --git a/spacy/pipeline/transition_parser.pyx b/spacy/pipeline/transition_parser.pyx
index 2eadfa6aa..5a6b491e0 100644
--- a/spacy/pipeline/transition_parser.pyx
+++ b/spacy/pipeline/transition_parser.pyx
@@ -6,6 +6,7 @@ from itertools import islice
 from libcpp.vector cimport vector
 from libc.string cimport memset
 from libc.stdlib cimport calloc, free
+import random
 
 import srsly
 from thinc.api import set_dropout_rate
@@ -275,22 +276,22 @@ cdef class Parser(Pipe):
         # Prepare the stepwise model, and get the callback for finishing the batch
         model, backprop_tok2vec = self.model.begin_update(
             [eg.predicted for eg in examples])
-        if self.cfg["update_with_oracle_cut_size"] >= 1:
-            # Chop sequences into lengths of this many transitions, to make the
+        max_moves = self.cfg["update_with_oracle_cut_size"]
+        if max_moves >= 1:
+            # Chop sequences into lengths of this many words, to make the
             # batch uniform length.
-            # We used to randomize this, but it's not clear that actually helps?
-            cut_size = self.cfg["update_with_oracle_cut_size"]
-            states, golds, max_steps = self._init_gold_batch(
+            max_moves = int(random.uniform(max_moves // 2, max_moves * 2))
+            states, golds, _ = self._init_gold_batch(
                 examples,
-                max_length=cut_size
+                max_length=max_moves
             )
         else:
             states, golds, _ = self.moves.init_gold_batch(examples)
-            max_steps = max([len(eg.x) for eg in examples])
         if not states:
             return losses
         all_states = list(states)
         states_golds = list(zip(states, golds))
+        n_moves = 0
         while states_golds:
             states, golds = zip(*states_golds)
             scores, backprop = model.begin_update(states)
@@ -303,6 +304,9 @@ cdef class Parser(Pipe):
             # Follow the predicted action
             self.transition_states(states, scores)
             states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final()]
+            if max_moves >= 1 and n_moves >= max_moves:
+                break
+            n_moves += 1
 
         backprop_tok2vec(golds)
         if sgd not in (None, False):
@@ -498,7 +502,7 @@ cdef class Parser(Pipe):
                     raise ValueError(Errors.E149) from None
         return self
 
-    def _init_gold_batch(self, examples, min_length=5, max_length=500):
+    def _init_gold_batch(self, examples, max_length):
         """Make a square batch, of length equal to the shortest transition
         sequence or a cap. A long
         doc will get multiple states. Let's say we have a doc of length 2*N,
@@ -511,8 +515,7 @@ cdef class Parser(Pipe):
         all_states = self.moves.init_batch([eg.predicted for eg in examples])
         states = []
         golds = []
-        kept = []
-        max_length_seen = 0
+        to_cut = []
         for state, eg in zip(all_states, examples):
             if self.moves.has_gold(eg) and not state.is_final():
                 gold = self.moves.init_gold(state, eg)
@@ -522,30 +525,22 @@ cdef class Parser(Pipe):
                 else:
                     oracle_actions = self.moves.get_oracle_sequence_from_state(
                         state.copy(), gold)
-                    kept.append((eg, state, gold, oracle_actions))
-                    min_length = min(min_length, len(oracle_actions))
-                    max_length_seen = max(max_length, len(oracle_actions))
-        if not kept:
+                    to_cut.append((eg, state, gold, oracle_actions))
+        if not to_cut:
             return states, golds, 0
-        max_length = max(min_length, min(max_length, max_length_seen))
         cdef int clas
-        max_moves = 0
-        for eg, state, gold, oracle_actions in kept:
+        for eg, state, gold, oracle_actions in to_cut:
             for i in range(0, len(oracle_actions), max_length):
                 start_state = state.copy()
-                n_moves = 0
                 for clas in oracle_actions[i:i+max_length]:
                     action = self.moves.c[clas]
                     action.do(state.c, action.label)
                     state.c.push_hist(action.clas)
-                    n_moves += 1
                     if state.is_final():
                         break
-                max_moves = max(max_moves, n_moves)
                 if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
                     states.append(start_state)
                     golds.append(gold)
-                    max_moves = max(max_moves, n_moves)
                 if state.is_final():
                     break
-        return states, golds, max_moves
+        return states, golds, max_length
diff --git a/spacy/scorer.py b/spacy/scorer.py
index 9bbc64cac..9b1831a91 100644
--- a/spacy/scorer.py
+++ b/spacy/scorer.py
@@ -85,7 +85,7 @@ class Scorer:
     ) -> None:
         """Initialize the Scorer.
 
-        DOCS: https://spacy.io/api/scorer#init
+        DOCS: https://nightly.spacy.io/api/scorer#init
         """
         self.nlp = nlp
         self.cfg = cfg
@@ -101,7 +101,7 @@ class Scorer:
         examples (Iterable[Example]): The predicted annotations + correct annotations.
         RETURNS (Dict): A dictionary of scores.
 
-        DOCS: https://spacy.io/api/scorer#score
+        DOCS: https://nightly.spacy.io/api/scorer#score
         """
         scores = {}
         if hasattr(self.nlp.tokenizer, "score"):
@@ -121,7 +121,7 @@ class Scorer:
         RETURNS (Dict[str, float]): A dictionary containing the scores
             token_acc/p/r/f.
 
-        DOCS: https://spacy.io/api/scorer#score_tokenization
+        DOCS: https://nightly.spacy.io/api/scorer#score_tokenization
         """
         acc_score = PRFScore()
         prf_score = PRFScore()
@@ -169,7 +169,7 @@ class Scorer:
         RETURNS (Dict[str, float]): A dictionary containing the accuracy score
             under the key attr_acc.
 
-        DOCS: https://spacy.io/api/scorer#score_token_attr
+        DOCS: https://nightly.spacy.io/api/scorer#score_token_attr
         """
         tag_score = PRFScore()
         for example in examples:
@@ -263,7 +263,7 @@ class Scorer:
         RETURNS (Dict[str, Any]): A dictionary containing the PRF scores under
             the keys attr_p/r/f and the per-type PRF scores under attr_per_type.
 
-        DOCS: https://spacy.io/api/scorer#score_spans
+        DOCS: https://nightly.spacy.io/api/scorer#score_spans
         """
         score = PRFScore()
         score_per_type = dict()
@@ -350,7 +350,7 @@ class Scorer:
                 attr_f_per_type,
                 attr_auc_per_type
 
-        DOCS: https://spacy.io/api/scorer#score_cats
+        DOCS: https://nightly.spacy.io/api/scorer#score_cats
         """
         if threshold is None:
             threshold = 0.5 if multi_label else 0.0
@@ -467,7 +467,7 @@ class Scorer:
         RETURNS (Dict[str, Any]): A dictionary containing the scores:
             attr_uas, attr_las, and attr_las_per_type.
 
-        DOCS: https://spacy.io/api/scorer#score_deps
+        DOCS: https://nightly.spacy.io/api/scorer#score_deps
         """
         unlabelled = PRFScore()
         labelled = PRFScore()
diff --git a/spacy/strings.pxd b/spacy/strings.pxd
index ba2476ec7..07768d347 100644
--- a/spacy/strings.pxd
+++ b/spacy/strings.pxd
@@ -23,7 +23,6 @@ cdef class StringStore:
     cdef Pool mem
 
     cdef vector[hash_t] keys
-    cdef set[hash_t] hits
     cdef public PreshMap _map
 
     cdef const Utf8Str* intern_unicode(self, unicode py_string)
diff --git a/spacy/strings.pyx b/spacy/strings.pyx
index 136eda9ff..cd442729c 100644
--- a/spacy/strings.pyx
+++ b/spacy/strings.pyx
@@ -91,7 +91,7 @@ cdef Utf8Str* _allocate(Pool mem, const unsigned char* chars, uint32_t length) e
 cdef class StringStore:
     """Look up strings by 64-bit hashes.
 
-    DOCS: https://spacy.io/api/stringstore
+    DOCS: https://nightly.spacy.io/api/stringstore
     """
     def __init__(self, strings=None, freeze=False):
         """Create the StringStore.
@@ -127,7 +127,6 @@ cdef class StringStore:
             return SYMBOLS_BY_INT[string_or_id]
         else:
             key = string_or_id
-            self.hits.insert(key)
             utf8str = <Utf8Str*>self._map.get(key)
             if utf8str is NULL:
                 raise KeyError(Errors.E018.format(hash_value=string_or_id))
@@ -198,7 +197,6 @@ cdef class StringStore:
         if key < len(SYMBOLS_BY_INT):
             return True
         else:
-            self.hits.insert(key)
             return self._map.get(key) is not NULL
 
     def __iter__(self):
@@ -210,7 +208,6 @@ cdef class StringStore:
         cdef hash_t key
         for i in range(self.keys.size()):
             key = self.keys[i]
-            self.hits.insert(key)
             utf8str = <Utf8Str*>self._map.get(key)
             yield decode_Utf8Str(utf8str)
         # TODO: Iterate OOV here?
@@ -269,41 +266,9 @@ cdef class StringStore:
         self.mem = Pool()
         self._map = PreshMap()
         self.keys.clear()
-        self.hits.clear()
         for string in strings:
             self.add(string)
 
-    def _cleanup_stale_strings(self, excepted):
-        """
-        excepted (list): Strings that should not be removed.
-        RETURNS (keys, strings): Dropped strings and keys that can be dropped from other places
-        """
-        if self.hits.size() == 0:
-            # If we don't have any hits, just skip cleanup
-            return
-
-        cdef vector[hash_t] tmp
-        dropped_strings = []
-        dropped_keys = []
-        for i in range(self.keys.size()):
-            key = self.keys[i]
-            # Here we cannot use __getitem__ because it also set hit.
-            utf8str = <Utf8Str*>self._map.get(key)
-            value = decode_Utf8Str(utf8str)
-            if self.hits.count(key) != 0 or value in excepted:
-                tmp.push_back(key)
-            else:
-                dropped_keys.append(key)
-                dropped_strings.append(value)
-
-        self.keys.swap(tmp)
-        strings = list(self)
-        self._reset_and_load(strings)
-        # Here we have strings but hits to it should be reseted
-        self.hits.clear()
-
-        return dropped_keys, dropped_strings
-
     cdef const Utf8Str* intern_unicode(self, unicode py_string):
         # 0 means missing, but we don't bother offsetting the index.
         cdef bytes byte_string = py_string.encode("utf8")
@@ -319,6 +284,5 @@ cdef class StringStore:
             return value
         value = _allocate(self.mem, <unsigned char*>utf8_string, length)
         self._map.set(key, value)
-        self.hits.insert(key)
         self.keys.push_back(key)
         return value
diff --git a/spacy/tests/doc/test_doc_api.py b/spacy/tests/doc/test_doc_api.py
index 954181df5..b37a31e43 100644
--- a/spacy/tests/doc/test_doc_api.py
+++ b/spacy/tests/doc/test_doc_api.py
@@ -317,7 +317,8 @@ def test_doc_from_array_morph(en_vocab):
 
 
 def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
-    en_texts = ["Merging the docs is fun.", "They don't think alike."]
+    en_texts = ["Merging the docs is fun.", "", "They don't think alike."]
+    en_texts_without_empty = [t for t in en_texts if len(t)]
     de_text = "Wie war die Frage?"
     en_docs = [en_tokenizer(text) for text in en_texts]
     docs_idx = en_texts[0].index("docs")
@@ -338,14 +339,14 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
         Doc.from_docs(en_docs + [de_doc])
 
     m_doc = Doc.from_docs(en_docs)
-    assert len(en_docs) == len(list(m_doc.sents))
+    assert len(en_texts_without_empty) == len(list(m_doc.sents))
     assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
-    assert str(m_doc) == " ".join(en_texts)
+    assert str(m_doc) == " ".join(en_texts_without_empty)
     p_token = m_doc[len(en_docs[0]) - 1]
     assert p_token.text == "." and bool(p_token.whitespace_)
     en_docs_tokens = [t for doc in en_docs for t in doc]
     assert len(m_doc) == len(en_docs_tokens)
-    think_idx = len(en_texts[0]) + 1 + en_texts[1].index("think")
+    think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
     assert m_doc[9].idx == think_idx
     with pytest.raises(AttributeError):
         # not callable, because it was not set via set_extension
@@ -353,14 +354,14 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
     assert len(m_doc.user_data) == len(en_docs[0].user_data)  # but it's there
 
     m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
-    assert len(en_docs) == len(list(m_doc.sents))
-    assert len(str(m_doc)) == len(en_texts[0]) + len(en_texts[1])
+    assert len(en_texts_without_empty) == len(list(m_doc.sents))
+    assert len(str(m_doc)) == sum(len(t) for t in en_texts)
     assert str(m_doc) == "".join(en_texts)
     p_token = m_doc[len(en_docs[0]) - 1]
     assert p_token.text == "." and not bool(p_token.whitespace_)
     en_docs_tokens = [t for doc in en_docs for t in doc]
     assert len(m_doc) == len(en_docs_tokens)
-    think_idx = len(en_texts[0]) + 0 + en_texts[1].index("think")
+    think_idx = len(en_texts[0]) + 0 + en_texts[2].index("think")
     assert m_doc[9].idx == think_idx
 
     m_doc = Doc.from_docs(en_docs, attrs=["lemma", "length", "pos"])
@@ -369,12 +370,12 @@ def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
         assert list(m_doc.sents)
     assert len(str(m_doc)) > len(en_texts[0]) + len(en_texts[1])
     # space delimiter considered, although spacy attribute was missing
-    assert str(m_doc) == " ".join(en_texts)
+    assert str(m_doc) == " ".join(en_texts_without_empty)
     p_token = m_doc[len(en_docs[0]) - 1]
     assert p_token.text == "." and bool(p_token.whitespace_)
     en_docs_tokens = [t for doc in en_docs for t in doc]
     assert len(m_doc) == len(en_docs_tokens)
-    think_idx = len(en_texts[0]) + 1 + en_texts[1].index("think")
+    think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
     assert m_doc[9].idx == think_idx
 
 
diff --git a/spacy/tests/lang/test_lemmatizers.py b/spacy/tests/lang/test_lemmatizers.py
index 8c235c86e..14c59659a 100644
--- a/spacy/tests/lang/test_lemmatizers.py
+++ b/spacy/tests/lang/test_lemmatizers.py
@@ -14,7 +14,7 @@ LANGUAGES = ["el", "en", "fr", "nl"]
 
 @pytest.mark.parametrize("lang", LANGUAGES)
 def test_lemmatizer_initialize(lang, capfd):
-    @registry.assets("lemmatizer_init_lookups")
+    @registry.misc("lemmatizer_init_lookups")
     def lemmatizer_init_lookups():
         lookups = Lookups()
         lookups.add_table("lemma_lookup", {"cope": "cope"})
@@ -25,9 +25,7 @@ def test_lemmatizer_initialize(lang, capfd):
 
     """Test that languages can be initialized."""
     nlp = get_lang_class(lang)()
-    nlp.add_pipe(
-        "lemmatizer", config={"lookups": {"@assets": "lemmatizer_init_lookups"}}
-    )
+    nlp.add_pipe("lemmatizer", config={"lookups": {"@misc": "lemmatizer_init_lookups"}})
     # Check for stray print statements (see #3342)
     doc = nlp("test")  # noqa: F841
     captured = capfd.readouterr()
diff --git a/spacy/tests/matcher/test_matcher_api.py b/spacy/tests/matcher/test_matcher_api.py
index bcb224bd3..8310c4466 100644
--- a/spacy/tests/matcher/test_matcher_api.py
+++ b/spacy/tests/matcher/test_matcher_api.py
@@ -2,7 +2,8 @@ import pytest
 import re
 from mock import Mock
 from spacy.matcher import Matcher, DependencyMatcher
-from spacy.tokens import Doc, Token
+from spacy.tokens import Doc, Token, Span
+
 from ..doc.test_underscore import clean_underscore  # noqa: F401
 
 
@@ -469,3 +470,26 @@ def test_matcher_span(matcher):
     assert len(matcher(doc)) == 2
     assert len(matcher(span_js)) == 1
     assert len(matcher(span_java)) == 1
+
+
+def test_matcher_as_spans(matcher):
+    """Test the new as_spans=True API."""
+    text = "JavaScript is good but Java is better"
+    doc = Doc(matcher.vocab, words=text.split())
+    matches = matcher(doc, as_spans=True)
+    assert len(matches) == 2
+    assert isinstance(matches[0], Span)
+    assert matches[0].text == "JavaScript"
+    assert matches[0].label_ == "JS"
+    assert isinstance(matches[1], Span)
+    assert matches[1].text == "Java"
+    assert matches[1].label_ == "Java"
+
+
+def test_matcher_deprecated(matcher):
+    doc = Doc(matcher.vocab, words=["hello", "world"])
+    with pytest.warns(DeprecationWarning) as record:
+        for _ in matcher.pipe([doc]):
+            pass
+        assert record.list
+        assert "spaCy v3.0" in str(record.list[0].message)
diff --git a/spacy/tests/matcher/test_phrase_matcher.py b/spacy/tests/matcher/test_phrase_matcher.py
index 2a3c7d693..4b7027f87 100644
--- a/spacy/tests/matcher/test_phrase_matcher.py
+++ b/spacy/tests/matcher/test_phrase_matcher.py
@@ -2,7 +2,7 @@ import pytest
 import srsly
 from mock import Mock
 from spacy.matcher import PhraseMatcher
-from spacy.tokens import Doc
+from spacy.tokens import Doc, Span
 from ..util import get_doc
 
 
@@ -287,3 +287,30 @@ def test_phrase_matcher_pickle(en_vocab):
     # clunky way to vaguely check that callback is unpickled
     (vocab, docs, callbacks, attr) = matcher_unpickled.__reduce__()[1]
     assert isinstance(callbacks.get("TEST2"), Mock)
+
+
+def test_phrase_matcher_as_spans(en_vocab):
+    """Test the new as_spans=True API."""
+    matcher = PhraseMatcher(en_vocab)
+    matcher.add("A", [Doc(en_vocab, words=["hello", "world"])])
+    matcher.add("B", [Doc(en_vocab, words=["test"])])
+    doc = Doc(en_vocab, words=["...", "hello", "world", "this", "is", "a", "test"])
+    matches = matcher(doc, as_spans=True)
+    assert len(matches) == 2
+    assert isinstance(matches[0], Span)
+    assert matches[0].text == "hello world"
+    assert matches[0].label_ == "A"
+    assert isinstance(matches[1], Span)
+    assert matches[1].text == "test"
+    assert matches[1].label_ == "B"
+
+
+def test_phrase_matcher_deprecated(en_vocab):
+    matcher = PhraseMatcher(en_vocab)
+    matcher.add("TEST", [Doc(en_vocab, words=["helllo"])])
+    doc = Doc(en_vocab, words=["hello", "world"])
+    with pytest.warns(DeprecationWarning) as record:
+        for _ in matcher.pipe([doc]):
+            pass
+        assert record.list
+        assert "spaCy v3.0" in str(record.list[0].message)
diff --git a/spacy/tests/pipeline/test_attributeruler.py b/spacy/tests/pipeline/test_attributeruler.py
index 96361a693..c12a2b650 100644
--- a/spacy/tests/pipeline/test_attributeruler.py
+++ b/spacy/tests/pipeline/test_attributeruler.py
@@ -31,7 +31,7 @@ def pattern_dicts():
     ]
 
 
-@registry.assets("attribute_ruler_patterns")
+@registry.misc("attribute_ruler_patterns")
 def attribute_ruler_patterns():
     return [
         {
@@ -86,7 +86,7 @@ def test_attributeruler_init_patterns(nlp, pattern_dicts):
     # initialize with patterns from asset
     nlp.add_pipe(
         "attribute_ruler",
-        config={"pattern_dicts": {"@assets": "attribute_ruler_patterns"}},
+        config={"pattern_dicts": {"@misc": "attribute_ruler_patterns"}},
     )
     doc = nlp("This is a test.")
     assert doc[2].lemma_ == "the"
diff --git a/spacy/tests/pipeline/test_entity_linker.py b/spacy/tests/pipeline/test_entity_linker.py
index 4385d2bf9..4eaa71272 100644
--- a/spacy/tests/pipeline/test_entity_linker.py
+++ b/spacy/tests/pipeline/test_entity_linker.py
@@ -137,7 +137,7 @@ def test_kb_undefined(nlp):
 
 def test_kb_empty(nlp):
     """Test that the EL can't train with an empty KB"""
-    config = {"kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 342}}
+    config = {"kb_loader": {"@misc": "spacy.EmptyKB.v1", "entity_vector_length": 342}}
     entity_linker = nlp.add_pipe("entity_linker", config=config)
     assert len(entity_linker.kb) == 0
     with pytest.raises(ValueError):
@@ -183,7 +183,7 @@ def test_el_pipe_configuration(nlp):
     ruler = nlp.add_pipe("entity_ruler")
     ruler.add_patterns([pattern])
 
-    @registry.assets.register("myAdamKB.v1")
+    @registry.misc.register("myAdamKB.v1")
     def mykb() -> Callable[["Vocab"], KnowledgeBase]:
         def create_kb(vocab):
             kb = KnowledgeBase(vocab, entity_vector_length=1)
@@ -199,7 +199,7 @@ def test_el_pipe_configuration(nlp):
     # run an EL pipe without a trained context encoder, to check the candidate generation step only
     nlp.add_pipe(
         "entity_linker",
-        config={"kb_loader": {"@assets": "myAdamKB.v1"}, "incl_context": False},
+        config={"kb_loader": {"@misc": "myAdamKB.v1"}, "incl_context": False},
     )
     # With the default get_candidates function, matching is case-sensitive
     text = "Douglas and douglas are not the same."
@@ -211,7 +211,7 @@ def test_el_pipe_configuration(nlp):
     def get_lowercased_candidates(kb, span):
         return kb.get_alias_candidates(span.text.lower())
 
-    @registry.assets.register("spacy.LowercaseCandidateGenerator.v1")
+    @registry.misc.register("spacy.LowercaseCandidateGenerator.v1")
     def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
         return get_lowercased_candidates
 
@@ -220,9 +220,9 @@ def test_el_pipe_configuration(nlp):
         "entity_linker",
         "entity_linker",
         config={
-            "kb_loader": {"@assets": "myAdamKB.v1"},
+            "kb_loader": {"@misc": "myAdamKB.v1"},
             "incl_context": False,
-            "get_candidates": {"@assets": "spacy.LowercaseCandidateGenerator.v1"},
+            "get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
         },
     )
     doc = nlp(text)
@@ -282,7 +282,7 @@ def test_append_invalid_alias(nlp):
 def test_preserving_links_asdoc(nlp):
     """Test that Span.as_doc preserves the existing entity links"""
 
-    @registry.assets.register("myLocationsKB.v1")
+    @registry.misc.register("myLocationsKB.v1")
     def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
         def create_kb(vocab):
             mykb = KnowledgeBase(vocab, entity_vector_length=1)
@@ -304,7 +304,7 @@ def test_preserving_links_asdoc(nlp):
     ]
     ruler = nlp.add_pipe("entity_ruler")
     ruler.add_patterns(patterns)
-    el_config = {"kb_loader": {"@assets": "myLocationsKB.v1"}, "incl_prior": False}
+    el_config = {"kb_loader": {"@misc": "myLocationsKB.v1"}, "incl_prior": False}
     el_pipe = nlp.add_pipe("entity_linker", config=el_config, last=True)
     el_pipe.begin_training(lambda: [])
     el_pipe.incl_context = False
@@ -387,7 +387,7 @@ def test_overfitting_IO():
         doc = nlp(text)
         train_examples.append(Example.from_dict(doc, annotation))
 
-    @registry.assets.register("myOverfittingKB.v1")
+    @registry.misc.register("myOverfittingKB.v1")
     def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
         def create_kb(vocab):
             # create artificial KB - assign same prior weight to the two russ cochran's
@@ -408,7 +408,7 @@ def test_overfitting_IO():
     # Create the Entity Linker component and add it to the pipeline
     nlp.add_pipe(
         "entity_linker",
-        config={"kb_loader": {"@assets": "myOverfittingKB.v1"}},
+        config={"kb_loader": {"@misc": "myOverfittingKB.v1"}},
         last=True,
     )
 
diff --git a/spacy/tests/pipeline/test_lemmatizer.py b/spacy/tests/pipeline/test_lemmatizer.py
index 8a70fdeeb..05e15bc16 100644
--- a/spacy/tests/pipeline/test_lemmatizer.py
+++ b/spacy/tests/pipeline/test_lemmatizer.py
@@ -13,7 +13,7 @@ def nlp():
 
 @pytest.fixture
 def lemmatizer(nlp):
-    @registry.assets("cope_lookups")
+    @registry.misc("cope_lookups")
     def cope_lookups():
         lookups = Lookups()
         lookups.add_table("lemma_lookup", {"cope": "cope"})
@@ -23,13 +23,13 @@ def lemmatizer(nlp):
         return lookups
 
     lemmatizer = nlp.add_pipe(
-        "lemmatizer", config={"mode": "rule", "lookups": {"@assets": "cope_lookups"}}
+        "lemmatizer", config={"mode": "rule", "lookups": {"@misc": "cope_lookups"}}
     )
     return lemmatizer
 
 
 def test_lemmatizer_init(nlp):
-    @registry.assets("cope_lookups")
+    @registry.misc("cope_lookups")
     def cope_lookups():
         lookups = Lookups()
         lookups.add_table("lemma_lookup", {"cope": "cope"})
@@ -39,7 +39,7 @@ def test_lemmatizer_init(nlp):
         return lookups
 
     lemmatizer = nlp.add_pipe(
-        "lemmatizer", config={"mode": "lookup", "lookups": {"@assets": "cope_lookups"}}
+        "lemmatizer", config={"mode": "lookup", "lookups": {"@misc": "cope_lookups"}}
     )
     assert isinstance(lemmatizer.lookups, Lookups)
     assert lemmatizer.mode == "lookup"
@@ -51,14 +51,14 @@ def test_lemmatizer_init(nlp):
 
     nlp.remove_pipe("lemmatizer")
 
-    @registry.assets("empty_lookups")
+    @registry.misc("empty_lookups")
     def empty_lookups():
         return Lookups()
 
     with pytest.raises(ValueError):
         nlp.add_pipe(
             "lemmatizer",
-            config={"mode": "lookup", "lookups": {"@assets": "empty_lookups"}},
+            config={"mode": "lookup", "lookups": {"@misc": "empty_lookups"}},
         )
 
 
@@ -79,7 +79,7 @@ def test_lemmatizer_config(nlp, lemmatizer):
 
 
 def test_lemmatizer_serialize(nlp, lemmatizer):
-    @registry.assets("cope_lookups")
+    @registry.misc("cope_lookups")
     def cope_lookups():
         lookups = Lookups()
         lookups.add_table("lemma_lookup", {"cope": "cope"})
@@ -90,7 +90,7 @@ def test_lemmatizer_serialize(nlp, lemmatizer):
 
     nlp2 = English()
     lemmatizer2 = nlp2.add_pipe(
-        "lemmatizer", config={"mode": "rule", "lookups": {"@assets": "cope_lookups"}}
+        "lemmatizer", config={"mode": "rule", "lookups": {"@misc": "cope_lookups"}}
     )
     lemmatizer2.from_bytes(lemmatizer.to_bytes())
     assert lemmatizer.to_bytes() == lemmatizer2.to_bytes()
diff --git a/spacy/tests/pipeline/test_tagger.py b/spacy/tests/pipeline/test_tagger.py
index 1af4a5121..a1aa7e1e1 100644
--- a/spacy/tests/pipeline/test_tagger.py
+++ b/spacy/tests/pipeline/test_tagger.py
@@ -28,8 +28,6 @@ def test_tagger_begin_training_tag_map():
 
 TAGS = ("N", "V", "J")
 
-MORPH_RULES = {"V": {"like": {"lemma": "luck"}}}
-
 TRAIN_DATA = [
     ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
     ("Eat blue ham", {"tags": ["V", "J", "N"]}),
@@ -69,3 +67,10 @@ def test_overfitting_IO():
         assert doc2[1].tag_ is "V"
         assert doc2[2].tag_ is "J"
         assert doc2[3].tag_ is "N"
+
+
+def test_tagger_requires_labels():
+    nlp = English()
+    tagger = nlp.add_pipe("tagger")
+    with pytest.raises(ValueError):
+        optimizer = nlp.begin_training()
diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py
index 66c27b233..12ead90cb 100644
--- a/spacy/tests/pipeline/test_textcat.py
+++ b/spacy/tests/pipeline/test_textcat.py
@@ -84,9 +84,8 @@ def test_overfitting_IO():
     # Simple test to try and quickly overfit the textcat component - ensuring the ML models work correctly
     fix_random_seed(0)
     nlp = English()
-    textcat = nlp.add_pipe("textcat")
     # Set exclusive labels
-    textcat.model.attrs["multi_label"] = False
+    textcat = nlp.add_pipe("textcat", config={"model": {"exclusive_classes": True}})
     train_examples = []
     for text, annotations in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
@@ -103,9 +102,8 @@ def test_overfitting_IO():
     test_text = "I am happy."
     doc = nlp(test_text)
     cats = doc.cats
-    # note that by default, exclusive_classes = false so we need a bigger error margin
-    assert cats["POSITIVE"] > 0.8
-    assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.1)
+    assert cats["POSITIVE"] > 0.9
+    assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.001)
 
     # Also test the results are still the same after IO
     with make_tempdir() as tmp_dir:
@@ -113,8 +111,8 @@ def test_overfitting_IO():
         nlp2 = util.load_model_from_path(tmp_dir)
         doc2 = nlp2(test_text)
         cats2 = doc2.cats
-        assert cats2["POSITIVE"] > 0.8
-        assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.1)
+        assert cats2["POSITIVE"] > 0.9
+        assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.001)
 
     # Test scoring
     scores = nlp.evaluate(train_examples, scorer_cfg={"positive_label": "POSITIVE"})
diff --git a/spacy/tests/regression/test_issue4001-4500.py b/spacy/tests/regression/test_issue4001-4500.py
index 1789973e9..e846841d4 100644
--- a/spacy/tests/regression/test_issue4001-4500.py
+++ b/spacy/tests/regression/test_issue4001-4500.py
@@ -326,7 +326,8 @@ def test_issue4348():
     nlp = English()
     example = Example.from_dict(nlp.make_doc(""), {"tags": []})
     TRAIN_DATA = [example, example]
-    nlp.add_pipe("tagger")
+    tagger = nlp.add_pipe("tagger")
+    tagger.add_label("A")
     optimizer = nlp.begin_training()
     for i in range(5):
         losses = {}
diff --git a/spacy/tests/regression/test_issue5230.py b/spacy/tests/regression/test_issue5230.py
index 2ac886625..af643aadc 100644
--- a/spacy/tests/regression/test_issue5230.py
+++ b/spacy/tests/regression/test_issue5230.py
@@ -63,6 +63,7 @@ def tagger():
     # need to add model for two reasons:
     # 1. no model leads to error in serialization,
     # 2. the affected line is the one for model serialization
+    tagger.add_label("A")
     tagger.begin_training(lambda: [], pipeline=nlp.pipeline)
     return tagger
 
@@ -70,7 +71,7 @@ def tagger():
 def entity_linker():
     nlp = Language()
 
-    @registry.assets.register("TestIssue5230KB.v1")
+    @registry.misc.register("TestIssue5230KB.v1")
     def dummy_kb() -> Callable[["Vocab"], KnowledgeBase]:
         def create_kb(vocab):
             kb = KnowledgeBase(vocab, entity_vector_length=1)
@@ -79,7 +80,7 @@ def entity_linker():
 
         return create_kb
 
-    config = {"kb_loader": {"@assets": "TestIssue5230KB.v1"}}
+    config = {"kb_loader": {"@misc": "TestIssue5230KB.v1"}}
     entity_linker = nlp.add_pipe("entity_linker", config=config)
     # need to add model for two reasons:
     # 1. no model leads to error in serialization,
diff --git a/spacy/tests/serialize/test_serialize_config.py b/spacy/tests/serialize/test_serialize_config.py
index e425d370d..0ab212fda 100644
--- a/spacy/tests/serialize/test_serialize_config.py
+++ b/spacy/tests/serialize/test_serialize_config.py
@@ -28,7 +28,7 @@ path = ${paths.train}
 path = ${paths.dev}
 
 [training.batcher]
-@batchers = "batch_by_words.v1"
+@batchers = "spacy.batch_by_words.v1"
 size = 666
 
 [nlp]
@@ -144,6 +144,7 @@ def test_serialize_nlp():
     """ Create a custom nlp pipeline from config and ensure it serializes it correctly """
     nlp_config = Config().from_str(nlp_config_string)
     nlp, _ = load_model_from_config(nlp_config, auto_fill=True)
+    nlp.get_pipe("tagger").add_label("A")
     nlp.begin_training()
     assert "tok2vec" in nlp.pipe_names
     assert "tagger" in nlp.pipe_names
diff --git a/spacy/tests/serialize/test_serialize_kb.py b/spacy/tests/serialize/test_serialize_kb.py
index 3cf5485d7..63736418b 100644
--- a/spacy/tests/serialize/test_serialize_kb.py
+++ b/spacy/tests/serialize/test_serialize_kb.py
@@ -85,7 +85,7 @@ def test_serialize_subclassed_kb():
             super().__init__(vocab, entity_vector_length)
             self.custom_field = custom_field
 
-    @registry.assets.register("spacy.CustomKB.v1")
+    @registry.misc.register("spacy.CustomKB.v1")
     def custom_kb(
         entity_vector_length: int, custom_field: int
     ) -> Callable[["Vocab"], KnowledgeBase]:
@@ -101,7 +101,7 @@ def test_serialize_subclassed_kb():
     nlp = English()
     config = {
         "kb_loader": {
-            "@assets": "spacy.CustomKB.v1",
+            "@misc": "spacy.CustomKB.v1",
             "entity_vector_length": 342,
             "custom_field": 666,
         }
diff --git a/spacy/tests/test_tok2vec.py b/spacy/tests/test_tok2vec.py
index b30705088..1068b662d 100644
--- a/spacy/tests/test_tok2vec.py
+++ b/spacy/tests/test_tok2vec.py
@@ -3,11 +3,18 @@ import pytest
 from spacy.ml.models.tok2vec import build_Tok2Vec_model
 from spacy.ml.models.tok2vec import MultiHashEmbed, CharacterEmbed
 from spacy.ml.models.tok2vec import MishWindowEncoder, MaxoutWindowEncoder
+from spacy.pipeline.tok2vec import Tok2Vec, Tok2VecListener
 from spacy.vocab import Vocab
 from spacy.tokens import Doc
-
+from spacy.gold import Example
+from spacy import util
+from spacy.lang.en import English
 from .util import get_batch
 
+from thinc.api import Config
+
+from numpy.testing import assert_equal
+
 
 def test_empty_doc():
     width = 128
@@ -41,7 +48,7 @@ def test_tok2vec_batch_sizes(batch_size, width, embed_size):
             also_use_static_vectors=False,
             also_embed_subwords=True,
         ),
-        MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3,),
+        MaxoutWindowEncoder(width=width, depth=4, window_size=1, maxout_pieces=3),
     )
     tok2vec.initialize()
     vectors, backprop = tok2vec.begin_update(batch)
@@ -74,3 +81,89 @@ def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_co
     assert len(vectors) == len(docs)
     assert vectors[0].shape == (len(docs[0]), width)
     backprop(vectors)
+
+
+def test_init_tok2vec():
+    # Simple test to initialize the default tok2vec
+    nlp = English()
+    tok2vec = nlp.add_pipe("tok2vec")
+    assert tok2vec.listeners == []
+    nlp.begin_training()
+
+
+cfg_string = """
+    [nlp]
+    lang = "en"
+    pipeline = ["tok2vec","tagger"]
+
+    [components]
+
+    [components.tagger]
+    factory = "tagger"
+
+    [components.tagger.model]
+    @architectures = "spacy.Tagger.v1"
+    nO = null
+
+    [components.tagger.model.tok2vec]
+    @architectures = "spacy.Tok2VecListener.v1"
+    width = ${components.tok2vec.model.encode.width}
+
+    [components.tok2vec]
+    factory = "tok2vec"
+
+    [components.tok2vec.model]
+    @architectures = "spacy.Tok2Vec.v1"
+
+    [components.tok2vec.model.embed]
+    @architectures = "spacy.MultiHashEmbed.v1"
+    width = ${components.tok2vec.model.encode.width}
+    rows = 2000
+    also_embed_subwords = true
+    also_use_static_vectors = false
+
+    [components.tok2vec.model.encode]
+    @architectures = "spacy.MaxoutWindowEncoder.v1"
+    width = 96
+    depth = 4
+    window_size = 1
+    maxout_pieces = 3
+    """
+
+TRAIN_DATA = [
+    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
+    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
+]
+
+def test_tok2vec_listener():
+    orig_config = Config().from_str(cfg_string)
+    nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
+    assert nlp.pipe_names == ["tok2vec", "tagger"]
+    tagger = nlp.get_pipe("tagger")
+    tok2vec = nlp.get_pipe("tok2vec")
+    tagger_tok2vec = tagger.model.get_ref("tok2vec")
+    assert isinstance(tok2vec, Tok2Vec)
+    assert isinstance(tagger_tok2vec, Tok2VecListener)
+    train_examples = []
+    for t in TRAIN_DATA:
+        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+        for tag in t[1]["tags"]:
+            tagger.add_label(tag)
+
+    # Check that the Tok2Vec component finds it listeners
+    assert tok2vec.listeners == []
+    optimizer = nlp.begin_training(lambda: train_examples)
+    assert tok2vec.listeners == [tagger_tok2vec]
+
+    for i in range(5):
+        losses = {}
+        nlp.update(train_examples, sgd=optimizer, losses=losses)
+
+    doc = nlp("Running the pipeline as a whole.")
+    doc_tensor = tagger_tok2vec.predict([doc])[0]
+    assert_equal(doc.tensor, doc_tensor)
+
+    # TODO: should this warn or error?
+    nlp.select_pipes(disable="tok2vec")
+    assert nlp.pipe_names == ["tagger"]
+    nlp("Running the pipeline with the Tok2Vec component disabled.")
diff --git a/spacy/tests/tokenizer/test_tokenizer.py b/spacy/tests/tokenizer/test_tokenizer.py
index b89c0627f..ff31ae8a9 100644
--- a/spacy/tests/tokenizer/test_tokenizer.py
+++ b/spacy/tests/tokenizer/test_tokenizer.py
@@ -105,7 +105,13 @@ def test_tokenizer_add_special_case(tokenizer, text, tokens):
     assert doc[1].text == tokens[1]["orth"]
 
 
-@pytest.mark.parametrize("text,tokens", [("lorem", [{"orth": "lo"}, {"orth": "re"}])])
+@pytest.mark.parametrize(
+    "text,tokens",
+    [
+        ("lorem", [{"orth": "lo"}, {"orth": "re"}]),
+        ("lorem", [{"orth": "lo", "tag": "A"}, {"orth": "rem"}]),
+    ],
+)
 def test_tokenizer_validate_special_case(tokenizer, text, tokens):
     with pytest.raises(ValueError):
         tokenizer.add_special_case(text, tokens)
diff --git a/spacy/tokenizer.pxd b/spacy/tokenizer.pxd
index 828f4550b..9c1398a17 100644
--- a/spacy/tokenizer.pxd
+++ b/spacy/tokenizer.pxd
@@ -34,9 +34,9 @@ cdef class Tokenizer:
                                        vector[SpanC] &filtered)
     cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens,
                                        object span_data)
-    cdef int _try_cache(self, hash_t key, Doc tokens) except -1
-    cdef int _try_specials(self, hash_t key, Doc tokens,
-                           int* has_special) except -1
+    cdef int _try_specials_and_cache(self, hash_t key, Doc tokens,
+                                     int* has_special,
+                                     bint with_special_cases) except -1
     cdef int _tokenize(self, Doc tokens, unicode span, hash_t key,
                        int* has_special, bint with_special_cases) except -1
     cdef unicode _split_affixes(self, Pool mem, unicode string,
diff --git a/spacy/tokenizer.pyx b/spacy/tokenizer.pyx
index 9fda1800b..5e7222d40 100644
--- a/spacy/tokenizer.pyx
+++ b/spacy/tokenizer.pyx
@@ -17,7 +17,7 @@ from .strings cimport hash_string
 from .lexeme cimport EMPTY_LEXEME
 
 from .attrs import intify_attrs
-from .symbols import ORTH
+from .symbols import ORTH, NORM
 from .errors import Errors, Warnings
 from . import util
 from .util import registry
@@ -31,7 +31,7 @@ cdef class Tokenizer:
     """Segment text, and create Doc objects with the discovered segment
     boundaries.
 
-    DOCS: https://spacy.io/api/tokenizer
+    DOCS: https://nightly.spacy.io/api/tokenizer
     """
     def __init__(self, Vocab vocab, rules=None, prefix_search=None,
                  suffix_search=None, infix_finditer=None, token_match=None,
@@ -54,7 +54,7 @@ cdef class Tokenizer:
         EXAMPLE:
             >>> tokenizer = Tokenizer(nlp.vocab)
 
-        DOCS: https://spacy.io/api/tokenizer#init
+        DOCS: https://nightly.spacy.io/api/tokenizer#init
         """
         self.mem = Pool()
         self._cache = PreshMap()
@@ -147,7 +147,7 @@ cdef class Tokenizer:
         string (str): The string to tokenize.
         RETURNS (Doc): A container for linguistic annotations.
 
-        DOCS: https://spacy.io/api/tokenizer#call
+        DOCS: https://nightly.spacy.io/api/tokenizer#call
         """
         doc = self._tokenize_affixes(string, True)
         self._apply_special_cases(doc)
@@ -169,8 +169,6 @@ cdef class Tokenizer:
         cdef int i = 0
         cdef int start = 0
         cdef int has_special = 0
-        cdef bint specials_hit = 0
-        cdef bint cache_hit = 0
         cdef bint in_ws = string[0].isspace()
         cdef unicode span
         # The task here is much like string.split, but not quite
@@ -186,13 +184,7 @@ cdef class Tokenizer:
                     # we don't have to create the slice when we hit the cache.
                     span = string[start:i]
                     key = hash_string(span)
-                    specials_hit = 0
-                    cache_hit = 0
-                    if with_special_cases:
-                        specials_hit = self._try_specials(key, doc, &has_special)
-                    if not specials_hit:
-                        cache_hit = self._try_cache(key, doc)
-                    if not specials_hit and not cache_hit:
+                    if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases):
                         self._tokenize(doc, span, key, &has_special, with_special_cases)
                 if uc == ' ':
                     doc.c[doc.length - 1].spacy = True
@@ -204,13 +196,7 @@ cdef class Tokenizer:
         if start < i:
             span = string[start:]
             key = hash_string(span)
-            specials_hit = 0
-            cache_hit = 0
-            if with_special_cases:
-                specials_hit = self._try_specials(key, doc, &has_special)
-            if not specials_hit:
-                cache_hit = self._try_cache(key, doc)
-            if not specials_hit and not cache_hit:
+            if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases):
                 self._tokenize(doc, span, key, &has_special, with_special_cases)
             doc.c[doc.length - 1].spacy = string[-1] == " " and not in_ws
         return doc
@@ -223,7 +209,7 @@ cdef class Tokenizer:
         Defaults to 1000.
         YIELDS (Doc): A sequence of Doc objects, in order.
 
-        DOCS: https://spacy.io/api/tokenizer#pipe
+        DOCS: https://nightly.spacy.io/api/tokenizer#pipe
         """
         for text in texts:
             yield self(text)
@@ -364,27 +350,33 @@ cdef class Tokenizer:
                     offset += span[3]
         return offset
 
-    cdef int _try_cache(self, hash_t key, Doc tokens) except -1:
-        cached = <_Cached*>self._cache.get(key)
-        if cached == NULL:
-            return False
+    cdef int _try_specials_and_cache(self, hash_t key, Doc tokens, int* has_special, bint with_special_cases) except -1:
+        cdef bint specials_hit = 0
+        cdef bint cache_hit = 0
         cdef int i
-        if cached.is_lex:
-            for i in range(cached.length):
-                tokens.push_back(cached.data.lexemes[i], False)
-        else:
-            for i in range(cached.length):
-                tokens.push_back(&cached.data.tokens[i], False)
-        return True
-
-    cdef int _try_specials(self, hash_t key, Doc tokens, int* has_special) except -1:
-        cached = <_Cached*>self._specials.get(key)
-        if cached == NULL:
+        if with_special_cases:
+            cached = <_Cached*>self._specials.get(key)
+            if cached == NULL:
+                specials_hit = False
+            else:
+                for i in range(cached.length):
+                    tokens.push_back(&cached.data.tokens[i], False)
+                has_special[0] = 1
+                specials_hit = True
+        if not specials_hit:
+            cached = <_Cached*>self._cache.get(key)
+            if cached == NULL:
+                cache_hit = False
+            else:
+                if cached.is_lex:
+                    for i in range(cached.length):
+                        tokens.push_back(cached.data.lexemes[i], False)
+                else:
+                    for i in range(cached.length):
+                        tokens.push_back(&cached.data.tokens[i], False)
+                cache_hit = True
+        if not specials_hit and not cache_hit:
             return False
-        cdef int i
-        for i in range(cached.length):
-            tokens.push_back(&cached.data.tokens[i], False)
-        has_special[0] = 1
         return True
 
     cdef int _tokenize(self, Doc tokens, unicode span, hash_t orig_key, int* has_special, bint with_special_cases) except -1:
@@ -462,12 +454,7 @@ cdef class Tokenizer:
             for i in range(prefixes.size()):
                 tokens.push_back(prefixes[0][i], False)
         if string:
-            if with_special_cases:
-                specials_hit = self._try_specials(hash_string(string), tokens,
-                                                  has_special)
-            if not specials_hit:
-                cache_hit = self._try_cache(hash_string(string), tokens)
-            if specials_hit or cache_hit:
+            if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases):
                 pass
             elif (self.token_match and self.token_match(string)) or \
                     (self.url_match and \
@@ -542,7 +529,7 @@ cdef class Tokenizer:
             and `.end()` methods, denoting the placement of internal segment
             separators, e.g. hyphens.
 
-        DOCS: https://spacy.io/api/tokenizer#find_infix
+        DOCS: https://nightly.spacy.io/api/tokenizer#find_infix
         """
         if self.infix_finditer is None:
             return 0
@@ -555,7 +542,7 @@ cdef class Tokenizer:
         string (str): The string to segment.
         RETURNS (int): The length of the prefix if present, otherwise `None`.
 
-        DOCS: https://spacy.io/api/tokenizer#find_prefix
+        DOCS: https://nightly.spacy.io/api/tokenizer#find_prefix
         """
         if self.prefix_search is None:
             return 0
@@ -569,7 +556,7 @@ cdef class Tokenizer:
         string (str): The string to segment.
         Returns (int): The length of the suffix if present, otherwise `None`.
 
-        DOCS: https://spacy.io/api/tokenizer#find_suffix
+        DOCS: https://nightly.spacy.io/api/tokenizer#find_suffix
         """
         if self.suffix_search is None:
             return 0
@@ -584,9 +571,11 @@ cdef class Tokenizer:
                 self.add_special_case(chunk, substrings)
 
     def _validate_special_case(self, chunk, substrings):
-        """Check whether the `ORTH` fields match the string.
+        """Check whether the `ORTH` fields match the string. Check that
+        additional features beyond `ORTH` and `NORM` are not set by the
+        exception.
 
-        string (str): The string to specially tokenize.
+        chunk (str): The string to specially tokenize.
         substrings (iterable): A sequence of dicts, where each dict describes
             a token and its attributes.
         """
@@ -594,6 +583,10 @@ cdef class Tokenizer:
         orth = "".join([spec[ORTH] for spec in attrs])
         if chunk != orth:
             raise ValueError(Errors.E997.format(chunk=chunk, orth=orth, token_attrs=substrings))
+        for substring in attrs:
+            for attr in substring:
+                if attr not in (ORTH, NORM):
+                    raise ValueError(Errors.E1005.format(attr=self.vocab.strings[attr], chunk=chunk))
 
     def add_special_case(self, unicode string, substrings):
         """Add a special-case tokenization rule.
@@ -603,7 +596,7 @@ cdef class Tokenizer:
             a token and its attributes. The `ORTH` fields of the attributes
             must exactly match the string when they are concatenated.
 
-        DOCS: https://spacy.io/api/tokenizer#add_special_case
+        DOCS: https://nightly.spacy.io/api/tokenizer#add_special_case
         """
         self._validate_special_case(string, substrings)
         substrings = list(substrings)
@@ -642,7 +635,7 @@ cdef class Tokenizer:
         string (str): The string to tokenize.
         RETURNS (list): A list of (pattern_string, token_string) tuples
 
-        DOCS: https://spacy.io/api/tokenizer#explain
+        DOCS: https://nightly.spacy.io/api/tokenizer#explain
         """
         prefix_search = self.prefix_search
         suffix_search = self.suffix_search
@@ -723,7 +716,7 @@ cdef class Tokenizer:
             it doesn't exist.
         exclude (list): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/tokenizer#to_disk
+        DOCS: https://nightly.spacy.io/api/tokenizer#to_disk
         """
         path = util.ensure_path(path)
         with path.open("wb") as file_:
@@ -737,7 +730,7 @@ cdef class Tokenizer:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Tokenizer): The modified `Tokenizer` object.
 
-        DOCS: https://spacy.io/api/tokenizer#from_disk
+        DOCS: https://nightly.spacy.io/api/tokenizer#from_disk
         """
         path = util.ensure_path(path)
         with path.open("rb") as file_:
@@ -751,7 +744,7 @@ cdef class Tokenizer:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized form of the `Tokenizer` object.
 
-        DOCS: https://spacy.io/api/tokenizer#to_bytes
+        DOCS: https://nightly.spacy.io/api/tokenizer#to_bytes
         """
         serializers = {
             "vocab": lambda: self.vocab.to_bytes(),
@@ -771,7 +764,7 @@ cdef class Tokenizer:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Tokenizer): The `Tokenizer` object.
 
-        DOCS: https://spacy.io/api/tokenizer#from_bytes
+        DOCS: https://nightly.spacy.io/api/tokenizer#from_bytes
         """
         data = {}
         deserializers = {
diff --git a/spacy/tokens/_retokenize.pyx b/spacy/tokens/_retokenize.pyx
index 8d57b791f..c5fac2299 100644
--- a/spacy/tokens/_retokenize.pyx
+++ b/spacy/tokens/_retokenize.pyx
@@ -24,8 +24,8 @@ from ..strings import get_string_id
 cdef class Retokenizer:
     """Helper class for doc.retokenize() context manager.
 
-    DOCS: https://spacy.io/api/doc#retokenize
-    USAGE: https://spacy.io/usage/linguistic-features#retokenization
+    DOCS: https://nightly.spacy.io/api/doc#retokenize
+    USAGE: https://nightly.spacy.io/usage/linguistic-features#retokenization
     """
     cdef Doc doc
     cdef list merges
@@ -47,7 +47,7 @@ cdef class Retokenizer:
         span (Span): The span to merge.
         attrs (dict): Attributes to set on the merged token.
 
-        DOCS: https://spacy.io/api/doc#retokenizer.merge
+        DOCS: https://nightly.spacy.io/api/doc#retokenizer.merge
         """
         if (span.start, span.end) in self._spans_to_merge:
             return
@@ -73,7 +73,7 @@ cdef class Retokenizer:
         attrs (dict): Attributes to set on all split tokens. Attribute names
             mapped to list of per-token attribute values.
 
-        DOCS: https://spacy.io/api/doc#retokenizer.split
+        DOCS: https://nightly.spacy.io/api/doc#retokenizer.split
         """
         if ''.join(orths) != token.text:
             raise ValueError(Errors.E117.format(new=''.join(orths), old=token.text))
diff --git a/spacy/tokens/_serialize.py b/spacy/tokens/_serialize.py
index a257c7919..cd8c81939 100644
--- a/spacy/tokens/_serialize.py
+++ b/spacy/tokens/_serialize.py
@@ -61,7 +61,7 @@ class DocBin:
         store_user_data (bool): Whether to include the `Doc.user_data`.
         docs (Iterable[Doc]): Docs to add.
 
-        DOCS: https://spacy.io/api/docbin#init
+        DOCS: https://nightly.spacy.io/api/docbin#init
         """
         attrs = sorted([intify_attr(attr) for attr in attrs])
         self.version = "0.1"
@@ -86,7 +86,7 @@ class DocBin:
 
         doc (Doc): The Doc object to add.
 
-        DOCS: https://spacy.io/api/docbin#add
+        DOCS: https://nightly.spacy.io/api/docbin#add
         """
         array = doc.to_array(self.attrs)
         if len(array.shape) == 1:
@@ -115,7 +115,7 @@ class DocBin:
         vocab (Vocab): The shared vocab.
         YIELDS (Doc): The Doc objects.
 
-        DOCS: https://spacy.io/api/docbin#get_docs
+        DOCS: https://nightly.spacy.io/api/docbin#get_docs
         """
         for string in self.strings:
             vocab[string]
@@ -141,7 +141,7 @@ class DocBin:
 
         other (DocBin): The DocBin to merge into the current bin.
 
-        DOCS: https://spacy.io/api/docbin#merge
+        DOCS: https://nightly.spacy.io/api/docbin#merge
         """
         if self.attrs != other.attrs:
             raise ValueError(Errors.E166.format(current=self.attrs, other=other.attrs))
@@ -158,7 +158,7 @@ class DocBin:
 
         RETURNS (bytes): The serialized DocBin.
 
-        DOCS: https://spacy.io/api/docbin#to_bytes
+        DOCS: https://nightly.spacy.io/api/docbin#to_bytes
         """
         for tokens in self.tokens:
             assert len(tokens.shape) == 2, tokens.shape  # this should never happen
@@ -185,7 +185,7 @@ class DocBin:
         bytes_data (bytes): The data to load from.
         RETURNS (DocBin): The loaded DocBin.
 
-        DOCS: https://spacy.io/api/docbin#from_bytes
+        DOCS: https://nightly.spacy.io/api/docbin#from_bytes
         """
         msg = srsly.msgpack_loads(zlib.decompress(bytes_data))
         self.attrs = msg["attrs"]
@@ -211,7 +211,7 @@ class DocBin:
 
         path (str / Path): The file path.
 
-        DOCS: https://spacy.io/api/docbin#to_disk
+        DOCS: https://nightly.spacy.io/api/docbin#to_disk
         """
         path = ensure_path(path)
         with path.open("wb") as file_:
@@ -223,7 +223,7 @@ class DocBin:
         path (str / Path): The file path.
         RETURNS (DocBin): The loaded DocBin.
 
-        DOCS: https://spacy.io/api/docbin#to_disk
+        DOCS: https://nightly.spacy.io/api/docbin#to_disk
         """
         path = ensure_path(path)
         with path.open("rb") as file_:
diff --git a/spacy/tokens/doc.pyx b/spacy/tokens/doc.pyx
index cd080bf35..29bdf85ab 100644
--- a/spacy/tokens/doc.pyx
+++ b/spacy/tokens/doc.pyx
@@ -104,7 +104,7 @@ cdef class Doc:
         >>> from spacy.tokens import Doc
         >>> doc = Doc(nlp.vocab, words=["hello", "world", "!"], spaces=[True, False, False])
 
-    DOCS: https://spacy.io/api/doc
+    DOCS: https://nightly.spacy.io/api/doc
     """
 
     @classmethod
@@ -118,8 +118,8 @@ cdef class Doc:
         method (callable): Optional method for method extension.
         force (bool): Force overwriting existing attribute.
 
-        DOCS: https://spacy.io/api/doc#set_extension
-        USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
+        DOCS: https://nightly.spacy.io/api/doc#set_extension
+        USAGE: https://nightly.spacy.io/usage/processing-pipelines#custom-components-attributes
         """
         if cls.has_extension(name) and not kwargs.get("force", False):
             raise ValueError(Errors.E090.format(name=name, obj="Doc"))
@@ -132,7 +132,7 @@ cdef class Doc:
         name (str): Name of the extension.
         RETURNS (tuple): A `(default, method, getter, setter)` tuple.
 
-        DOCS: https://spacy.io/api/doc#get_extension
+        DOCS: https://nightly.spacy.io/api/doc#get_extension
         """
         return Underscore.doc_extensions.get(name)
 
@@ -143,7 +143,7 @@ cdef class Doc:
         name (str): Name of the extension.
         RETURNS (bool): Whether the extension has been registered.
 
-        DOCS: https://spacy.io/api/doc#has_extension
+        DOCS: https://nightly.spacy.io/api/doc#has_extension
         """
         return name in Underscore.doc_extensions
 
@@ -155,7 +155,7 @@ cdef class Doc:
         RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
             removed extension.
 
-        DOCS: https://spacy.io/api/doc#remove_extension
+        DOCS: https://nightly.spacy.io/api/doc#remove_extension
         """
         if not cls.has_extension(name):
             raise ValueError(Errors.E046.format(name=name))
@@ -173,7 +173,7 @@ cdef class Doc:
             it is not. If `None`, defaults to `[True]*len(words)`
         user_data (dict or None): Optional extra data to attach to the Doc.
 
-        DOCS: https://spacy.io/api/doc#init
+        DOCS: https://nightly.spacy.io/api/doc#init
         """
         self.vocab = vocab
         size = max(20, (len(words) if words is not None else 0))
@@ -288,7 +288,7 @@ cdef class Doc:
             You can use negative indices and open-ended ranges, which have
             their normal Python semantics.
 
-        DOCS: https://spacy.io/api/doc#getitem
+        DOCS: https://nightly.spacy.io/api/doc#getitem
         """
         if isinstance(i, slice):
             start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
@@ -305,7 +305,7 @@ cdef class Doc:
         than-Python speeds are required, you can instead access the annotations
         as a numpy array, or access the underlying C data directly from Cython.
 
-        DOCS: https://spacy.io/api/doc#iter
+        DOCS: https://nightly.spacy.io/api/doc#iter
         """
         cdef int i
         for i in range(self.length):
@@ -316,7 +316,7 @@ cdef class Doc:
 
         RETURNS (int): The number of tokens in the document.
 
-        DOCS: https://spacy.io/api/doc#len
+        DOCS: https://nightly.spacy.io/api/doc#len
         """
         return self.length
 
@@ -349,7 +349,7 @@ cdef class Doc:
             the span.
         RETURNS (Span): The newly constructed object.
 
-        DOCS: https://spacy.io/api/doc#char_span
+        DOCS: https://nightly.spacy.io/api/doc#char_span
         """
         if not isinstance(label, int):
             label = self.vocab.strings.add(label)
@@ -374,7 +374,7 @@ cdef class Doc:
             `Span`, `Token` and `Lexeme` objects.
         RETURNS (float): A scalar similarity score. Higher is more similar.
 
-        DOCS: https://spacy.io/api/doc#similarity
+        DOCS: https://nightly.spacy.io/api/doc#similarity
         """
         if "similarity" in self.user_hooks:
             return self.user_hooks["similarity"](self, other)
@@ -407,7 +407,7 @@ cdef class Doc:
 
         RETURNS (bool): Whether a word vector is associated with the object.
 
-        DOCS: https://spacy.io/api/doc#has_vector
+        DOCS: https://nightly.spacy.io/api/doc#has_vector
         """
         if "has_vector" in self.user_hooks:
             return self.user_hooks["has_vector"](self)
@@ -425,7 +425,7 @@ cdef class Doc:
         RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
             representing the document's semantics.
 
-        DOCS: https://spacy.io/api/doc#vector
+        DOCS: https://nightly.spacy.io/api/doc#vector
         """
         def __get__(self):
             if "vector" in self.user_hooks:
@@ -453,7 +453,7 @@ cdef class Doc:
 
         RETURNS (float): The L2 norm of the vector representation.
 
-        DOCS: https://spacy.io/api/doc#vector_norm
+        DOCS: https://nightly.spacy.io/api/doc#vector_norm
         """
         def __get__(self):
             if "vector_norm" in self.user_hooks:
@@ -493,7 +493,7 @@ cdef class Doc:
 
         RETURNS (tuple): Entities in the document, one `Span` per entity.
 
-        DOCS: https://spacy.io/api/doc#ents
+        DOCS: https://nightly.spacy.io/api/doc#ents
         """
         def __get__(self):
             cdef int i
@@ -584,7 +584,7 @@ cdef class Doc:
 
         YIELDS (Span): Noun chunks in the document.
 
-        DOCS: https://spacy.io/api/doc#noun_chunks
+        DOCS: https://nightly.spacy.io/api/doc#noun_chunks
         """
 
         # Accumulate the result before beginning to iterate over it. This
@@ -609,7 +609,7 @@ cdef class Doc:
 
         YIELDS (Span): Sentences in the document.
 
-        DOCS: https://spacy.io/api/doc#sents
+        DOCS: https://nightly.spacy.io/api/doc#sents
         """
         if not self.is_sentenced:
             raise ValueError(Errors.E030)
@@ -722,7 +722,7 @@ cdef class Doc:
         attr_id (int): The attribute ID to key the counts.
         RETURNS (dict): A dictionary mapping attributes to integer counts.
 
-        DOCS: https://spacy.io/api/doc#count_by
+        DOCS: https://nightly.spacy.io/api/doc#count_by
         """
         cdef int i
         cdef attr_t attr
@@ -777,7 +777,7 @@ cdef class Doc:
         array (numpy.ndarray[ndim=2, dtype='int32']): The attribute values.
         RETURNS (Doc): Itself.
 
-        DOCS: https://spacy.io/api/doc#from_array
+        DOCS: https://nightly.spacy.io/api/doc#from_array
         """
         # Handle scalar/list inputs of strings/ints for py_attr_ids
         # See also #3064
@@ -872,7 +872,7 @@ cdef class Doc:
         attrs (list): Optional list of attribute ID ints or attribute name strings.
         RETURNS (Doc): A doc that contains the concatenated docs, or None if no docs were given.
 
-        DOCS: https://spacy.io/api/doc#from_docs
+        DOCS: https://nightly.spacy.io/api/doc#from_docs
         """
         if not docs:
             return None
@@ -920,7 +920,9 @@ cdef class Doc:
                         warnings.warn(Warnings.W101.format(name=name))
                 else:
                     warnings.warn(Warnings.W102.format(key=key, value=value))
-            char_offset += len(doc.text) if not ensure_whitespace or doc[-1].is_space else len(doc.text) + 1
+            char_offset += len(doc.text)
+            if ensure_whitespace and not (len(doc) > 0 and doc[-1].is_space):
+                char_offset += 1
 
         arrays = [doc.to_array(attrs) for doc in docs]
 
@@ -932,7 +934,7 @@ cdef class Doc:
             token_offset = -1
             for doc in docs[:-1]:
                 token_offset += len(doc)
-                if not doc[-1].is_space:
+                if not (len(doc) > 0 and doc[-1].is_space):
                     concat_spaces[token_offset] = True
 
         concat_array = numpy.concatenate(arrays)
@@ -951,7 +953,7 @@ cdef class Doc:
         RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
             (n, n), where n = len(self).
 
-        DOCS: https://spacy.io/api/doc#get_lca_matrix
+        DOCS: https://nightly.spacy.io/api/doc#get_lca_matrix
         """
         return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
 
@@ -985,7 +987,7 @@ cdef class Doc:
             it doesn't exist. Paths may be either strings or Path-like objects.
         exclude (Iterable[str]): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/doc#to_disk
+        DOCS: https://nightly.spacy.io/api/doc#to_disk
         """
         path = util.ensure_path(path)
         with path.open("wb") as file_:
@@ -1000,7 +1002,7 @@ cdef class Doc:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Doc): The modified `Doc` object.
 
-        DOCS: https://spacy.io/api/doc#from_disk
+        DOCS: https://nightly.spacy.io/api/doc#from_disk
         """
         path = util.ensure_path(path)
         with path.open("rb") as file_:
@@ -1014,7 +1016,7 @@ cdef class Doc:
         RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
             all annotations.
 
-        DOCS: https://spacy.io/api/doc#to_bytes
+        DOCS: https://nightly.spacy.io/api/doc#to_bytes
         """
         return srsly.msgpack_dumps(self.to_dict(exclude=exclude))
 
@@ -1025,7 +1027,7 @@ cdef class Doc:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Doc): Itself.
 
-        DOCS: https://spacy.io/api/doc#from_bytes
+        DOCS: https://nightly.spacy.io/api/doc#from_bytes
         """
         return self.from_dict(srsly.msgpack_loads(bytes_data), exclude=exclude)
 
@@ -1036,7 +1038,7 @@ cdef class Doc:
         RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
             all annotations.
 
-        DOCS: https://spacy.io/api/doc#to_bytes
+        DOCS: https://nightly.spacy.io/api/doc#to_bytes
         """
         array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE, ENT_ID, NORM, ENT_KB_ID]
         if self.is_tagged:
@@ -1084,7 +1086,7 @@ cdef class Doc:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Doc): Itself.
 
-        DOCS: https://spacy.io/api/doc#from_dict
+        DOCS: https://nightly.spacy.io/api/doc#from_dict
         """
         if self.length != 0:
             raise ValueError(Errors.E033.format(length=self.length))
@@ -1164,8 +1166,8 @@ cdef class Doc:
         retokenization are invalidated, although they may accidentally
         continue to work.
 
-        DOCS: https://spacy.io/api/doc#retokenize
-        USAGE: https://spacy.io/usage/linguistic-features#retokenization
+        DOCS: https://nightly.spacy.io/api/doc#retokenize
+        USAGE: https://nightly.spacy.io/usage/linguistic-features#retokenization
         """
         return Retokenizer(self)
 
@@ -1200,7 +1202,7 @@ cdef class Doc:
         be added to an "_" key in the data, e.g. "_": {"foo": "bar"}.
         RETURNS (dict): The data in spaCy's JSON format.
 
-        DOCS: https://spacy.io/api/doc#to_json
+        DOCS: https://nightly.spacy.io/api/doc#to_json
         """
         data = {"text": self.text}
         if self.is_nered:
diff --git a/spacy/tokens/span.pyx b/spacy/tokens/span.pyx
index 15e6518d6..f06f3307d 100644
--- a/spacy/tokens/span.pyx
+++ b/spacy/tokens/span.pyx
@@ -27,7 +27,7 @@ from .underscore import Underscore, get_ext_args
 cdef class Span:
     """A slice from a Doc object.
 
-    DOCS: https://spacy.io/api/span
+    DOCS: https://nightly.spacy.io/api/span
     """
     @classmethod
     def set_extension(cls, name, **kwargs):
@@ -40,8 +40,8 @@ cdef class Span:
         method (callable): Optional method for method extension.
         force (bool): Force overwriting existing attribute.
 
-        DOCS: https://spacy.io/api/span#set_extension
-        USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
+        DOCS: https://nightly.spacy.io/api/span#set_extension
+        USAGE: https://nightly.spacy.io/usage/processing-pipelines#custom-components-attributes
         """
         if cls.has_extension(name) and not kwargs.get("force", False):
             raise ValueError(Errors.E090.format(name=name, obj="Span"))
@@ -54,7 +54,7 @@ cdef class Span:
         name (str): Name of the extension.
         RETURNS (tuple): A `(default, method, getter, setter)` tuple.
 
-        DOCS: https://spacy.io/api/span#get_extension
+        DOCS: https://nightly.spacy.io/api/span#get_extension
         """
         return Underscore.span_extensions.get(name)
 
@@ -65,7 +65,7 @@ cdef class Span:
         name (str): Name of the extension.
         RETURNS (bool): Whether the extension has been registered.
 
-        DOCS: https://spacy.io/api/span#has_extension
+        DOCS: https://nightly.spacy.io/api/span#has_extension
         """
         return name in Underscore.span_extensions
 
@@ -77,7 +77,7 @@ cdef class Span:
         RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
             removed extension.
 
-        DOCS: https://spacy.io/api/span#remove_extension
+        DOCS: https://nightly.spacy.io/api/span#remove_extension
         """
         if not cls.has_extension(name):
             raise ValueError(Errors.E046.format(name=name))
@@ -95,7 +95,7 @@ cdef class Span:
         vector (ndarray[ndim=1, dtype='float32']): A meaning representation
             of the span.
 
-        DOCS: https://spacy.io/api/span#init
+        DOCS: https://nightly.spacy.io/api/span#init
         """
         if not (0 <= start <= end <= len(doc)):
             raise IndexError(Errors.E035.format(start=start, end=end, length=len(doc)))
@@ -151,7 +151,7 @@ cdef class Span:
 
         RETURNS (int): The number of tokens in the span.
 
-        DOCS: https://spacy.io/api/span#len
+        DOCS: https://nightly.spacy.io/api/span#len
         """
         self._recalculate_indices()
         if self.end < self.start:
@@ -168,7 +168,7 @@ cdef class Span:
             the span to get.
         RETURNS (Token or Span): The token at `span[i]`.
 
-        DOCS: https://spacy.io/api/span#getitem
+        DOCS: https://nightly.spacy.io/api/span#getitem
         """
         self._recalculate_indices()
         if isinstance(i, slice):
@@ -189,7 +189,7 @@ cdef class Span:
 
         YIELDS (Token): A `Token` object.
 
-        DOCS: https://spacy.io/api/span#iter
+        DOCS: https://nightly.spacy.io/api/span#iter
         """
         self._recalculate_indices()
         for i in range(self.start, self.end):
@@ -210,7 +210,7 @@ cdef class Span:
         copy_user_data (bool): Whether or not to copy the original doc's user data.
         RETURNS (Doc): The `Doc` copy of the span.
 
-        DOCS: https://spacy.io/api/span#as_doc
+        DOCS: https://nightly.spacy.io/api/span#as_doc
         """
         # TODO: make copy_user_data a keyword-only argument (Python 3 only)
         words = [t.text for t in self]
@@ -292,7 +292,7 @@ cdef class Span:
         RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
             (n, n), where n = len(self).
 
-        DOCS: https://spacy.io/api/span#get_lca_matrix
+        DOCS: https://nightly.spacy.io/api/span#get_lca_matrix
         """
         return numpy.asarray(_get_lca_matrix(self.doc, self.start, self.end))
 
@@ -304,7 +304,7 @@ cdef class Span:
             `Span`, `Token` and `Lexeme` objects.
         RETURNS (float): A scalar similarity score. Higher is more similar.
 
-        DOCS: https://spacy.io/api/span#similarity
+        DOCS: https://nightly.spacy.io/api/span#similarity
         """
         if "similarity" in self.doc.user_span_hooks:
             return self.doc.user_span_hooks["similarity"](self, other)
@@ -400,7 +400,7 @@ cdef class Span:
 
         RETURNS (tuple): Entities in the span, one `Span` per entity.
 
-        DOCS: https://spacy.io/api/span#ents
+        DOCS: https://nightly.spacy.io/api/span#ents
         """
         ents = []
         for ent in self.doc.ents:
@@ -415,7 +415,7 @@ cdef class Span:
 
         RETURNS (bool): Whether a word vector is associated with the object.
 
-        DOCS: https://spacy.io/api/span#has_vector
+        DOCS: https://nightly.spacy.io/api/span#has_vector
         """
         if "has_vector" in self.doc.user_span_hooks:
             return self.doc.user_span_hooks["has_vector"](self)
@@ -434,7 +434,7 @@ cdef class Span:
         RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
             representing the span's semantics.
 
-        DOCS: https://spacy.io/api/span#vector
+        DOCS: https://nightly.spacy.io/api/span#vector
         """
         if "vector" in self.doc.user_span_hooks:
             return self.doc.user_span_hooks["vector"](self)
@@ -448,7 +448,7 @@ cdef class Span:
 
         RETURNS (float): The L2 norm of the vector representation.
 
-        DOCS: https://spacy.io/api/span#vector_norm
+        DOCS: https://nightly.spacy.io/api/span#vector_norm
         """
         if "vector_norm" in self.doc.user_span_hooks:
             return self.doc.user_span_hooks["vector"](self)
@@ -508,7 +508,7 @@ cdef class Span:
 
         YIELDS (Span): Base noun-phrase `Span` objects.
 
-        DOCS: https://spacy.io/api/span#noun_chunks
+        DOCS: https://nightly.spacy.io/api/span#noun_chunks
         """
         if not self.doc.is_parsed:
             raise ValueError(Errors.E029)
@@ -533,7 +533,7 @@ cdef class Span:
 
         RETURNS (Token): The root token.
 
-        DOCS: https://spacy.io/api/span#root
+        DOCS: https://nightly.spacy.io/api/span#root
         """
         self._recalculate_indices()
         if "root" in self.doc.user_span_hooks:
@@ -590,7 +590,7 @@ cdef class Span:
 
         RETURNS (tuple): A tuple of Token objects.
 
-        DOCS: https://spacy.io/api/span#lefts
+        DOCS: https://nightly.spacy.io/api/span#lefts
         """
         return self.root.conjuncts
 
@@ -601,7 +601,7 @@ cdef class Span:
 
         YIELDS (Token):A left-child of a token of the span.
 
-        DOCS: https://spacy.io/api/span#lefts
+        DOCS: https://nightly.spacy.io/api/span#lefts
         """
         for token in reversed(self):  # Reverse, so we get tokens in order
             for left in token.lefts:
@@ -615,7 +615,7 @@ cdef class Span:
 
         YIELDS (Token): A right-child of a token of the span.
 
-        DOCS: https://spacy.io/api/span#rights
+        DOCS: https://nightly.spacy.io/api/span#rights
         """
         for token in self:
             for right in token.rights:
@@ -630,7 +630,7 @@ cdef class Span:
         RETURNS (int): The number of leftward immediate children of the
             span, in the syntactic dependency parse.
 
-        DOCS: https://spacy.io/api/span#n_lefts
+        DOCS: https://nightly.spacy.io/api/span#n_lefts
         """
         return len(list(self.lefts))
 
@@ -642,7 +642,7 @@ cdef class Span:
         RETURNS (int): The number of rightward immediate children of the
             span, in the syntactic dependency parse.
 
-        DOCS: https://spacy.io/api/span#n_rights
+        DOCS: https://nightly.spacy.io/api/span#n_rights
         """
         return len(list(self.rights))
 
@@ -652,7 +652,7 @@ cdef class Span:
 
         YIELDS (Token): A token within the span, or a descendant from it.
 
-        DOCS: https://spacy.io/api/span#subtree
+        DOCS: https://nightly.spacy.io/api/span#subtree
         """
         for word in self.lefts:
             yield from word.subtree
diff --git a/spacy/tokens/token.pyx b/spacy/tokens/token.pyx
index 8afde60ee..50f1c5da3 100644
--- a/spacy/tokens/token.pyx
+++ b/spacy/tokens/token.pyx
@@ -30,7 +30,7 @@ cdef class Token:
     """An individual token – i.e. a word, punctuation symbol, whitespace,
     etc.
 
-    DOCS: https://spacy.io/api/token
+    DOCS: https://nightly.spacy.io/api/token
     """
     @classmethod
     def set_extension(cls, name, **kwargs):
@@ -43,8 +43,8 @@ cdef class Token:
         method (callable): Optional method for method extension.
         force (bool): Force overwriting existing attribute.
 
-        DOCS: https://spacy.io/api/token#set_extension
-        USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
+        DOCS: https://nightly.spacy.io/api/token#set_extension
+        USAGE: https://nightly.spacy.io/usage/processing-pipelines#custom-components-attributes
         """
         if cls.has_extension(name) and not kwargs.get("force", False):
             raise ValueError(Errors.E090.format(name=name, obj="Token"))
@@ -57,7 +57,7 @@ cdef class Token:
         name (str): Name of the extension.
         RETURNS (tuple): A `(default, method, getter, setter)` tuple.
 
-        DOCS: https://spacy.io/api/token#get_extension
+        DOCS: https://nightly.spacy.io/api/token#get_extension
         """
         return Underscore.token_extensions.get(name)
 
@@ -68,7 +68,7 @@ cdef class Token:
         name (str): Name of the extension.
         RETURNS (bool): Whether the extension has been registered.
 
-        DOCS: https://spacy.io/api/token#has_extension
+        DOCS: https://nightly.spacy.io/api/token#has_extension
         """
         return name in Underscore.token_extensions
 
@@ -80,7 +80,7 @@ cdef class Token:
         RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
             removed extension.
 
-        DOCS: https://spacy.io/api/token#remove_extension
+        DOCS: https://nightly.spacy.io/api/token#remove_extension
         """
         if not cls.has_extension(name):
             raise ValueError(Errors.E046.format(name=name))
@@ -93,7 +93,7 @@ cdef class Token:
         doc (Doc): The parent document.
         offset (int): The index of the token within the document.
 
-        DOCS: https://spacy.io/api/token#init
+        DOCS: https://nightly.spacy.io/api/token#init
         """
         self.vocab = vocab
         self.doc = doc
@@ -108,7 +108,7 @@ cdef class Token:
 
         RETURNS (int): The number of unicode characters in the token.
 
-        DOCS: https://spacy.io/api/token#len
+        DOCS: https://nightly.spacy.io/api/token#len
         """
         return self.c.lex.length
 
@@ -171,7 +171,7 @@ cdef class Token:
         flag_id (int): The ID of the flag attribute.
         RETURNS (bool): Whether the flag is set.
 
-        DOCS: https://spacy.io/api/token#check_flag
+        DOCS: https://nightly.spacy.io/api/token#check_flag
         """
         return Lexeme.c_check_flag(self.c.lex, flag_id)
 
@@ -181,7 +181,7 @@ cdef class Token:
         i (int): The relative position of the token to get. Defaults to 1.
         RETURNS (Token): The token at position `self.doc[self.i+i]`.
 
-        DOCS: https://spacy.io/api/token#nbor
+        DOCS: https://nightly.spacy.io/api/token#nbor
         """
         if self.i+i < 0 or (self.i+i >= len(self.doc)):
             raise IndexError(Errors.E042.format(i=self.i, j=i, length=len(self.doc)))
@@ -195,7 +195,7 @@ cdef class Token:
             `Span`, `Token` and `Lexeme` objects.
         RETURNS (float): A scalar similarity score. Higher is more similar.
 
-        DOCS: https://spacy.io/api/token#similarity
+        DOCS: https://nightly.spacy.io/api/token#similarity
         """
         if "similarity" in self.doc.user_token_hooks:
             return self.doc.user_token_hooks["similarity"](self, other)
@@ -373,7 +373,7 @@ cdef class Token:
 
         RETURNS (bool): Whether a word vector is associated with the object.
 
-        DOCS: https://spacy.io/api/token#has_vector
+        DOCS: https://nightly.spacy.io/api/token#has_vector
         """
         if "has_vector" in self.doc.user_token_hooks:
             return self.doc.user_token_hooks["has_vector"](self)
@@ -388,7 +388,7 @@ cdef class Token:
         RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
             representing the token's semantics.
 
-        DOCS: https://spacy.io/api/token#vector
+        DOCS: https://nightly.spacy.io/api/token#vector
         """
         if "vector" in self.doc.user_token_hooks:
             return self.doc.user_token_hooks["vector"](self)
@@ -403,7 +403,7 @@ cdef class Token:
 
         RETURNS (float): The L2 norm of the vector representation.
 
-        DOCS: https://spacy.io/api/token#vector_norm
+        DOCS: https://nightly.spacy.io/api/token#vector_norm
         """
         if "vector_norm" in self.doc.user_token_hooks:
             return self.doc.user_token_hooks["vector_norm"](self)
@@ -426,7 +426,7 @@ cdef class Token:
         RETURNS (int): The number of leftward immediate children of the
             word, in the syntactic dependency parse.
 
-        DOCS: https://spacy.io/api/token#n_lefts
+        DOCS: https://nightly.spacy.io/api/token#n_lefts
         """
         return self.c.l_kids
 
@@ -438,7 +438,7 @@ cdef class Token:
         RETURNS (int): The number of rightward immediate children of the
             word, in the syntactic dependency parse.
 
-        DOCS: https://spacy.io/api/token#n_rights
+        DOCS: https://nightly.spacy.io/api/token#n_rights
         """
         return self.c.r_kids
 
@@ -470,7 +470,7 @@ cdef class Token:
         RETURNS (bool / None): Whether the token starts a sentence.
             None if unknown.
 
-        DOCS: https://spacy.io/api/token#is_sent_start
+        DOCS: https://nightly.spacy.io/api/token#is_sent_start
         """
         def __get__(self):
             if self.c.sent_start == 0:
@@ -499,7 +499,7 @@ cdef class Token:
         RETURNS (bool / None): Whether the token ends a sentence.
             None if unknown.
 
-        DOCS: https://spacy.io/api/token#is_sent_end
+        DOCS: https://nightly.spacy.io/api/token#is_sent_end
         """
         def __get__(self):
             if self.i + 1 == len(self.doc):
@@ -521,7 +521,7 @@ cdef class Token:
 
         YIELDS (Token): A left-child of the token.
 
-        DOCS: https://spacy.io/api/token#lefts
+        DOCS: https://nightly.spacy.io/api/token#lefts
         """
         cdef int nr_iter = 0
         cdef const TokenC* ptr = self.c - (self.i - self.c.l_edge)
@@ -541,7 +541,7 @@ cdef class Token:
 
         YIELDS (Token): A right-child of the token.
 
-        DOCS: https://spacy.io/api/token#rights
+        DOCS: https://nightly.spacy.io/api/token#rights
         """
         cdef const TokenC* ptr = self.c + (self.c.r_edge - self.i)
         tokens = []
@@ -563,7 +563,7 @@ cdef class Token:
 
         YIELDS (Token): A child token such that `child.head==self`.
 
-        DOCS: https://spacy.io/api/token#children
+        DOCS: https://nightly.spacy.io/api/token#children
         """
         yield from self.lefts
         yield from self.rights
@@ -576,7 +576,7 @@ cdef class Token:
         YIELDS (Token): A descendent token such that
             `self.is_ancestor(descendent) or token == self`.
 
-        DOCS: https://spacy.io/api/token#subtree
+        DOCS: https://nightly.spacy.io/api/token#subtree
         """
         for word in self.lefts:
             yield from word.subtree
@@ -607,7 +607,7 @@ cdef class Token:
         YIELDS (Token): A sequence of ancestor tokens such that
             `ancestor.is_ancestor(self)`.
 
-        DOCS: https://spacy.io/api/token#ancestors
+        DOCS: https://nightly.spacy.io/api/token#ancestors
         """
         cdef const TokenC* head_ptr = self.c
         # Guard against infinite loop, no token can have
@@ -625,7 +625,7 @@ cdef class Token:
         descendant (Token): Another token.
         RETURNS (bool): Whether this token is the ancestor of the descendant.
 
-        DOCS: https://spacy.io/api/token#is_ancestor
+        DOCS: https://nightly.spacy.io/api/token#is_ancestor
         """
         if self.doc is not descendant.doc:
             return False
@@ -729,7 +729,7 @@ cdef class Token:
 
         RETURNS (tuple): The coordinated tokens.
 
-        DOCS: https://spacy.io/api/token#conjuncts
+        DOCS: https://nightly.spacy.io/api/token#conjuncts
         """
         cdef Token word, child
         if "conjuncts" in self.doc.user_token_hooks:
diff --git a/spacy/util.py b/spacy/util.py
index 0eb76c3d1..fa4815df8 100644
--- a/spacy/util.py
+++ b/spacy/util.py
@@ -76,7 +76,7 @@ class registry(thinc.registry):
     lemmatizers = catalogue.create("spacy", "lemmatizers", entry_points=True)
     lookups = catalogue.create("spacy", "lookups", entry_points=True)
     displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
-    assets = catalogue.create("spacy", "assets", entry_points=True)
+    misc = catalogue.create("spacy", "misc", entry_points=True)
     # Callback functions used to manipulate nlp object etc.
     callbacks = catalogue.create("spacy", "callbacks")
     batchers = catalogue.create("spacy", "batchers", entry_points=True)
diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index bcea87e67..ae2508c87 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -44,7 +44,7 @@ cdef class Vectors:
     the table need to be assigned - so len(list(vectors.keys())) may be
     greater or smaller than vectors.shape[0].
 
-    DOCS: https://spacy.io/api/vectors
+    DOCS: https://nightly.spacy.io/api/vectors
     """
     cdef public object name
     cdef public object data
@@ -59,7 +59,7 @@ cdef class Vectors:
         keys (iterable): A sequence of keys, aligned with the data.
         name (str): A name to identify the vectors table.
 
-        DOCS: https://spacy.io/api/vectors#init
+        DOCS: https://nightly.spacy.io/api/vectors#init
         """
         self.name = name
         if data is None:
@@ -83,7 +83,7 @@ cdef class Vectors:
 
         RETURNS (tuple): A `(rows, dims)` pair.
 
-        DOCS: https://spacy.io/api/vectors#shape
+        DOCS: https://nightly.spacy.io/api/vectors#shape
         """
         return self.data.shape
 
@@ -93,7 +93,7 @@ cdef class Vectors:
 
         RETURNS (int): The vector size.
 
-        DOCS: https://spacy.io/api/vectors#size
+        DOCS: https://nightly.spacy.io/api/vectors#size
         """
         return self.data.shape[0] * self.data.shape[1]
 
@@ -103,7 +103,7 @@ cdef class Vectors:
 
         RETURNS (bool): `True` if no slots are available for new keys.
 
-        DOCS: https://spacy.io/api/vectors#is_full
+        DOCS: https://nightly.spacy.io/api/vectors#is_full
         """
         return self._unset.size() == 0
 
@@ -114,7 +114,7 @@ cdef class Vectors:
 
         RETURNS (int): The number of keys in the table.
 
-        DOCS: https://spacy.io/api/vectors#n_keys
+        DOCS: https://nightly.spacy.io/api/vectors#n_keys
         """
         return len(self.key2row)
 
@@ -127,7 +127,7 @@ cdef class Vectors:
         key (int): The key to get the vector for.
         RETURNS (ndarray): The vector for the key.
 
-        DOCS: https://spacy.io/api/vectors#getitem
+        DOCS: https://nightly.spacy.io/api/vectors#getitem
         """
         i = self.key2row[key]
         if i is None:
@@ -141,7 +141,7 @@ cdef class Vectors:
         key (int): The key to set the vector for.
         vector (ndarray): The vector to set.
 
-        DOCS: https://spacy.io/api/vectors#setitem
+        DOCS: https://nightly.spacy.io/api/vectors#setitem
         """
         i = self.key2row[key]
         self.data[i] = vector
@@ -153,7 +153,7 @@ cdef class Vectors:
 
         YIELDS (int): A key in the table.
 
-        DOCS: https://spacy.io/api/vectors#iter
+        DOCS: https://nightly.spacy.io/api/vectors#iter
         """
         yield from self.key2row
 
@@ -162,7 +162,7 @@ cdef class Vectors:
 
         RETURNS (int): The number of vectors in the data.
 
-        DOCS: https://spacy.io/api/vectors#len
+        DOCS: https://nightly.spacy.io/api/vectors#len
         """
         return self.data.shape[0]
 
@@ -172,7 +172,7 @@ cdef class Vectors:
         key (int): The key to check.
         RETURNS (bool): Whether the key has a vector entry.
 
-        DOCS: https://spacy.io/api/vectors#contains
+        DOCS: https://nightly.spacy.io/api/vectors#contains
         """
         return key in self.key2row
 
@@ -189,7 +189,7 @@ cdef class Vectors:
         inplace (bool): Reallocate the memory.
         RETURNS (list): The removed items as a list of `(key, row)` tuples.
 
-        DOCS: https://spacy.io/api/vectors#resize
+        DOCS: https://nightly.spacy.io/api/vectors#resize
         """
         xp = get_array_module(self.data)
         if inplace:
@@ -224,7 +224,7 @@ cdef class Vectors:
 
         YIELDS (ndarray): A vector in the table.
 
-        DOCS: https://spacy.io/api/vectors#values
+        DOCS: https://nightly.spacy.io/api/vectors#values
         """
         for row, vector in enumerate(range(self.data.shape[0])):
             if not self._unset.count(row):
@@ -235,7 +235,7 @@ cdef class Vectors:
 
         YIELDS (tuple): A key/vector pair.
 
-        DOCS: https://spacy.io/api/vectors#items
+        DOCS: https://nightly.spacy.io/api/vectors#items
         """
         for key, row in self.key2row.items():
             yield key, self.data[row]
@@ -281,7 +281,7 @@ cdef class Vectors:
         row (int / None): The row number of a vector to map the key to.
         RETURNS (int): The row the vector was added to.
 
-        DOCS: https://spacy.io/api/vectors#add
+        DOCS: https://nightly.spacy.io/api/vectors#add
         """
         # use int for all keys and rows in key2row for more efficient access
         # and serialization
@@ -368,7 +368,7 @@ cdef class Vectors:
         path (str / Path): A path to a directory, which will be created if
             it doesn't exists.
 
-        DOCS: https://spacy.io/api/vectors#to_disk
+        DOCS: https://nightly.spacy.io/api/vectors#to_disk
         """
         xp = get_array_module(self.data)
         if xp is numpy:
@@ -396,7 +396,7 @@ cdef class Vectors:
         path (str / Path): Directory path, string or Path-like object.
         RETURNS (Vectors): The modified object.
 
-        DOCS: https://spacy.io/api/vectors#from_disk
+        DOCS: https://nightly.spacy.io/api/vectors#from_disk
         """
         def load_key2row(path):
             if path.exists():
@@ -432,7 +432,7 @@ cdef class Vectors:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized form of the `Vectors` object.
 
-        DOCS: https://spacy.io/api/vectors#to_bytes
+        DOCS: https://nightly.spacy.io/api/vectors#to_bytes
         """
         def serialize_weights():
             if hasattr(self.data, "to_bytes"):
@@ -453,7 +453,7 @@ cdef class Vectors:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Vectors): The `Vectors` object.
 
-        DOCS: https://spacy.io/api/vectors#from_bytes
+        DOCS: https://nightly.spacy.io/api/vectors#from_bytes
         """
         def deserialize_weights(b):
             if hasattr(self.data, "from_bytes"):
diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 9e14f37d2..ef0847e54 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -54,7 +54,7 @@ cdef class Vocab:
     instance also provides access to the `StringStore`, and owns underlying
     C-data that is shared between `Doc` objects.
 
-    DOCS: https://spacy.io/api/vocab
+    DOCS: https://nightly.spacy.io/api/vocab
     """
     def __init__(self, lex_attr_getters=None, strings=tuple(), lookups=None,
                  oov_prob=-20., vectors_name=None, writing_system={},
@@ -117,7 +117,7 @@ cdef class Vocab:
             available bit will be chosen.
         RETURNS (int): The integer ID by which the flag value can be checked.
 
-        DOCS: https://spacy.io/api/vocab#add_flag
+        DOCS: https://nightly.spacy.io/api/vocab#add_flag
         """
         if flag_id == -1:
             for bit in range(1, 64):
@@ -201,7 +201,7 @@ cdef class Vocab:
         string (unicode): The ID string.
         RETURNS (bool) Whether the string has an entry in the vocabulary.
 
-        DOCS: https://spacy.io/api/vocab#contains
+        DOCS: https://nightly.spacy.io/api/vocab#contains
         """
         cdef hash_t int_key
         if isinstance(key, bytes):
@@ -218,7 +218,7 @@ cdef class Vocab:
 
         YIELDS (Lexeme): An entry in the vocabulary.
 
-        DOCS: https://spacy.io/api/vocab#iter
+        DOCS: https://nightly.spacy.io/api/vocab#iter
         """
         cdef attr_t key
         cdef size_t addr
@@ -241,7 +241,7 @@ cdef class Vocab:
             >>> apple = nlp.vocab.strings["apple"]
             >>> assert nlp.vocab[apple] == nlp.vocab[u"apple"]
 
-        DOCS: https://spacy.io/api/vocab#getitem
+        DOCS: https://nightly.spacy.io/api/vocab#getitem
         """
         cdef attr_t orth
         if isinstance(id_or_string, unicode):
@@ -309,7 +309,7 @@ cdef class Vocab:
             word was mapped to, and `score` the similarity score between the
             two words.
 
-        DOCS: https://spacy.io/api/vocab#prune_vectors
+        DOCS: https://nightly.spacy.io/api/vocab#prune_vectors
         """
         xp = get_array_module(self.vectors.data)
         # Make prob negative so it sorts by rank ascending
@@ -349,7 +349,7 @@ cdef class Vocab:
             and shape determined by the `vocab.vectors` instance. Usually, a
             numpy ndarray of shape (300,) and dtype float32.
 
-        DOCS: https://spacy.io/api/vocab#get_vector
+        DOCS: https://nightly.spacy.io/api/vocab#get_vector
         """
         if isinstance(orth, str):
             orth = self.strings.add(orth)
@@ -396,7 +396,7 @@ cdef class Vocab:
         orth (int / unicode): The word.
         vector (numpy.ndarray[ndim=1, dtype='float32']): The vector to set.
 
-        DOCS: https://spacy.io/api/vocab#set_vector
+        DOCS: https://nightly.spacy.io/api/vocab#set_vector
         """
         if isinstance(orth, str):
             orth = self.strings.add(orth)
@@ -418,7 +418,7 @@ cdef class Vocab:
         orth (int / unicode): The word.
         RETURNS (bool): Whether the word has a vector.
 
-        DOCS: https://spacy.io/api/vocab#has_vector
+        DOCS: https://nightly.spacy.io/api/vocab#has_vector
         """
         if isinstance(orth, str):
             orth = self.strings.add(orth)
@@ -431,7 +431,7 @@ cdef class Vocab:
             it doesn't exist.
         exclude (list): String names of serialization fields to exclude.
 
-        DOCS: https://spacy.io/api/vocab#to_disk
+        DOCS: https://nightly.spacy.io/api/vocab#to_disk
         """
         path = util.ensure_path(path)
         if not path.exists():
@@ -452,7 +452,7 @@ cdef class Vocab:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Vocab): The modified `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#to_disk
+        DOCS: https://nightly.spacy.io/api/vocab#to_disk
         """
         path = util.ensure_path(path)
         getters = ["strings", "vectors"]
@@ -477,7 +477,7 @@ cdef class Vocab:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (bytes): The serialized form of the `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#to_bytes
+        DOCS: https://nightly.spacy.io/api/vocab#to_bytes
         """
         def deserialize_vectors():
             if self.vectors is None:
@@ -499,7 +499,7 @@ cdef class Vocab:
         exclude (list): String names of serialization fields to exclude.
         RETURNS (Vocab): The `Vocab` object.
 
-        DOCS: https://spacy.io/api/vocab#from_bytes
+        DOCS: https://nightly.spacy.io/api/vocab#from_bytes
         """
         def serialize_vectors(b):
             if self.vectors is None:
diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md
index 3089fa1b3..ee844d961 100644
--- a/website/docs/api/architectures.md
+++ b/website/docs/api/architectures.md
@@ -25,36 +25,6 @@ usage documentation on
 
 ## Tok2Vec architectures {#tok2vec-arch source="spacy/ml/models/tok2vec.py"}
 
-### spacy.HashEmbedCNN.v1 {#HashEmbedCNN}
-
-> #### Example Config
->
-> ```ini
-> [model]
-> @architectures = "spacy.HashEmbedCNN.v1"
-> pretrained_vectors = null
-> width = 96
-> depth = 4
-> embed_size = 2000
-> window_size = 1
-> maxout_pieces = 3
-> subword_features = true
-> ```
-
-Build spaCy's "standard" embedding layer, which uses hash embedding with subword
-features and a CNN with layer-normalized maxout.
-
-| Name                 | Description                                                                                                                                                                                                                                                                   |
-| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `width`              | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~                                                                                                          |
-| `depth`              | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~                                                                                                                                                                                |
-| `embed_size`         | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~                                                                                            |
-| `window_size`        | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
-| `maxout_pieces`      | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~                                                                                   |
-| `subword_features`   | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~                                       |
-| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~                                                                                                                                                                                                                                  |
-| **CREATES**          | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                        |
-
 ### spacy.Tok2Vec.v1 {#Tok2Vec}
 
 > #### Example config
@@ -72,7 +42,8 @@ features and a CNN with layer-normalized maxout.
 > # ...
 > ```
 
-Construct a tok2vec model out of embedding and encoding subnetworks. See the
+Construct a tok2vec model out of two subnetworks: one for embedding and one for
+encoding. See the
 ["Embed, Encode, Attend, Predict"](https://explosion.ai/blog/deep-learning-formula-nlp)
 blog post for background.
 
@@ -82,6 +53,39 @@ blog post for background.
 | `encode`    | Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder). ~~Model[List[Floats2d], List[Floats2d]]~~            |
 | **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                           |
 
+### spacy.HashEmbedCNN.v1 {#HashEmbedCNN}
+
+> #### Example Config
+>
+> ```ini
+> [model]
+> @architectures = "spacy.HashEmbedCNN.v1"
+> pretrained_vectors = null
+> width = 96
+> depth = 4
+> embed_size = 2000
+> window_size = 1
+> maxout_pieces = 3
+> subword_features = true
+> ```
+
+Build spaCy's "standard" tok2vec layer. This layer is defined by a
+[MultiHashEmbed](/api/architectures#MultiHashEmbed) embedding layer that uses
+subword features, and a
+[MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder) encoding layer
+consisting of a CNN and a layer-normalized maxout activation function.
+
+| Name                 | Description                                                                                                                                                                                                                                                                   |
+| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `width`              | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~                                                                                                          |
+| `depth`              | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~                                                                                                                                                                                |
+| `embed_size`         | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~                                                                                            |
+| `window_size`        | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
+| `maxout_pieces`      | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~                                                                                   |
+| `subword_features`   | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~                                       |
+| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~                                                                                                                                                                                                                                  |
+| **CREATES**          | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                        |
+
 ### spacy.Tok2VecListener.v1 {#Tok2VecListener}
 
 > #### Example config
@@ -118,11 +122,11 @@ Instead of defining its own `Tok2Vec` instance, a model architecture like
 [Tagger](/api/architectures#tagger) can define a listener as its `tok2vec`
 argument that connects to the shared `tok2vec` component in the pipeline.
 
-| Name        | Description                                                                                                                                                                                                                                                                                                    |
-| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `width`     | The width of the vectors produced by the "upstream" [`Tok2Vec`](/api/tok2vec) component. ~~int~~                                                                                                                                                                                                               |
-| `upstream`  | A string to identify the "upstream" `Tok2Vec` component to communicate with. The upstream name should either be the wildcard string `"*"`, or the name of the `Tok2Vec` component. You'll almost never have multiple upstream `Tok2Vec` components, so the wildcard string will almost always be fine. ~~str~~ |
-| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                                                         |
+| Name        | Description                                                                                                                                                                                                                                                                                                                          |
+| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `width`     | The width of the vectors produced by the "upstream" [`Tok2Vec`](/api/tok2vec) component. ~~int~~                                                                                                                                                                                                                                     |
+| `upstream`  | A string to identify the "upstream" `Tok2Vec` component to communicate with. By default, the upstream name is the wildcard string `"*"`, but you could also specify the name of the `Tok2Vec` component. You'll almost never have multiple upstream `Tok2Vec` components, so the wildcard string will almost always be fine. ~~str~~ |
+| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~                                                                                                                                                                                                                                                               |
 
 ### spacy.MultiHashEmbed.v1 {#MultiHashEmbed}
 
@@ -316,18 +320,18 @@ for details and system requirements.
 > tokenizer_config = {"use_fast": true}
 >
 > [model.get_spans]
-> @span_getters = "strided_spans.v1"
+> @span_getters = "spacy-transformers.strided_spans.v1"
 > window = 128
 > stride = 96
 > ```
 
 Load and wrap a transformer model from the
 [HuggingFace `transformers`](https://huggingface.co/transformers) library. You
-can any transformer that has pretrained weights and a PyTorch implementation.
-The `name` variable is passed through to the underlying library, so it can be
-either a string or a path. If it's a string, the pretrained weights will be
-downloaded via the transformers library if they are not already available
-locally.
+can use any transformer that has pretrained weights and a PyTorch
+implementation. The `name` variable is passed through to the underlying library,
+so it can be either a string or a path. If it's a string, the pretrained weights
+will be downloaded via the transformers library if they are not already
+available locally.
 
 In order to support longer documents, the
 [TransformerModel](/api/architectures#TransformerModel) layer allows you to pass
@@ -346,13 +350,13 @@ in other components, see
 | `tokenizer_config` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). ~~Dict[str, Any]~~                                                                                   |
 | **CREATES**        | The model using the architecture. ~~Model[List[Doc], FullTransformerBatch]~~                                                                                                                                                                          |
 
-### spacy-transformers.Tok2VecListener.v1 {#transformers-Tok2VecListener}
+### spacy-transformers.TransformerListener.v1 {#TransformerListener}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy-transformers.Tok2VecListener.v1"
+> @architectures = "spacy-transformers.TransformerListener.v1"
 > grad_factor = 1.0
 >
 > [model.pooling]
@@ -669,11 +673,11 @@ into the "real world". This requires 3 main components:
 > subword_features = true
 >
 > [kb_loader]
-> @assets = "spacy.EmptyKB.v1"
+> @misc = "spacy.EmptyKB.v1"
 > entity_vector_length = 64
 >
 > [get_candidates]
-> @assets = "spacy.CandidateGenerator.v1"
+> @misc = "spacy.CandidateGenerator.v1"
 > ```
 
 The `EntityLinker` model architecture is a Thinc `Model` with a
diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
index 9070855fa..7852d0482 100644
--- a/website/docs/api/cli.md
+++ b/website/docs/api/cli.md
@@ -1,6 +1,6 @@
 ---
 title: Command Line Interface
-teaser: Download, train and package models, and debug spaCy
+teaser: Download, train and package pipelines, and debug spaCy
 source: spacy/cli
 menu:
   - ['download', 'download']
@@ -17,45 +17,47 @@ menu:
 ---
 
 spaCy's CLI provides a range of helpful commands for downloading and training
-models, converting data and debugging your config, data and installation. For a
-list of available commands, you can type `python -m spacy --help`. You can also
-add the `--help` flag to any command or subcommand to see the description,
+pipelines, converting data and debugging your config, data and installation. For
+a list of available commands, you can type `python -m spacy --help`. You can
+also add the `--help` flag to any command or subcommand to see the description,
 available arguments and usage.
 
 ## download {#download tag="command"}
 
-Download [models](/usage/models) for spaCy. The downloader finds the
-best-matching compatible version and uses `pip install` to download the model as
-a package. Direct downloads don't perform any compatibility checks and require
-the model name to be specified with its version (e.g. `en_core_web_sm-2.2.0`).
+Download [trained pipelines](/usage/models) for spaCy. The downloader finds the
+best-matching compatible version and uses `pip install` to download the Python
+package. Direct downloads don't perform any compatibility checks and require the
+pipeline name to be specified with its version (e.g. `en_core_web_sm-2.2.0`).
 
 > #### Downloading best practices
 >
 > The `download` command is mostly intended as a convenient, interactive wrapper
 > – it performs compatibility checks and prints detailed messages in case things
 > go wrong. It's **not recommended** to use this command as part of an automated
-> process. If you know which model your project needs, you should consider a
-> [direct download via pip](/usage/models#download-pip), or uploading the model
-> to a local PyPi installation and fetching it straight from there. This will
-> also allow you to add it as a versioned package dependency to your project.
+> process. If you know which package your project needs, you should consider a
+> [direct download via pip](/usage/models#download-pip), or uploading the
+> package to a local PyPi installation and fetching it straight from there. This
+> will also allow you to add it as a versioned package dependency to your
+> project.
 
 ```cli
 $ python -m spacy download [model] [--direct] [pip_args]
 ```
 
-| Name                                  | Description                                                                                                                                                                                                                          |
-| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `model`                               | Model name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). ~~str (positional)~~                                                                                                                                                 |
-| `--direct`, `-d`                      | Force direct download of exact model version. ~~bool (flag)~~                                                                                                                                                                        |
-| `--help`, `-h`                        | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                                           |
-| pip args <Tag variant="new">2.1</Tag> | Additional installation options to be passed to `pip install` when installing the model package. For example, `--user` to install to the user home directory or `--no-deps` to not install model dependencies. ~~Any (option/flag)~~ |
-| **CREATES**                           | The installed model package in your `site-packages` directory.                                                                                                                                                                       |
+| Name                                  | Description                                                                                                                                                                                                                               |
+| ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `model`                               | Pipeline package name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). ~~str (positional)~~                                                                                                                                           |
+| `--direct`, `-d`                      | Force direct download of exact package version. ~~bool (flag)~~                                                                                                                                                                           |
+| `--help`, `-h`                        | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                                                |
+| pip args <Tag variant="new">2.1</Tag> | Additional installation options to be passed to `pip install` when installing the pipeline package. For example, `--user` to install to the user home directory or `--no-deps` to not install package dependencies. ~~Any (option/flag)~~ |
+| **CREATES**                           | The installed pipeline package in your `site-packages` directory.                                                                                                                                                                         |
 
 ## info {#info tag="command"}
 
-Print information about your spaCy installation, models and local setup, and
-generate [Markdown](https://en.wikipedia.org/wiki/Markdown)-formatted markup to
-copy-paste into [GitHub issues](https://github.com/explosion/spaCy/issues).
+Print information about your spaCy installation, trained pipelines and local
+setup, and generate [Markdown](https://en.wikipedia.org/wiki/Markdown)-formatted
+markup to copy-paste into
+[GitHub issues](https://github.com/explosion/spaCy/issues).
 
 ```cli
 $ python -m spacy info [--markdown] [--silent]
@@ -65,41 +67,41 @@ $ python -m spacy info [--markdown] [--silent]
 $ python -m spacy info [model] [--markdown] [--silent]
 ```
 
-| Name                                             | Description                                                                    |
-| ------------------------------------------------ | ------------------------------------------------------------------------------ |
-| `model`                                          | A model, i.e. package name or path (optional). ~~Optional[str] \(positional)~~ |
-| `--markdown`, `-md`                              | Print information as Markdown. ~~bool (flag)~~                                 |
-| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | Don't print anything, just return the values. ~~bool (flag)~~                  |
-| `--help`, `-h`                                   | Show help message and available arguments. ~~bool (flag)~~                     |
-| **PRINTS**                                       | Information about your spaCy installation.                                     |
+| Name                                             | Description                                                                               |
+| ------------------------------------------------ | ----------------------------------------------------------------------------------------- |
+| `model`                                          | A trained pipeline, i.e. package name or path (optional). ~~Optional[str] \(positional)~~ |
+| `--markdown`, `-md`                              | Print information as Markdown. ~~bool (flag)~~                                            |
+| `--silent`, `-s` <Tag variant="new">2.0.12</Tag> | Don't print anything, just return the values. ~~bool (flag)~~                             |
+| `--help`, `-h`                                   | Show help message and available arguments. ~~bool (flag)~~                                |
+| **PRINTS**                                       | Information about your spaCy installation.                                                |
 
 ## validate {#validate new="2" tag="command"}
 
-Find all models installed in the current environment and check whether they are
-compatible with the currently installed version of spaCy. Should be run after
-upgrading spaCy via `pip install -U spacy` to ensure that all installed models
-are can be used with the new version. It will show a list of models and their
-installed versions. If any model is out of date, the latest compatible versions
-and command for updating are shown.
+Find all trained pipeline packages installed in the current environment and
+check whether they are compatible with the currently installed version of spaCy.
+Should be run after upgrading spaCy via `pip install -U spacy` to ensure that
+all installed packages are can be used with the new version. It will show a list
+of packages and their installed versions. If any package is out of date, the
+latest compatible versions and command for updating are shown.
 
 > #### Automated validation
 >
 > You can also use the `validate` command as part of your build process or test
-> suite, to ensure all models are up to date before proceeding. If incompatible
-> models are found, it will return `1`.
+> suite, to ensure all packages are up to date before proceeding. If
+> incompatible packages are found, it will return `1`.
 
 ```cli
 $ python -m spacy validate
 ```
 
-| Name       | Description                                               |
-| ---------- | --------------------------------------------------------- |
-| **PRINTS** | Details about the compatibility of your installed models. |
+| Name       | Description                                                          |
+| ---------- | -------------------------------------------------------------------- |
+| **PRINTS** | Details about the compatibility of your installed pipeline packages. |
 
 ## init {#init new="3"}
 
 The `spacy init` CLI includes helpful commands for initializing training config
-files and model directories.
+files and pipeline directories.
 
 ### init config {#init-config new="3" tag="command"}
 
@@ -125,7 +127,7 @@ $ python -m spacy init config [output_file] [--lang] [--pipeline] [--optimize] [
 | ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `output_file`      | Path to output `.cfg` file or `-` to write the config to stdout (so you can pipe it forward to a file). Note that if you're writing to stdout, no additional logging info is printed. ~~Path (positional)~~                                                                                                                        |
 | `--lang`, `-l`     | Optional code of the [language](/usage/models#languages) to use. Defaults to `"en"`. ~~str (option)~~                                                                                                                                                                                                                              |
-| `--pipeline`, `-p` | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include in the model. Defaults to `"tagger,parser,ner"`. ~~str (option)~~                                                                                                                                                         |
+| `--pipeline`, `-p` | Comma-separated list of trainable [pipeline components](/usage/processing-pipelines#built-in) to include. Defaults to `"tagger,parser,ner"`. ~~str (option)~~                                                                                                                                                                      |
 | `--optimize`, `-o` | `"efficiency"` or `"accuracy"`. Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters. Defaults to `"efficiency"`. ~~str (option)~~ |
 | `--cpu`, `-C`      | Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. ~~bool (flag)~~                                                                                                                                                                                |
 | `--help`, `-h`     | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                                                                                                                                         |
@@ -165,36 +167,38 @@ $ python -m spacy init fill-config [base_path] [output_file] [--diff]
 | `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~                                                                          |
 | **CREATES**    | Complete and auto-filled config file for training.                                                                                  |
 
-### init model {#init-model new="2" tag="command"}
+### init vocab {#init-vocab new="3" tag="command"}
 
-Create a new model directory from raw data, like word frequencies, Brown
-clusters and word vectors. Note that in order to populate the model's vocab, you
+Create a blank pipeline directory from raw data, like word frequencies, Brown
+clusters and word vectors. Note that in order to populate the vocabulary, you
 need to pass in a JSONL-formatted
 [vocabulary file](/api/data-formats#vocab-jsonl) as `--jsonl-loc` with optional
 `id` values that correspond to the vectors table. Just loading in vectors will
 not automatically populate the vocab.
 
-<Infobox title="New in v3.0" variant="warning">
+<Infobox title="New in v3.0" variant="warning" id="init-model">
 
-The `init-model` command is now available as a subcommand of `spacy init`.
+This command was previously called `init-model`.
 
 </Infobox>
 
 ```cli
-$ python -m spacy init model [lang] [output_dir] [--jsonl-loc] [--vectors-loc] [--prune-vectors]
+$ python -m spacy init vocab [lang] [output_dir] [--jsonl-loc] [--vectors-loc] [--prune-vectors] [--vectors-name] [--meta-name] [--base]
 ```
 
 | Name                                                    | Description                                                                                                                                                                                                                                                                         |
 | ------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `lang`                                                  | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. ~~str (positional)~~                                                                                                                                                                   |
-| `output_dir`                                            | Model output directory. Will be created if it doesn't exist. ~~Path (positional)~~                                                                                                                                                                                                  |
+| `lang`                                                  | Pipeline language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes), e.g. `en`. ~~str (positional)~~                                                                                                                                                                |
+| `output_dir`                                            | Pipeline output directory. Will be created if it doesn't exist. ~~Path (positional)~~                                                                                                                                                                                               |
 | `--jsonl-loc`, `-j`                                     | Optional location of JSONL-formatted [vocabulary file](/api/data-formats#vocab-jsonl) with lexical attributes. ~~Optional[Path] \(option)~~                                                                                                                                         |
 | `--vectors-loc`, `-v`                                   | Optional location of vectors. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in `.txt` format or as a zipped text file in `.zip` or `.tar.gz` format. ~~Optional[Path] \(option)~~ |
 | `--truncate-vectors`, `-t` <Tag variant="new">2.3</Tag> | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. ~~int (option)~~                                                                                                                                                                  |
 | `--prune-vectors`, `-V`                                 | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. ~~int (option)~~                                                                                                                                                                                     |
-| `--vectors-name`, `-vn`                                 | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~str (option)~~                                                                                                                                                                              |
+| `--vectors-name`, `-vn`                                 | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~Optional[str] \(option)~~                                                                                                                                                                   |
+| `--meta-name`, `-mn`                                    | Optional name of the package for the pipeline meta. ~~Optional[str] \(option)~~                                                                                                                                                                                                     |
+| `--base`, `-b`                                          | Optional name of or path to base pipeline to start with (mostly relevant for pipelines with custom tokenizers). ~~Optional[str] \(option)~~                                                                                                                                         |
 | `--help`, `-h`                                          | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                                                                                          |
-| **CREATES**                                             | A spaCy model containing the vocab and vectors.                                                                                                                                                                                                                                     |
+| **CREATES**                                             | A spaCy pipeline directory containing the vocab and vectors.                                                                                                                                                                                                                        |
 
 ## convert {#convert tag="command"}
 
@@ -205,7 +209,7 @@ management functions. The converter can be specified on the command line, or
 chosen based on the file extension of the input file.
 
 ```cli
-$ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type] [--n-sents] [--seg-sents] [--model] [--morphology] [--merge-subtokens] [--ner-map] [--lang]
+$ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type] [--n-sents] [--seg-sents] [--base] [--morphology] [--merge-subtokens] [--ner-map] [--lang]
 ```
 
 | Name                                             | Description                                                                                                                               |
@@ -216,7 +220,7 @@ $ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type]
 | `--file-type`, `-t` <Tag variant="new">2.1</Tag> | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
 | `--n-sents`, `-n`                                | Number of sentences per document. ~~int (option)~~                                                                                        |
 | `--seg-sents`, `-s` <Tag variant="new">2.2</Tag> | Segment sentences (for `--converter ner`). ~~bool (flag)~~                                                                                |
-| `--model`, `-b` <Tag variant="new">2.2</Tag>     | Model for parser-based sentence segmentation (for `--seg-sents`). ~~Optional[str](option)~~                                               |
+| `--base`, `-b`                                   | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~                            |
 | `--morphology`, `-m`                             | Enable appending morphology to tags. ~~bool (flag)~~                                                                                      |
 | `--ner-map`, `-nm`                               | NER tag mapping (as JSON-encoded dict of entity types). ~~Optional[Path](option)~~                                                        |
 | `--lang`, `-l` <Tag variant="new">2.1</Tag>      | Language code (if tokenizer required). ~~Optional[str] \(option)~~                                                                        |
@@ -267,7 +271,7 @@ training -> dropout     field required
 training -> optimizer   field required
 training -> optimize    extra fields not permitted
 
-{'vectors': 'en_vectors_web_lg', 'seed': 0, 'accumulate_gradient': 1, 'init_tok2vec': None, 'raw_text': None, 'patience': 1600, 'max_epochs': 0, 'max_steps': 20000, 'eval_frequency': 200, 'frozen_components': [], 'optimize': None, 'batcher': {'@batchers': 'batch_by_words.v1', 'discard_oversize': False, 'tolerance': 0.2, 'get_length': None, 'size': {'@schedules': 'compounding.v1', 'start': 100, 'stop': 1000, 'compound': 1.001, 't': 0.0}}, 'dev_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}, 'score_weights': {'tag_acc': 0.5, 'dep_uas': 0.25, 'dep_las': 0.25, 'sents_f': 0.0}, 'train_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}}
+{'vectors': 'en_vectors_web_lg', 'seed': 0, 'accumulate_gradient': 1, 'init_tok2vec': None, 'raw_text': None, 'patience': 1600, 'max_epochs': 0, 'max_steps': 20000, 'eval_frequency': 200, 'frozen_components': [], 'optimize': None, 'batcher': {'@batchers': 'spacy.batch_by_words.v1', 'discard_oversize': False, 'tolerance': 0.2, 'get_length': None, 'size': {'@schedules': 'compounding.v1', 'start': 100, 'stop': 1000, 'compound': 1.001, 't': 0.0}}, 'dev_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}, 'score_weights': {'tag_acc': 0.5, 'dep_uas': 0.25, 'dep_las': 0.25, 'sents_f': 0.0}, 'train_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}}
 
 If your config contains missing values, you can run the 'init fill-config'
 command to fill in all the defaults, if possible:
@@ -357,7 +361,7 @@ Module     spacy.gold.loggers
 File       /path/to/spacy/gold/loggers.py (line 8)
 ℹ [training.batcher]
 Registry   @batchers
-Name       batch_by_words.v1
+Name       spacy.batch_by_words.v1
 Module     spacy.gold.batchers
 File       /path/to/spacy/gold/batchers.py (line 49)
 ℹ [training.batcher.size]
@@ -594,11 +598,11 @@ $ python -m spacy debug profile [model] [inputs] [--n-texts]
 
 | Name              | Description                                                                        |
 | ----------------- | ---------------------------------------------------------------------------------- |
-| `model`           | A loadable spaCy model. ~~str (positional)~~                                       |
+| `model`           | A loadable spaCy pipeline (package name or path). ~~str (positional)~~             |
 | `inputs`          | Optional path to input file, or `-` for standard input. ~~Path (positional)~~      |
 | `--n-texts`, `-n` | Maximum number of texts to use if available. Defaults to `10000`. ~~int (option)~~ |
 | `--help`, `-h`    | Show help message and available arguments. ~~bool (flag)~~                         |
-| **PRINTS**        | Profiling information for the model.                                               |
+| **PRINTS**        | Profiling information for the pipeline.                                            |
 
 ### debug model {#debug-model new="3" tag="command"}
 
@@ -724,10 +728,10 @@ $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P
 
 ## train {#train tag="command"}
 
-Train a model. Expects data in spaCy's
+Train a pipeline. Expects data in spaCy's
 [binary format](/api/data-formats#training) and a
 [config file](/api/data-formats#config) with all settings and hyperparameters.
-Will save out the best model from all epochs, as well as the final model. The
+Will save out the best model from all epochs, as well as the final pipeline. The
 `--code` argument can be used to provide a Python file that's imported before
 the training process starts. This lets you register
 [custom functions](/usage/training#custom-functions) and architectures and refer
@@ -753,12 +757,12 @@ $ python -m spacy train [config_path] [--output] [--code] [--verbose] [overrides
 | Name              | Description                                                                                                                                                                                |
 | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `config_path`     | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~                                                                |
-| `--output`, `-o`  | Directory to store model in. Will be created if it doesn't exist. ~~Optional[Path] \(positional)~~                                                                                         |
+| `--output`, `-o`  | Directory to store trained pipeline in. Will be created if it doesn't exist. ~~Optional[Path] \(positional)~~                                                                              |
 | `--code`, `-c`    | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~       |
 | `--verbose`, `-V` | Show more detailed messages during training. ~~bool (flag)~~                                                                                                                               |
 | `--help`, `-h`    | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                 |
 | overrides         | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
-| **CREATES**       | The final model and the best model.                                                                                                                                                        |
+| **CREATES**       | The final trained pipeline and the best trained pipeline.                                                                                                                                  |
 
 ## pretrain {#pretrain new="2.1" tag="command,experimental"}
 
@@ -769,7 +773,7 @@ a component like a CNN, BiLSTM, etc to predict vectors which match the
 pretrained ones. The weights are saved to a directory after each epoch. You can
 then include a **path to one of these pretrained weights files** in your
 [training config](/usage/training#config) as the `init_tok2vec` setting when you
-train your model. This technique may be especially helpful if you have little
+train your pipeline. This technique may be especially helpful if you have little
 labelled data. See the usage docs on [pretraining](/usage/training#pretraining)
 for more info.
 
@@ -792,7 +796,7 @@ $ python -m spacy pretrain [texts_loc] [output_dir] [config_path] [--code] [--re
 | Name                    | Description                                                                                                                                                                                        |
 | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `texts_loc`             | Path to JSONL file with raw texts to learn from, with text provided as the key `"text"` or tokens as the key `"tokens"`. [See here](/api/data-formats#pretrain) for details. ~~Path (positional)~~ |
-| `output_dir`            | Directory to write models to on each epoch. ~~Path (positional)~~                                                                                                                                  |
+| `output_dir`            | Directory to save binary weights to on each epoch. ~~Path (positional)~~                                                                                                                           |
 | `config_path`           | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. ~~Path (positional)~~                                                                        |
 | `--code`, `-c`          | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~               |
 | `--resume-path`, `-r`   | Path to pretrained weights from which to resume pretraining. ~~Optional[Path] \(option)~~                                                                                                          |
@@ -803,7 +807,8 @@ $ python -m spacy pretrain [texts_loc] [output_dir] [config_path] [--code] [--re
 
 ## evaluate {#evaluate new="2" tag="command"}
 
-Evaluate a model. Expects a loadable spaCy model and evaluation data in the
+Evaluate a trained pipeline. Expects a loadable spaCy pipeline (package name or
+path) and evaluation data in the
 [binary `.spacy` format](/api/data-formats#binary-training). The
 `--gold-preproc` option sets up the evaluation examples with gold-standard
 sentences and tokens for the predictions. Gold preprocessing helps the
@@ -819,7 +824,7 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--gold-preproc] [--gp
 
 | Name                      | Description                                                                                                                                                               |
 | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `model`                   | Model to evaluate. Can be a package or a path to a model data directory. ~~str (positional)~~                                                                             |
+| `model`                   | Pipeline to evaluate. Can be a package or a path to a data directory. ~~str (positional)~~                                                                                |
 | `data_path`               | Location of evaluation data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~                                                                 |
 | `--output`, `-o`          | Output JSON file for metrics. If not set, no metrics will be exported. ~~Optional[Path] \(option)~~                                                                       |
 | `--gold-preproc`, `-G`    | Use gold preprocessing. ~~bool (flag)~~                                                                                                                                   |
@@ -831,13 +836,12 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--gold-preproc] [--gp
 
 ## package {#package tag="command"}
 
-Generate an installable
-[model Python package](/usage/training#models-generating) from an existing model
-data directory. All data files are copied over. If the path to a
-[`meta.json`](/api/data-formats#meta) is supplied, or a `meta.json` is found in
-the input directory, this file is used. Otherwise, the data can be entered
-directly from the command line. spaCy will then create a `.tar.gz` archive file
-that you can distribute and install with `pip install`.
+Generate an installable [Python package](/usage/training#models-generating) from
+an existing pipeline data directory. All data files are copied over. If the path
+to a [`meta.json`](/api/data-formats#meta) is supplied, or a `meta.json` is
+found in the input directory, this file is used. Otherwise, the data can be
+entered directly from the command line. spaCy will then create a `.tar.gz`
+archive file that you can distribute and install with `pip install`.
 
 <Infobox title="New in v3.0" variant="warning">
 
@@ -855,13 +859,13 @@ $ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta]
 >
 > ```cli
 > $ python -m spacy package /input /output
-> $ cd /output/en_model-0.0.0
-> $ pip install dist/en_model-0.0.0.tar.gz
+> $ cd /output/en_pipeline-0.0.0
+> $ pip install dist/en_pipeline-0.0.0.tar.gz
 > ```
 
 | Name                                             | Description                                                                                                                                                                                                     |
 | ------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `input_dir`                                      | Path to directory containing model data. ~~Path (positional)~~                                                                                                                                                  |
+| `input_dir`                                      | Path to directory containing pipeline data. ~~Path (positional)~~                                                                                                                                               |
 | `output_dir`                                     | Directory to create package folder in. ~~Path (positional)~~                                                                                                                                                    |
 | `--meta-path`, `-m` <Tag variant="new">2</Tag>   | Path to [`meta.json`](/api/data-formats#meta) file (optional). ~~Optional[Path] \(option)~~                                                                                                                     |
 | `--create-meta`, `-C` <Tag variant="new">2</Tag> | Create a `meta.json` file on the command line, even if one already exists in the directory. If an existing file is found, its entries will be shown as the defaults in the command line prompt. ~~bool (flag)~~ |
@@ -869,13 +873,13 @@ $ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta]
 | `--version`, `-v` <Tag variant="new">3</Tag>     | Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. ~~Optional[str] \(option)~~                                                            |
 | `--force`, `-f`                                  | Force overwriting of existing folder in output directory. ~~bool (flag)~~                                                                                                                                       |
 | `--help`, `-h`                                   | Show help message and available arguments. ~~bool (flag)~~                                                                                                                                                      |
-| **CREATES**                                      | A Python package containing the spaCy model.                                                                                                                                                                    |
+| **CREATES**                                      | A Python package containing the spaCy pipeline.                                                                                                                                                                 |
 
 ## project {#project new="3"}
 
 The `spacy project` CLI includes subcommands for working with
 [spaCy projects](/usage/projects), end-to-end workflows for building and
-deploying custom spaCy models.
+deploying custom spaCy pipelines.
 
 ### project clone {#project-clone tag="command"}
 
@@ -1015,9 +1019,9 @@ Download all files or directories listed as `outputs` for commands, unless they
 are not already present locally. When searching for files in the remote, `pull`
 won't just look at the output path, but will also consider the **command
 string** and the **hashes of the dependencies**. For instance, let's say you've
-previously pushed a model checkpoint to the remote, but now you've changed some
+previously pushed a checkpoint to the remote, but now you've changed some
 hyper-parameters. Because you've changed the inputs to the command, if you run
-`pull`, you won't retrieve the stale result. If you train your model and push
+`pull`, you won't retrieve the stale result. If you train your pipeline and push
 the outputs to the remote, the outputs will be saved alongside the prior
 outputs, so if you change the config back, you'll be able to fetch back the
 result.
diff --git a/website/docs/api/data-formats.md b/website/docs/api/data-formats.md
index 8ef8041ee..3fd2818f4 100644
--- a/website/docs/api/data-formats.md
+++ b/website/docs/api/data-formats.md
@@ -6,18 +6,18 @@ menu:
   - ['Training Data', 'training']
   - ['Pretraining Data', 'pretraining']
   - ['Vocabulary', 'vocab-jsonl']
-  - ['Model Meta', 'meta']
+  - ['Pipeline Meta', 'meta']
 ---
 
 This section documents input and output formats of data used by spaCy, including
 the [training config](/usage/training#config), training data and lexical
 vocabulary data. For an overview of label schemes used by the models, see the
-[models directory](/models). Each model documents the label schemes used in its
-components, depending on the data it was trained on.
+[models directory](/models). Each trained pipeline documents the label schemes
+used in its components, depending on the data it was trained on.
 
 ## Training config {#config new="3"}
 
-Config files define the training process and model pipeline and can be passed to
+Config files define the training process and pipeline and can be passed to
 [`spacy train`](/api/cli#train). They use
 [Thinc's configuration system](https://thinc.ai/docs/usage-config) under the
 hood. For details on how to use training configs, see the
@@ -74,16 +74,16 @@ your config and check that it's valid, you can run the
 Defines the `nlp` object, its tokenizer and
 [processing pipeline](/usage/processing-pipelines) component names.
 
-| Name                      | Description                                                                                                                                                                                                                                                                                          |
-| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `lang`                    | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `null`. ~~str~~                                                                                                                                                                                        |
-| `pipeline`                | Names of pipeline components in order. Should correspond to sections in the `[components]` block, e.g. `[components.ner]`. See docs on [defining components](/usage/training#config-components). Defaults to `[]`. ~~List[str]~~                                                                     |
-| `disabled`                | Names of pipeline components that are loaded but disabled by default and not run as part of the pipeline. Should correspond to components listed in `pipeline`. After a model is loaded, disabled components can be enabled using [`Language.enable_pipe`](/api/language#enable_pipe). ~~List[str]~~ |
-| `load_vocab_data`         | Whether to load additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) if available. Defaults to `true`. ~~bool~~                                                                                                                             |
-| `before_creation`         | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `Language` subclass before it's initialized. Defaults to `null`. ~~Optional[Callable[[Type[Language]], Type[Language]]]~~                                                                                                   |
-| `after_creation`          | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object right after it's initialized. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~                                                                                                                 |
-| `after_pipeline_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object after the pipeline components have been added. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~                                                                                                |
-| `tokenizer`               | The tokenizer to use. Defaults to [`Tokenizer`](/api/tokenizer). ~~Callable[[str], Doc]~~                                                                                                                                                                                                            |
+| Name                      | Description                                                                                                                                                                                                                                                                                             |
+| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lang`                    | Pipeline language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `null`. ~~str~~                                                                                                                                                                                        |
+| `pipeline`                | Names of pipeline components in order. Should correspond to sections in the `[components]` block, e.g. `[components.ner]`. See docs on [defining components](/usage/training#config-components). Defaults to `[]`. ~~List[str]~~                                                                        |
+| `disabled`                | Names of pipeline components that are loaded but disabled by default and not run as part of the pipeline. Should correspond to components listed in `pipeline`. After a pipeline is loaded, disabled components can be enabled using [`Language.enable_pipe`](/api/language#enable_pipe). ~~List[str]~~ |
+| `load_vocab_data`         | Whether to load additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) if available. Defaults to `true`. ~~bool~~                                                                                                                                |
+| `before_creation`         | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `Language` subclass before it's initialized. Defaults to `null`. ~~Optional[Callable[[Type[Language]], Type[Language]]]~~                                                                                                      |
+| `after_creation`          | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object right after it's initialized. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~                                                                                                                    |
+| `after_pipeline_creation` | Optional [callback](/usage/training#custom-code-nlp-callbacks) to modify `nlp` object after the pipeline components have been added. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~                                                                                                   |
+| `tokenizer`               | The tokenizer to use. Defaults to [`Tokenizer`](/api/tokenizer). ~~Callable[[str], Doc]~~                                                                                                                                                                                                               |
 
 ### components {#config-components tag="section"}
 
@@ -105,8 +105,8 @@ This section includes definitions of the
 [pipeline components](/usage/processing-pipelines) and their models, if
 available. Components in this section can be referenced in the `pipeline` of the
 `[nlp]` block. Component blocks need to specify either a `factory` (named
-function to use to create component) or a `source` (name of path of pretrained
-model to copy components from). See the docs on
+function to use to create component) or a `source` (name of path of trained
+pipeline to copy components from). See the docs on
 [defining pipeline components](/usage/training#config-components) for details.
 
 ### paths, system {#config-variables tag="variables"}
@@ -145,7 +145,7 @@ process that are used when you run [`spacy train`](/api/cli#train).
 | `score_weights`       | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~                                |
 | `seed`                | The random seed. Defaults to variable `${system.seed}`. ~~int~~                                                                                                                                              |
 | `train_corpus`        | Callable that takes the current `nlp` object and yields [`Example`](/api/example) objects. Defaults to [`Corpus`](/api/corpus). ~~Callable[[Language], Iterator[Example]]~~                                  |
-| `vectors`             | Model name or path to model containing pretrained word vectors to use, e.g. created with [`init model`](/api/cli#init-model). Defaults to `null`. ~~Optional[str]~~                                          |
+| `vectors`             | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vocab`](/api/cli#init-vocab). Defaults to `null`. ~~Optional[str]~~                                             |
 
 ### pretraining {#config-pretraining tag="section,optional"}
 
@@ -184,7 +184,7 @@ run [`spacy pretrain`](/api/cli#pretrain).
 
 The main data format used in spaCy v3.0 is a **binary format** created by
 serializing a [`DocBin`](/api/docbin), which represents a collection of `Doc`
-objects. This means that you can train spaCy models using the same format it
+objects. This means that you can train spaCy pipelines using the same format it
 outputs: annotated `Doc` objects. The binary format is extremely **efficient in
 storage**, especially when packing multiple documents together.
 
@@ -286,8 +286,8 @@ a dictionary of gold-standard annotations.
 [internal training API](/usage/training#api) and they're expected when you call
 [`nlp.update`](/api/language#update). However, for most use cases, you
 **shouldn't** have to write your own training scripts. It's recommended to train
-your models via the [`spacy train`](/api/cli#train) command with a config file
-to keep track of your settings and hyperparameters and your own
+your pipelines via the [`spacy train`](/api/cli#train) command with a config
+file to keep track of your settings and hyperparameters and your own
 [registered functions](/usage/training/#custom-code) to customize the setup.
 
 </Infobox>
@@ -406,15 +406,15 @@ in line-by-line, while still making it easy to represent newlines in the data.
 
 ## Lexical data for vocabulary {#vocab-jsonl new="2"}
 
-To populate a model's vocabulary, you can use the
-[`spacy init model`](/api/cli#init-model) command and load in a
+To populate a pipeline's vocabulary, you can use the
+[`spacy init vocab`](/api/cli#init-vocab) command and load in a
 [newline-delimited JSON](http://jsonlines.org/) (JSONL) file containing one
 lexical entry per line via the `--jsonl-loc` option. The first line defines the
 language and vocabulary settings. All other lines are expected to be JSON
 objects describing an individual lexeme. The lexical attributes will be then set
 as attributes on spaCy's [`Lexeme`](/api/lexeme#attributes) object. The `vocab`
-command outputs a ready-to-use spaCy model with a `Vocab` containing the lexical
-data.
+command outputs a ready-to-use spaCy pipeline with a `Vocab` containing the
+lexical data.
 
 ```python
 ### First line
@@ -459,11 +459,11 @@ Here's an example of the 20 most frequent lexemes in the English training data:
 https://github.com/explosion/spaCy/tree/master/examples/training/vocab-data.jsonl
 ```
 
-## Model meta {#meta}
+## Pipeline meta {#meta}
 
-The model meta is available as the file `meta.json` and exported automatically
-when you save an `nlp` object to disk. Its contents are available as
-[`nlp.meta`](/api/language#meta).
+The pipeline meta is available as the file `meta.json` and exported
+automatically when you save an `nlp` object to disk. Its contents are available
+as [`nlp.meta`](/api/language#meta).
 
 <Infobox variant="warning" title="Changed in v3.0">
 
@@ -473,8 +473,8 @@ creating a Python package with [`spacy package`](/api/cli#package). How to set
 up the `nlp` object is now defined in the
 [`config.cfg`](/api/data-formats#config), which includes detailed information
 about the pipeline components and their model architectures, and all other
-settings and hyperparameters used to train the model. It's the **single source
-of truth** used for loading a model.
+settings and hyperparameters used to train the pipeline. It's the **single
+source of truth** used for loading a pipeline.
 
 </Infobox>
 
@@ -482,12 +482,12 @@ of truth** used for loading a model.
 >
 > ```json
 > {
->   "name": "example_model",
+>   "name": "example_pipeline",
 >   "lang": "en",
 >   "version": "1.0.0",
 >   "spacy_version": ">=3.0.0,<3.1.0",
 >   "parent_package": "spacy",
->   "description": "Example model for spaCy",
+>   "description": "Example pipeline for spaCy",
 >   "author": "You",
 >   "email": "you@example.com",
 >   "url": "https://example.com",
@@ -510,23 +510,23 @@ of truth** used for loading a model.
 > }
 > ```
 
-| Name                                           | Description                                                                                                                                                                                                                                                                                                              |
-| ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `lang`                                         | Model language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `"en"`. ~~str~~                                                                                                                                                                                                            |
-| `name`                                         | Model name, e.g. `"core_web_sm"`. The final model package name will be `{lang}_{name}`. Defaults to `"model"`. ~~str~~                                                                                                                                                                                                   |
-| `version`                                      | Model version. Will be used to version a Python package created with [`spacy package`](/api/cli#package). Defaults to `"0.0.0"`. ~~str~~                                                                                                                                                                                 |
-| `spacy_version`                                | spaCy version range the model is compatible with. Defaults to the spaCy version used to create the model, up to next minor version, which is the default compatibility for the available [pretrained models](/models). For instance, a model trained with v3.0.0 will have the version range `">=3.0.0,<3.1.0"`. ~~str~~ |
-| `parent_package`                               | Name of the spaCy package. Typically `"spacy"` or `"spacy_nightly"`. Defaults to `"spacy"`. ~~str~~                                                                                                                                                                                                                      |
-| `description`                                  | Model description. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                               |
-| `author`                                       | Model author name. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                               |
-| `email`                                        | Model author email. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                              |
-| `url`                                          | Model author URL. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                |
-| `license`                                      | Model license. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                   |
-| `sources`                                      | Data sources used to train the model. Typically a list of dicts with the keys `"name"`, `"url"`, `"author"` and `"license"`. [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `None`. ~~Optional[List[Dict[str, str]]]~~                                                 |
-| `vectors`                                      | Information about the word vectors included with the model. Typically a dict with the keys `"width"`, `"vectors"` (number of vectors), `"keys"` and `"name"`. ~~Dict[str, Any]~~                                                                                                                                         |
-| `pipeline`                                     | Names of pipeline component names in the model, in order. Corresponds to [`nlp.pipe_names`](/api/language#pipe_names). Only exists for reference and is not used to create the components. This information is defined in the [`config.cfg`](/api/data-formats#config). Defaults to `[]`. ~~List[str]~~                  |
-| `labels`                                       | Label schemes of the trained pipeline components, keyed by component name. Corresponds to [`nlp.pipe_labels`](/api/language#pipe_labels). [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `{}`. ~~Dict[str, Dict[str, List[str]]]~~                                     |
-| `accuracy`                                     | Training accuracy, added automatically by [`spacy train`](/api/cli#train). Dictionary of [score names](/usage/training#metrics) mapped to scores. Defaults to `{}`. ~~Dict[str, Union[float, Dict[str, float]]]~~                                                                                                        |
-| `speed`                                        | Model speed, added automatically by [`spacy train`](/api/cli#train). Typically a dictionary with the keys `"cpu"`, `"gpu"` and `"nwords"` (words per second). Defaults to `{}`. ~~Dict[str, Optional[Union[float, str]]]~~                                                                                               |
-| `spacy_git_version` <Tag variant="new">3</Tag> | Git commit of [`spacy`](https://github.com/explosion/spaCy) used to create model. ~~str~~                                                                                                                                                                                                                                |
-| other                                          | Any other custom meta information you want to add. The data is preserved in [`nlp.meta`](/api/language#meta). ~~Any~~                                                                                                                                                                                                    |
+| Name                                           | Description                                                                                                                                                                                                                                                                                                                      |
+| ---------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `lang`                                         | Pipeline language [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes). Defaults to `"en"`. ~~str~~                                                                                                                                                                                                                 |
+| `name`                                         | Pipeline name, e.g. `"core_web_sm"`. The final package name will be `{lang}_{name}`. Defaults to `"pipeline"`. ~~str~~                                                                                                                                                                                                           |
+| `version`                                      | Pipeline version. Will be used to version a Python package created with [`spacy package`](/api/cli#package). Defaults to `"0.0.0"`. ~~str~~                                                                                                                                                                                      |
+| `spacy_version`                                | spaCy version range the package is compatible with. Defaults to the spaCy version used to create the pipeline, up to next minor version, which is the default compatibility for the available [trained pipelines](/models). For instance, a pipeline trained with v3.0.0 will have the version range `">=3.0.0,<3.1.0"`. ~~str~~ |
+| `parent_package`                               | Name of the spaCy package. Typically `"spacy"` or `"spacy_nightly"`. Defaults to `"spacy"`. ~~str~~                                                                                                                                                                                                                              |
+| `description`                                  | Pipeline description. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                    |
+| `author`                                       | Pipeline author name. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                    |
+| `email`                                        | Pipeline author email. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                   |
+| `url`                                          | Pipeline author URL. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                     |
+| `license`                                      | Pipeline license. Also used for Python package. Defaults to `""`. ~~str~~                                                                                                                                                                                                                                                        |
+| `sources`                                      | Data sources used to train the pipeline. Typically a list of dicts with the keys `"name"`, `"url"`, `"author"` and `"license"`. [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `None`. ~~Optional[List[Dict[str, str]]]~~                                                      |
+| `vectors`                                      | Information about the word vectors included with the pipeline. Typically a dict with the keys `"width"`, `"vectors"` (number of vectors), `"keys"` and `"name"`. ~~Dict[str, Any]~~                                                                                                                                              |
+| `pipeline`                                     | Names of pipeline component names, in order. Corresponds to [`nlp.pipe_names`](/api/language#pipe_names). Only exists for reference and is not used to create the components. This information is defined in the [`config.cfg`](/api/data-formats#config). Defaults to `[]`. ~~List[str]~~                                       |
+| `labels`                                       | Label schemes of the trained pipeline components, keyed by component name. Corresponds to [`nlp.pipe_labels`](/api/language#pipe_labels). [See here](https://github.com/explosion/spacy-models/tree/master/meta) for examples. Defaults to `{}`. ~~Dict[str, Dict[str, List[str]]]~~                                             |
+| `accuracy`                                     | Training accuracy, added automatically by [`spacy train`](/api/cli#train). Dictionary of [score names](/usage/training#metrics) mapped to scores. Defaults to `{}`. ~~Dict[str, Union[float, Dict[str, float]]]~~                                                                                                                |
+| `speed`                                        | Inference speed, added automatically by [`spacy train`](/api/cli#train). Typically a dictionary with the keys `"cpu"`, `"gpu"` and `"nwords"` (words per second). Defaults to `{}`. ~~Dict[str, Optional[Union[float, str]]]~~                                                                                                   |
+| `spacy_git_version` <Tag variant="new">3</Tag> | Git commit of [`spacy`](https://github.com/explosion/spaCy) used to create pipeline. ~~str~~                                                                                                                                                                                                                                     |
+| other                                          | Any other custom meta information you want to add. The data is preserved in [`nlp.meta`](/api/language#meta). ~~Any~~                                                                                                                                                                                                            |
diff --git a/website/docs/api/dependencymatcher.md b/website/docs/api/dependencymatcher.md
index 2fb903100..b0395cc42 100644
--- a/website/docs/api/dependencymatcher.md
+++ b/website/docs/api/dependencymatcher.md
@@ -9,8 +9,8 @@ The `DependencyMatcher` follows the same API as the [`Matcher`](/api/matcher)
 and [`PhraseMatcher`](/api/phrasematcher) and lets you match on dependency trees
 using the
 [Semgrex syntax](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/semgraph/semgrex/SemgrexPattern.html).
-It requires a pretrained [`DependencyParser`](/api/parser) or other component
-that sets the `Token.dep` attribute.
+It requires a trained [`DependencyParser`](/api/parser) or other component that
+sets the `Token.dep` attribute.
 
 ## Pattern format {#patterns}
 
diff --git a/website/docs/api/entitylinker.md b/website/docs/api/entitylinker.md
index 679c3c0c2..8cde6c490 100644
--- a/website/docs/api/entitylinker.md
+++ b/website/docs/api/entitylinker.md
@@ -13,8 +13,8 @@ An `EntityLinker` component disambiguates textual mentions (tagged as named
 entities) to unique identifiers, grounding the named entities into the "real
 world". It requires a `KnowledgeBase`, as well as a function to generate
 plausible candidates from that `KnowledgeBase` given a certain textual mention,
-and a ML model to pick the right candidate, given the local context of the
-mention.
+and a machine learning model to pick the right candidate, given the local
+context of the mention.
 
 ## Config and implementation {#config}
 
@@ -34,8 +34,8 @@ architectures and their arguments and hyperparameters.
 >    "incl_prior": True,
 >    "incl_context": True,
 >    "model": DEFAULT_NEL_MODEL,
->    "kb_loader": {'@assets': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
->    "get_candidates": {'@assets': 'spacy.CandidateGenerator.v1'},
+>    "kb_loader": {'@misc': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
+>    "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
 > }
 > nlp.add_pipe("entity_linker", config=config)
 > ```
@@ -66,7 +66,7 @@ https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/entity_linker.py
 > entity_linker = nlp.add_pipe("entity_linker", config=config)
 >
 > # Construction via add_pipe with custom KB and candidate generation
-> config = {"kb": {"@assets": "my_kb.v1"}}
+> config = {"kb": {"@misc": "my_kb.v1"}}
 > entity_linker = nlp.add_pipe("entity_linker", config=config)
 >
 > # Construction from class
diff --git a/website/docs/api/language.md b/website/docs/api/language.md
index e2668c522..7799f103b 100644
--- a/website/docs/api/language.md
+++ b/website/docs/api/language.md
@@ -7,9 +7,9 @@ source: spacy/language.py
 
 Usually you'll load this once per process as `nlp` and pass the instance around
 your application. The `Language` class is created when you call
-[`spacy.load()`](/api/top-level#spacy.load) and contains the shared vocabulary
-and [language data](/usage/adding-languages), optional model data loaded from a
-[model package](/models) or a path, and a
+[`spacy.load`](/api/top-level#spacy.load) and contains the shared vocabulary and
+[language data](/usage/adding-languages), optional binary weights, e.g. provided
+by a [trained pipeline](/models), and the
 [processing pipeline](/usage/processing-pipelines) containing components like
 the tagger or parser that are called on a document in order. You can also add
 your own processing pipeline components that take a `Doc` object, modify it and
@@ -37,7 +37,7 @@ Initialize a `Language` object.
 | `vocab`            | A `Vocab` object. If `True`, a vocab is created using the default language data settings. ~~Vocab~~                      |
 | _keyword-only_     |                                                                                                                          |
 | `max_length`       | Maximum number of characters allowed in a single text. Defaults to `10 ** 6`. ~~int~~                                    |
-| `meta`             | Custom meta data for the `Language` class. Is written to by models to add model meta data. ~~dict~~                      |
+| `meta`             | Custom meta data for the `Language` class. Is written to by pipelines to add meta data. ~~dict~~                         |
 | `create_tokenizer` | Optional function that receives the `nlp` object and returns a tokenizer. ~~Callable[[Language], Callable[[str], Doc]]~~ |
 
 ## Language.from_config {#from_config tag="classmethod" new="3"}
@@ -232,7 +232,7 @@ tuples of `Doc` and `GoldParse` objects.
 
 ## Language.resume_training {#resume_training tag="method,experimental" new="3"}
 
-Continue training a pretrained model. Create and return an optimizer, and
+Continue training a trained pipeline. Create and return an optimizer, and
 initialize "rehearsal" for any pipeline component that has a `rehearse` method.
 Rehearsal is used to prevent models from "forgetting" their initialized
 "knowledge". To perform rehearsal, collect samples of text you want the models
@@ -314,7 +314,7 @@ the "catastrophic forgetting" problem. This feature is experimental.
 
 ## Language.evaluate {#evaluate tag="method"}
 
-Evaluate a model's pipeline components.
+Evaluate a pipeline's components.
 
 <Infobox variant="warning" title="Changed in v3.0">
 
@@ -386,24 +386,24 @@ component, adds it to the pipeline and returns it.
 > nlp.add_pipe("component", before="ner")
 > component = nlp.add_pipe("component", name="custom_name", last=True)
 >
-> # Add component from source model
+> # Add component from source pipeline
 > source_nlp = spacy.load("en_core_web_sm")
 > nlp.add_pipe("ner", source=source_nlp)
 > ```
 
-| Name                                  | Description                                                                                                                                                                                                                                                                     |
-| ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `factory_name`                        | Name of the registered component factory. ~~str~~                                                                                                                                                                                                                               |
-| `name`                                | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~                                                                                                     |
-| _keyword-only_                        |                                                                                                                                                                                                                                                                                 |
-| `before`                              | Component name or index to insert component directly before. ~~Optional[Union[str, int]]~~                                                                                                                                                                                      |
-| `after`                               | Component name or index to insert component directly after. ~~Optional[Union[str, int]]~~                                                                                                                                                                                       |
-| `first`                               | Insert component first / not first in the pipeline. ~~Optional[bool]~~                                                                                                                                                                                                          |
-| `last`                                | Insert component last / not last in the pipeline. ~~Optional[bool]~~                                                                                                                                                                                                            |
-| `config` <Tag variant="new">3</Tag>   | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~                                                                                                                 |
-| `source` <Tag variant="new">3</Tag>   | Optional source model to copy component from. If a source is provided, the `factory_name` is interpreted as the name of the component in the source pipeline. Make sure that the vocab, vectors and settings of the source model match the target model. ~~Optional[Language]~~ |
-| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~                                                                                                                                                  |
-| **RETURNS**                           | The pipeline component. ~~Callable[[Doc], Doc]~~                                                                                                                                                                                                                                |
+| Name                                  | Description                                                                                                                                                                                                                                                                              |
+| ------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `factory_name`                        | Name of the registered component factory. ~~str~~                                                                                                                                                                                                                                        |
+| `name`                                | Optional unique name of pipeline component instance. If not set, the factory name is used. An error is raised if the name already exists in the pipeline. ~~Optional[str]~~                                                                                                              |
+| _keyword-only_                        |                                                                                                                                                                                                                                                                                          |
+| `before`                              | Component name or index to insert component directly before. ~~Optional[Union[str, int]]~~                                                                                                                                                                                               |
+| `after`                               | Component name or index to insert component directly after. ~~Optional[Union[str, int]]~~                                                                                                                                                                                                |
+| `first`                               | Insert component first / not first in the pipeline. ~~Optional[bool]~~                                                                                                                                                                                                                   |
+| `last`                                | Insert component last / not last in the pipeline. ~~Optional[bool]~~                                                                                                                                                                                                                     |
+| `config` <Tag variant="new">3</Tag>   | Optional config parameters to use for this component. Will be merged with the `default_config` specified by the component factory. ~~Optional[Dict[str, Any]]~~                                                                                                                          |
+| `source` <Tag variant="new">3</Tag>   | Optional source pipeline to copy component from. If a source is provided, the `factory_name` is interpreted as the name of the component in the source pipeline. Make sure that the vocab, vectors and settings of the source pipeline match the target pipeline. ~~Optional[Language]~~ |
+| `validate` <Tag variant="new">3</Tag> | Whether to validate the component config and arguments against the types expected by the factory. Defaults to `True`. ~~bool~~                                                                                                                                                           |
+| **RETURNS**                           | The pipeline component. ~~Callable[[Doc], Doc]~~                                                                                                                                                                                                                                         |
 
 ## Language.create_pipe {#create_pipe tag="method" new="2"}
 
@@ -790,9 +790,10 @@ token.ent_iob, token.ent_type
 
 ## Language.meta {#meta tag="property"}
 
-Custom meta data for the Language class. If a model is loaded, contains meta
-data of the model. The `Language.meta` is also what's serialized as the
-[`meta.json`](/api/data-formats#meta) when you save an `nlp` object to disk.
+Custom meta data for the Language class. If a trained pipeline is loaded, this
+contains meta data of the pipeline. The `Language.meta` is also what's
+serialized as the [`meta.json`](/api/data-formats#meta) when you save an `nlp`
+object to disk.
 
 > #### Example
 >
@@ -827,13 +828,15 @@ subclass of the built-in `dict`. It supports the additional methods `to_disk`
 
 ## Language.to_disk {#to_disk tag="method" new="2"}
 
-Save the current state to a directory. If a model is loaded, this will **include
-the model**.
+Save the current state to a directory. Under the hood, this method delegates to
+the `to_disk` methods of the individual pipeline components, if available. This
+means that if a trained pipeline is loaded, all components and their weights
+will be saved to disk.
 
 > #### Example
 >
 > ```python
-> nlp.to_disk("/path/to/models")
+> nlp.to_disk("/path/to/pipeline")
 > ```
 
 | Name           | Description                                                                                                                                |
@@ -844,22 +847,28 @@ the model**.
 
 ## Language.from_disk {#from_disk tag="method" new="2"}
 
-Loads state from a directory. Modifies the object in place and returns it. If
-the saved `Language` object contains a model, the model will be loaded. Note
-that this method is commonly used via the subclasses like `English` or `German`
-to make language-specific functionality like the
-[lexical attribute getters](/usage/adding-languages#lex-attrs) available to the
-loaded object.
+Loads state from a directory, including all data that was saved with the
+`Language` object. Modifies the object in place and returns it.
+
+<Infobox variant="warning" title="Important note">
+
+Keep in mind that this method **only loads serialized state** and doesn't set up
+the `nlp` object. This means that it requires the correct language class to be
+initialized and all pipeline components to be added to the pipeline. If you want
+to load a serialized pipeline from a directory, you should use
+[`spacy.load`](/api/top-level#spacy.load), which will set everything up for you.
+
+</Infobox>
 
 > #### Example
 >
 > ```python
 > from spacy.language import Language
-> nlp = Language().from_disk("/path/to/model")
+> nlp = Language().from_disk("/path/to/pipeline")
 >
-> # using language-specific subclass
+> # Using language-specific subclass
 > from spacy.lang.en import English
-> nlp = English().from_disk("/path/to/en_model")
+> nlp = English().from_disk("/path/to/pipeline")
 > ```
 
 | Name           | Description                                                                                                 |
@@ -924,7 +933,7 @@ available to the loaded object.
 | `components` <Tag variant="new">3</Tag>       | List of all available `(name, component)` tuples, including components that are currently disabled. ~~List[Tuple[str, Callable[[Doc], Doc]]]~~ |
 | `component_names` <Tag variant="new">3</Tag>  | List of all available component names, including components that are currently disabled. ~~List[str]~~                                         |
 | `disabled` <Tag variant="new">3</Tag>         | Names of components that are currently disabled and don't run as part of the pipeline. ~~List[str]~~                                           |
-| `path` <Tag variant="new">2</Tag>             | Path to the model data directory, if a model is loaded. Otherwise `None`. ~~Optional[Path]~~                                                   |
+| `path` <Tag variant="new">2</Tag>             | Path to the pipeline data directory, if a pipeline is loaded from a path or package. Otherwise `None`. ~~Optional[Path]~~                      |
 
 ## Class attributes {#class-attributes}
 
@@ -1004,7 +1013,7 @@ serialization by passing in the string names via the `exclude` argument.
 >
 > ```python
 > data = nlp.to_bytes(exclude=["tokenizer", "vocab"])
-> nlp.from_disk("./model-data", exclude=["ner"])
+> nlp.from_disk("/pipeline", exclude=["ner"])
 > ```
 
 | Name        | Description                                                        |
diff --git a/website/docs/api/matcher.md b/website/docs/api/matcher.md
index f259174e2..1f1946be5 100644
--- a/website/docs/api/matcher.md
+++ b/website/docs/api/matcher.md
@@ -116,31 +116,12 @@ Find all token sequences matching the supplied patterns on the `Doc` or `Span`.
 > matches = matcher(doc)
 > ```
 
-| Name        | Description                                                                                                                                                                                             |
-| ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `doclike`   | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~                                                                                                                                                 |
-| **RETURNS** | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
-
-## Matcher.pipe {#pipe tag="method"}
-
-Match a stream of documents, yielding them in turn.
-
-> #### Example
->
-> ```python
-> from spacy.matcher import Matcher
-> matcher = Matcher(nlp.vocab)
-> for doc in matcher.pipe(docs, batch_size=50):
->     pass
-> ```
-
-| Name                                          | Description                                                                                                                                                                                                                         |
-| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `docs`                                        | A stream of documents or spans. ~~Iterable[Union[Doc, Span]]~~                                                                                                                                                                      |
-| `batch_size`                                  | The number of documents to accumulate into a working set. ~~int~~                                                                                                                                                                   |
-| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~                                                                                                                                         |
-| `as_tuples`                                   | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
-| **YIELDS**                                    | Documents, in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~                                                                                                                                                   |
+| Name                                  | Description                                                                                                                                                                                                                                                                                              |
+| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doclike`                             | The `Doc` or `Span` to match over. ~~Union[Doc, Span]~~                                                                                                                                                                                                                                                  |
+| _keyword-only_                        |                                                                                                                                                                                                                                                                                                          |
+| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~                                                                                                                                            |
+| **RETURNS**                           | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
 
 ## Matcher.\_\_len\_\_ {#len tag="method" new="2"}
 
diff --git a/website/docs/api/phrasematcher.md b/website/docs/api/phrasematcher.md
index 143eb9edf..39e3a298b 100644
--- a/website/docs/api/phrasematcher.md
+++ b/website/docs/api/phrasematcher.md
@@ -57,10 +57,12 @@ Find all token sequences matching the supplied patterns on the `Doc`.
 > matches = matcher(doc)
 > ```
 
-| Name        | Description                         |
-| ----------- | ----------------------------------- |
-| `doc`       | The document to match over. ~~Doc~~ |
-| **RETURNS** | list                                | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
+| Name                                  | Description                                                                                                                                                                                                                                                                                              |
+| ------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `doc`                                 | The document to match over. ~~Doc~~                                                                                                                                                                                                                                                                      |
+| _keyword-only_                        |                                                                                                                                                                                                                                                                                                          |
+| `as_spans` <Tag variant="new">3</Tag> | Instead of tuples, return a list of [`Span`](/api/span) objects of the matches, with the `match_id` assigned as the span label. Defaults to `False`. ~~bool~~                                                                                                                                            |
+| **RETURNS**                           | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. The `match_id` is the ID of the added match pattern. If `as_spans` is set to `True`, a list of `Span` objects is returned instead. ~~Union[List[Tuple[int, int, int]], List[Span]]~~ |
 
 <Infobox title="Note on retrieving the string representation of the match_id" variant="warning">
 
@@ -74,27 +76,6 @@ match_id_string = nlp.vocab.strings[match_id]
 
 </Infobox>
 
-## PhraseMatcher.pipe {#pipe tag="method"}
-
-Match a stream of documents, yielding them in turn.
-
-> #### Example
->
-> ```python
->   from spacy.matcher import PhraseMatcher
->   matcher = PhraseMatcher(nlp.vocab)
->   for doc in matcher.pipe(docs, batch_size=50):
->       pass
-> ```
-
-| Name                                          | Description                                                                                                                                                                                                                         |
-| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `docs`                                        | A stream of documents. ~~Iterable[Doc]~~                                                                                                                                                                                            |
-| `batch_size`                                  | The number of documents to accumulate into a working set. ~~int~~                                                                                                                                                                   |
-| `return_matches` <Tag variant="new">2.1</Tag> | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~                                                                                                                                         |
-| `as_tuples`                                   | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
-| **YIELDS**                                    | Documents and optional matches or context in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~                                                                                                                    |
-
 ## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
 
 Get the number of rules added to the matcher. Note that this only returns the
diff --git a/website/docs/api/pipe.md b/website/docs/api/pipe.md
index 9c3a4104e..57b2af44d 100644
--- a/website/docs/api/pipe.md
+++ b/website/docs/api/pipe.md
@@ -286,7 +286,7 @@ context, the original parameters are restored.
 
 ## Pipe.add_label {#add_label tag="method"}
 
-Add a new label to the pipe. It's possible to extend pretrained models with new
+Add a new label to the pipe. It's possible to extend trained models with new
 labels, but care should be taken to avoid the "catastrophic forgetting" problem.
 
 > #### Example
diff --git a/website/docs/api/top-level.md b/website/docs/api/top-level.md
index c2cc5bbab..7f2eb2e66 100644
--- a/website/docs/api/top-level.md
+++ b/website/docs/api/top-level.md
@@ -4,6 +4,7 @@ menu:
   - ['spacy', 'spacy']
   - ['displacy', 'displacy']
   - ['registry', 'registry']
+  - ['Loggers', 'loggers']
   - ['Batchers', 'batchers']
   - ['Data & Alignment', 'gold']
   - ['Utility Functions', 'util']
@@ -11,14 +12,14 @@ menu:
 
 ## spaCy {#spacy hidden="true"}
 
-### spacy.load {#spacy.load tag="function" model="any"}
+### spacy.load {#spacy.load tag="function"}
 
-Load a model using the name of an installed
-[model package](/usage/training#models-generating), a string path or a
-`Path`-like object. spaCy will try resolving the load argument in this order. If
-a model is loaded from a model name, spaCy will assume it's a Python package and
-import it and call the model's own `load()` method. If a model is loaded from a
-path, spaCy will assume it's a data directory, load its
+Load a pipeline using the name of an installed
+[package](/usage/saving-loading#models), a string path or a `Path`-like object.
+spaCy will try resolving the load argument in this order. If a pipeline is
+loaded from a string name, spaCy will assume it's a Python package and import it
+and call the package's own `load()` method. If a pipeline is loaded from a path,
+spaCy will assume it's a data directory, load its
 [`config.cfg`](/api/data-formats#config) and use the language and pipeline
 information to construct the `Language` class. The data will be loaded in via
 [`Language.from_disk`](/api/language#from_disk).
@@ -35,38 +36,38 @@ specified separately using the new `exclude` keyword argument.
 >
 > ```python
 > nlp = spacy.load("en_core_web_sm") # package
-> nlp = spacy.load("/path/to/en") # string path
-> nlp = spacy.load(Path("/path/to/en")) # pathlib Path
+> nlp = spacy.load("/path/to/pipeline") # string path
+> nlp = spacy.load(Path("/path/to/pipeline")) # pathlib Path
 >
 > nlp = spacy.load("en_core_web_sm", exclude=["parser", "tagger"])
 > ```
 
 | Name                                 | Description                                                                                                                                                                                                                                    |
 | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `name`                               | Model to load, i.e. package name or path. ~~Union[str, Path]~~                                                                                                                                                                                 |
+| `name`                               | Pipeline to load, i.e. package name or path. ~~Union[str, Path]~~                                                                                                                                                                              |
 | _keyword-only_                       |                                                                                                                                                                                                                                                |
 | `disable`                            | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). ~~List[str]~~ |
 | `exclude` <Tag variant="new">3</Tag> | Names of pipeline components to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~List[str]~~                                                                                                           |
 | `config` <Tag variant="new">3</Tag>  | Optional config overrides, either as nested dict or dict keyed by section value in dot notation, e.g. `"components.name.value"`. ~~Union[Dict[str, Any], Config]~~                                                                             |
-| **RETURNS**                          | A `Language` object with the loaded model. ~~Language~~                                                                                                                                                                                        |
+| **RETURNS**                          | A `Language` object with the loaded pipeline. ~~Language~~                                                                                                                                                                                     |
 
-Essentially, `spacy.load()` is a convenience wrapper that reads the model's
+Essentially, `spacy.load()` is a convenience wrapper that reads the pipeline's
 [`config.cfg`](/api/data-formats#config), uses the language and pipeline
 information to construct a `Language` object, loads in the model data and
-returns it.
+weights, and returns it.
 
 ```python
 ### Abstract example
-cls = util.get_lang_class(lang)         #  get language for ID, e.g. "en"
-nlp = cls()                             #  initialize the language
+cls = spacy.util.get_lang_class(lang)  # 1. Get Language class, e.g. English
+nlp = cls()                            # 2. Initialize it
 for name in pipeline:
-    nlp.add_pipe(name)                  #  add component to pipeline
-nlp.from_disk(model_data_path)          #  load in model data
+    nlp.add_pipe(name)                 # 3. Add the component to the pipeline
+nlp.from_disk(data_path)               # 4. Load in the binary data
 ```
 
 ### spacy.blank {#spacy.blank tag="function" new="2"}
 
-Create a blank model of a given language class. This function is the twin of
+Create a blank pipeline of a given language class. This function is the twin of
 `spacy.load()`.
 
 > #### Example
@@ -84,9 +85,7 @@ Create a blank model of a given language class. This function is the twin of
 ### spacy.info {#spacy.info tag="function"}
 
 The same as the [`info` command](/api/cli#info). Pretty-print information about
-your installation, models and local setup from within spaCy. To get the model
-meta data as a dictionary instead, you can use the `meta` attribute on your
-`nlp` object with a loaded model, e.g. `nlp.meta`.
+your installation, installed pipelines and local setup from within spaCy.
 
 > #### Example
 >
@@ -96,12 +95,12 @@ meta data as a dictionary instead, you can use the `meta` attribute on your
 > markdown = spacy.info(markdown=True, silent=True)
 > ```
 
-| Name           | Description                                                        |
-| -------------- | ------------------------------------------------------------------ |
-| `model`        | A model, i.e. a package name or path (optional). ~~Optional[str]~~ |
-| _keyword-only_ |                                                                    |
-| `markdown`     | Print information as Markdown. ~~bool~~                            |
-| `silent`       | Don't print anything, just return. ~~bool~~                        |
+| Name           | Description                                                                  |
+| -------------- | ---------------------------------------------------------------------------- |
+| `model`        | Optional pipeline, i.e. a package name or path (optional). ~~Optional[str]~~ |
+| _keyword-only_ |                                                                              |
+| `markdown`     | Print information as Markdown. ~~bool~~                                      |
+| `silent`       | Don't print anything, just return. ~~bool~~                                  |
 
 ### spacy.explain {#spacy.explain tag="function"}
 
@@ -132,7 +131,7 @@ list of available terms, see
 Allocate data and perform operations on [GPU](/usage/#gpu), if available. If
 data has already been allocated on CPU, it will not be moved. Ideally, this
 function should be called right after importing spaCy and _before_ loading any
-models.
+pipelines.
 
 > #### Example
 >
@@ -151,7 +150,7 @@ models.
 Allocate data and perform operations on [GPU](/usage/#gpu). Will raise an error
 if no GPU is available. If data has already been allocated on CPU, it will not
 be moved. Ideally, this function should be called right after importing spaCy
-and _before_ loading any models.
+and _before_ loading any pipelines.
 
 > #### Example
 >
@@ -270,9 +269,9 @@ If a setting is not present in the options, the default value will be used.
 | `template` <Tag variant="new">2.2</Tag> | Optional template to overwrite the HTML used to render entity spans. Should be a format string and can use `{bg}`, `{text}` and `{label}`. See [`templates.py`](https://github.com/explosion/spaCy/blob/master/spacy/displacy/templates.py) for examples. ~~Optional[str]~~ |
 
 By default, displaCy comes with colors for all entity types used by
-[spaCy models](/models). If you're using custom entity types, you can use the
-`colors` setting to add your own colors for them. Your application or model
-package can also expose a
+[spaCy's trained pipelines](/models). If you're using custom entity types, you
+can use the `colors` setting to add your own colors for them. Your application
+or pipeline package can also expose a
 [`spacy_displacy_colors` entry point](/usage/saving-loading#entry-points-displacy)
 to add custom labels and their colors automatically.
 
@@ -308,7 +307,6 @@ factories.
 | Registry name     | Description                                                                                                                                                                                                                                        |
 | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `architectures`   | Registry for functions that create [model architectures](/api/architectures). Can be used to register custom model architectures and reference them in the `config.cfg`.                                                                           |
-| `assets`          | Registry for data assets, knowledge bases etc.                                                                                                                                                                                                     |
 | `batchers`        | Registry for training and evaluation [data batchers](#batchers).                                                                                                                                                                                   |
 | `callbacks`       | Registry for custom callbacks to [modify the `nlp` object](/usage/training#custom-code-nlp-callbacks) before training.                                                                                                                             |
 | `displacy_colors` | Registry for custom color scheme for the [`displacy` NER visualizer](/usage/visualizers). Automatically reads from [entry points](/usage/saving-loading#entry-points).                                                                             |
@@ -316,8 +314,10 @@ factories.
 | `initializers`    | Registry for functions that create [initializers](https://thinc.ai/docs/api-initializers).                                                                                                                                                         |
 | `languages`       | Registry for language-specific `Language` subclasses. Automatically reads from [entry points](/usage/saving-loading#entry-points).                                                                                                                 |
 | `layers`          | Registry for functions that create [layers](https://thinc.ai/docs/api-layers).                                                                                                                                                                     |
+| `loggers`         | Registry for functions that log [training results](/usage/training).                                                                                                                                                                               |
 | `lookups`         | Registry for large lookup tables available via `vocab.lookups`.                                                                                                                                                                                    |
 | `losses`          | Registry for functions that create [losses](https://thinc.ai/docs/api-loss).                                                                                                                                                                       |
+| `misc`            | Registry for miscellaneous functions that return data assets, knowledge bases or anything else you may need.                                                                                                                                       |
 | `optimizers`      | Registry for functions that create [optimizers](https://thinc.ai/docs/api-optimizers).                                                                                                                                                             |
 | `readers`         | Registry for training and evaluation data readers like [`Corpus`](/api/corpus).                                                                                                                                                                    |
 | `schedules`       | Registry for functions that create [schedules](https://thinc.ai/docs/api-schedules).                                                                                                                                                               |
@@ -340,7 +340,7 @@ See the [`Transformer`](/api/transformer) API reference and
 >     def annotation_setter(docs, trf_data) -> None:
 >        # Set annotations on the docs
 >
->     return annotation_sette
+>     return annotation_setter
 > ```
 
 | Registry name                                               | Description                                                                                                                                                                                                                                       |
@@ -348,6 +348,110 @@ See the [`Transformer`](/api/transformer) API reference and
 | [`span_getters`](/api/transformer#span_getters)             | Registry for functions that take a batch of `Doc` objects and return a list of `Span` objects to process by the transformer, e.g. sentences.                                                                                                      |
 | [`annotation_setters`](/api/transformer#annotation_setters) | Registry for functions that create annotation setters. Annotation setters are functions that take a batch of `Doc` objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set additional annotations on the `Doc`. |
 
+## Loggers {#loggers source="spacy/gold/loggers.py" new="3"}
+
+A logger records the training results. When a logger is created, two functions
+are returned: one for logging the information for each training step, and a
+second function that is called to finalize the logging when the training is
+finished. To log each training step, a
+[dictionary](/usage/training#custom-logging) is passed on from the
+[`spacy train`](/api/cli#train), including information such as the training loss
+and the accuracy scores on the development set.
+
+There are two built-in logging functions: a logger printing results to the
+console in tabular format (which is the default), and one that also sends the
+results to a [Weights & Biases](https://www.wandb.com/) dashboard. Instead of
+using one of the built-in loggers listed here, you can also
+[implement your own](/usage/training#custom-logging).
+
+#### spacy.ConsoleLogger {#ConsoleLogger tag="registered function"}
+
+> #### Example config
+>
+> ```ini
+> [training.logger]
+> @loggers = "spacy.ConsoleLogger.v1"
+> ```
+
+Writes the results of a training step to the console in a tabular format.
+
+<Accordion title="Example console output" spaced>
+
+```cli
+$ python -m spacy train config.cfg
+```
+
+```
+ℹ Using CPU
+ℹ Loading config and nlp from: config.cfg
+ℹ Pipeline: ['tok2vec', 'tagger']
+ℹ Start training
+ℹ Training. Initial learn rate: 0.0
+
+E     #        LOSS TOK2VEC   LOSS TAGGER   TAG_ACC   SCORE
+---   ------   ------------   -----------   -------   ------
+  1        0           0.00         86.20      0.22     0.00
+  1      200           3.08      18968.78     34.00     0.34
+  1      400          31.81      22539.06     33.64     0.34
+  1      600          92.13      22794.91     43.80     0.44
+  1      800         183.62      21541.39     56.05     0.56
+  1     1000         352.49      25461.82     65.15     0.65
+  1     1200         422.87      23708.82     71.84     0.72
+  1     1400         601.92      24994.79     76.57     0.77
+  1     1600         662.57      22268.02     80.20     0.80
+  1     1800        1101.50      28413.77     82.56     0.83
+  1     2000        1253.43      28736.36     85.00     0.85
+  1     2200        1411.02      28237.53     87.42     0.87
+  1     2400        1605.35      28439.95     88.70     0.89
+```
+
+Note that the cumulative loss keeps increasing within one epoch, but should
+start decreasing across epochs.
+
+ </Accordion>
+
+#### spacy.WandbLogger {#WandbLogger tag="registered function"}
+
+> #### Installation
+>
+> ```bash
+> $ pip install wandb
+> $ wandb login
+> ```
+
+Built-in logger that sends the results of each training step to the dashboard of
+the [Weights & Biases](https://www.wandb.com/) tool. To use this logger, Weights
+& Biases should be installed, and you should be logged in. The logger will send
+the full config file to W&B, as well as various system information such as
+memory utilization, network traffic, disk IO, GPU statistics, etc. This will
+also include information such as your hostname and operating system, as well as
+the location of your Python executable.
+
+<Infobox variant="warning">
+
+Note that by default, the full (interpolated)
+[training config](/usage/training#config) is sent over to the W&B dashboard. If
+you prefer to **exclude certain information** such as path names, you can list
+those fields in "dot notation" in the `remove_config_values` parameter. These
+fields will then be removed from the config before uploading, but will otherwise
+remain in the config file stored on your local system.
+
+</Infobox>
+
+> #### Example config
+>
+> ```ini
+> [training.logger]
+> @loggers = "spacy.WandbLogger.v1"
+> project_name = "monitor_spacy_training"
+> remove_config_values = ["paths.train", "paths.dev", "training.dev_corpus.path", "training.train_corpus.path"]
+> ```
+
+| Name                   | Description                                                                                                                           |
+| ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet. ~~str~~ |
+| `remove_config_values` | A list of values to include from the config before it is uploaded to W&B (default: empty). ~~List[str]~~                              |
+
 ## Batchers {#batchers source="spacy/gold/batchers.py" new="3"}
 
 A data batcher implements a batching strategy that essentially turns a stream of
@@ -362,7 +466,7 @@ Instead of using one of the built-in batchers listed here, you can also
 [implement your own](/usage/training#custom-code-readers-batchers), which may or
 may not use a custom schedule.
 
-#### batch_by_words.v1 {#batch_by_words tag="registered function"}
+#### batch_by_words {#batch_by_words tag="registered function"}
 
 Create minibatches of roughly a given number of words. If any examples are
 longer than the specified batch length, they will appear in a batch by
@@ -374,7 +478,7 @@ themselves, or be discarded if `discard_oversize` is set to `True`. The argument
 >
 > ```ini
 > [training.batcher]
-> @batchers = "batch_by_words.v1"
+> @batchers = "spacy.batch_by_words.v1"
 > size = 100
 > tolerance = 0.2
 > discard_oversize = false
@@ -389,13 +493,13 @@ themselves, or be discarded if `discard_oversize` is set to `True`. The argument
 | `discard_oversize` | Whether to discard sequences that by themselves exceed the tolerated size. ~~bool~~                                                                                                     |
 | `get_length`       | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. ~~Optional[Callable[[Any], int]]~~                                 |
 
-#### batch_by_sequence.v1 {#batch_by_sequence tag="registered function"}
+#### batch_by_sequence {#batch_by_sequence tag="registered function"}
 
 > #### Example config
 >
 > ```ini
 > [training.batcher]
-> @batchers = "batch_by_sequence.v1"
+> @batchers = "spacy.batch_by_sequence.v1"
 > size = 32
 > get_length = null
 > ```
@@ -407,13 +511,13 @@ Create a batcher that creates batches of the specified size.
 | `size`       | The target number of items per batch. Can also be a block referencing a schedule, e.g. [`compounding`](https://thinc.ai/docs/api-schedules/#compounding). ~~Union[int, Sequence[int]]~~ |
 | `get_length` | Optional function that receives a sequence item and returns its length. Defaults to the built-in `len()` if not set. ~~Optional[Callable[[Any], int]]~~                                 |
 
-#### batch_by_padded.v1 {#batch_by_padded tag="registered function"}
+#### batch_by_padded {#batch_by_padded tag="registered function"}
 
 > #### Example config
 >
 > ```ini
 > [training.batcher]
-> @batchers = "batch_by_padded.v1"
+> @batchers = "spacy.batch_by_padded.v1"
 > size = 100
 > buffer = 256
 > discard_oversize = false
@@ -560,8 +664,8 @@ loaded lazily, to avoid expensive setup code associated with the language data.
 
 ### util.load_model {#util.load_model tag="function" new="2"}
 
-Load a model from a package or data path. If called with a package name, spaCy
-will assume the model is a Python package and import and call its `load()`
+Load a pipeline from a package or data path. If called with a string name, spaCy
+will assume the pipeline is a Python package and import and call its `load()`
 method. If called with a path, spaCy will assume it's a data directory, read the
 language and pipeline settings from the [`config.cfg`](/api/data-formats#config)
 and create a `Language` object. The model data will then be loaded in via
@@ -577,16 +681,16 @@ and create a `Language` object. The model data will then be loaded in via
 
 | Name                                 | Description                                                                                                                                                                                                                                    |
 | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `name`                               | Package name or model path. ~~str~~                                                                                                                                                                                                            |
+| `name`                               | Package name or path. ~~str~~                                                                                                                                                                                                                  |
 | `vocab` <Tag variant="new">3</Tag>   | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~.                                                                                                         |
 | `disable`                            | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). ~~List[str]~~ |
 | `exclude` <Tag variant="new">3</Tag> | Names of pipeline components to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~List[str]~~                                                                                                           |
 | `config` <Tag variant="new">3</Tag>  | Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. `"nlp.pipeline"`. ~~Union[Dict[str, Any], Config]~~                                                                                                 |
-| **RETURNS**                          | `Language` class with the loaded model. ~~Language~~                                                                                                                                                                                           |
+| **RETURNS**                          | `Language` class with the loaded pipeline. ~~Language~~                                                                                                                                                                                        |
 
 ### util.load_model_from_init_py {#util.load_model_from_init_py tag="function" new="2"}
 
-A helper function to use in the `load()` method of a model package's
+A helper function to use in the `load()` method of a pipeline package's
 [`__init__.py`](https://github.com/explosion/spacy-models/tree/master/template/model/xx_model_name/__init__.py).
 
 > #### Example
@@ -600,70 +704,72 @@ A helper function to use in the `load()` method of a model package's
 
 | Name                                 | Description                                                                                                                                                                                                                                    |
 | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `init_file`                          | Path to model's `__init__.py`, i.e. `__file__`. ~~Union[str, Path]~~                                                                                                                                                                           |
+| `init_file`                          | Path to package's `__init__.py`, i.e. `__file__`. ~~Union[str, Path]~~                                                                                                                                                                         |
 | `vocab` <Tag variant="new">3</Tag>   | Optional shared vocab to pass in on initialization. If `True` (default), a new `Vocab` object will be created. ~~Union[Vocab, bool]~~.                                                                                                         |
 | `disable`                            | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). Disabled pipes will be loaded but they won't be run unless you explicitly enable them by calling [nlp.enable_pipe](/api/language#enable_pipe). ~~List[str]~~ |
 | `exclude` <Tag variant="new">3</Tag> | Names of pipeline components to [exclude](/usage/processing-pipelines#disabling). Excluded components won't be loaded. ~~List[str]~~                                                                                                           |
 | `config` <Tag variant="new">3</Tag>  | Config overrides as nested dict or flat dict keyed by section values in dot notation, e.g. `"nlp.pipeline"`. ~~Union[Dict[str, Any], Config]~~                                                                                                 |
-| **RETURNS**                          | `Language` class with the loaded model. ~~Language~~                                                                                                                                                                                           |
+| **RETURNS**                          | `Language` class with the loaded pipeline. ~~Language~~                                                                                                                                                                                        |
 
 ### util.load_config {#util.load_config tag="function" new="3"}
 
-Load a model's [`config.cfg`](/api/data-formats#config) from a file path. The
-config typically includes details about the model pipeline and how its
-components are created, as well as all training settings and hyperparameters.
+Load a pipeline's [`config.cfg`](/api/data-formats#config) from a file path. The
+config typically includes details about the components and how they're created,
+as well as all training settings and hyperparameters.
 
 > #### Example
 >
 > ```python
-> config = util.load_config("/path/to/model/config.cfg")
+> config = util.load_config("/path/to/config.cfg")
 > print(config.to_str())
 > ```
 
 | Name          | Description                                                                                                                                                                 |
 | ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `path`        | Path to the model's `config.cfg`. ~~Union[str, Path]~~                                                                                                                      |
+| `path`        | Path to the pipeline's `config.cfg`. ~~Union[str, Path]~~                                                                                                                   |
 | `overrides`   | Optional config overrides to replace in loaded config. Can be provided as nested dict, or as flat dict with keys in dot notation, e.g. `"nlp.pipeline"`. ~~Dict[str, Any]~~ |
 | `interpolate` | Whether to interpolate the config and replace variables like `${paths.train}` with their values. Defaults to `False`. ~~bool~~                                              |
-| **RETURNS**   | The model's config. ~~Config~~                                                                                                                                              |
+| **RETURNS**   | The pipeline's config. ~~Config~~                                                                                                                                           |
 
 ### util.load_meta {#util.load_meta tag="function" new="3"}
 
-Get a model's [`meta.json`](/api/data-formats#meta) from a file path and
-validate its contents.
+Get a pipeline's [`meta.json`](/api/data-formats#meta) from a file path and
+validate its contents. The meta typically includes details about author,
+licensing, data sources and version.
 
 > #### Example
 >
 > ```python
-> meta = util.load_meta("/path/to/model/meta.json")
+> meta = util.load_meta("/path/to/meta.json")
 > ```
 
-| Name        | Description                                           |
-| ----------- | ----------------------------------------------------- |
-| `path`      | Path to the model's `meta.json`. ~~Union[str, Path]~~ |
-| **RETURNS** | The model's meta data. ~~Dict[str, Any]~~             |
+| Name        | Description                                              |
+| ----------- | -------------------------------------------------------- |
+| `path`      | Path to the pipeline's `meta.json`. ~~Union[str, Path]~~ |
+| **RETURNS** | The pipeline's meta data. ~~Dict[str, Any]~~             |
 
 ### util.get_installed_models {#util.get_installed_models tag="function" new="3"}
 
-List all model packages installed in the current environment. This will include
-any spaCy model that was packaged with [`spacy package`](/api/cli#package).
-Under the hood, model packages expose a Python entry point that spaCy can check,
-without having to load the model.
+List all pipeline packages installed in the current environment. This will
+include any spaCy pipeline that was packaged with
+[`spacy package`](/api/cli#package). Under the hood, pipeline packages expose a
+Python entry point that spaCy can check, without having to load the `nlp`
+object.
 
 > #### Example
 >
 > ```python
-> model_names = util.get_installed_models()
+> names = util.get_installed_models()
 > ```
 
-| Name        | Description                                                                        |
-| ----------- | ---------------------------------------------------------------------------------- |
-| **RETURNS** | The string names of the models installed in the current environment. ~~List[str]~~ |
+| Name        | Description                                                                           |
+| ----------- | ------------------------------------------------------------------------------------- |
+| **RETURNS** | The string names of the pipelines installed in the current environment. ~~List[str]~~ |
 
 ### util.is_package {#util.is_package tag="function"}
 
 Check if string maps to a package installed via pip. Mainly used to validate
-[model packages](/usage/models).
+[pipeline packages](/usage/models).
 
 > #### Example
 >
@@ -680,7 +786,8 @@ Check if string maps to a package installed via pip. Mainly used to validate
 ### util.get_package_path {#util.get_package_path tag="function" new="2"}
 
 Get path to an installed package. Mainly used to resolve the location of
-[model packages](/usage/models). Currently imports the package to find its path.
+[pipeline packages](/usage/models). Currently imports the package to find its
+path.
 
 > #### Example
 >
@@ -689,10 +796,10 @@ Get path to an installed package. Mainly used to resolve the location of
 > # /usr/lib/python3.6/site-packages/en_core_web_sm
 > ```
 
-| Name           | Description                               |
-| -------------- | ----------------------------------------- |
-| `package_name` | Name of installed package. ~~str~~        |
-| **RETURNS**    | Path to model package directory. ~~Path~~ |
+| Name           | Description                                  |
+| -------------- | -------------------------------------------- |
+| `package_name` | Name of installed package. ~~str~~           |
+| **RETURNS**    | Path to pipeline package directory. ~~Path~~ |
 
 ### util.is_in_jupyter {#util.is_in_jupyter tag="function" new="2"}
 
diff --git a/website/docs/api/transformer.md b/website/docs/api/transformer.md
index c32651e02..b41a18890 100644
--- a/website/docs/api/transformer.md
+++ b/website/docs/api/transformer.md
@@ -25,8 +25,8 @@ work out-of-the-box.
 
 </Infobox>
 
-This pipeline component lets you use transformer models in your pipeline.
-Supports all models that are available via the
+This pipeline component lets you use transformer models in your pipeline. It
+supports all models that are available via the
 [HuggingFace `transformers`](https://huggingface.co/transformers) library.
 Usually you will connect subsequent components to the shared transformer using
 the [TransformerListener](/api/architectures#TransformerListener) layer. This
@@ -50,8 +50,8 @@ The default config is defined by the pipeline component factory and describes
 how the component should be configured. You can override its settings via the
 `config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
 [`config.cfg` for training](/usage/training#config). See the
-[model architectures](/api/architectures) documentation for details on the
-architectures and their arguments and hyperparameters.
+[model architectures](/api/architectures#transformers) documentation for details
+on the transformer architectures and their arguments and hyperparameters.
 
 > #### Example
 >
@@ -61,11 +61,11 @@ architectures and their arguments and hyperparameters.
 > nlp.add_pipe("transformer", config=DEFAULT_CONFIG)
 > ```
 
-| Setting             | Description                                                                                                                                                                                                                                                                                                            |
-| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `max_batch_items`   | Maximum size of a padded batch. Defaults to `4096`. ~~int~~                                                                                                                                                                                                                                                            |
-| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. Defaults to `null_annotation_setter` (no additional annotations). ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
-| `model`             | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [TransformerModel](/api/architectures#TransformerModel). ~~Model[List[Doc], FullTransformerBatch]~~                                                                                                                         |
+| Setting             | Description                                                                                                                                                                                                                                                                                                           |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `max_batch_items`   | Maximum size of a padded batch. Defaults to `4096`. ~~int~~                                                                                                                                                                                                                                                           |
+| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs to set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. Defaults to `null_annotation_setter` (no additional annotations). ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
+| `model`             | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Defaults to [TransformerModel](/api/architectures#TransformerModel). ~~Model[List[Doc], FullTransformerBatch]~~                                                                                                                        |
 
 ```python
 https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py
@@ -102,14 +102,14 @@ attribute. You can also provide a callback to set additional annotations. In
 your application, you would normally use a shortcut for this and instantiate the
 component using its string name and [`nlp.add_pipe`](/api/language#create_pipe).
 
-| Name                | Description                                                                                                                                                                                                                                                                              |
-| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `vocab`             | The shared vocabulary. ~~Vocab~~                                                                                                                                                                                                                                                         |
-| `model`             | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. ~~Model[List[Doc], FullTransformerBatch]~~                                                       |
-| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs can set additional annotations on the `Doc`. The `Doc._.transformer_data` attribute is set prior to calling the callback. By default, no annotations are set. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
-| _keyword-only_      |                                                                                                                                                                                                                                                                                          |
-| `name`              | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                                                                                                                                                                                      |
-| `max_batch_items`   | Maximum size of a padded batch. Defaults to `128*32`. ~~int~~                                                                                                                                                                                                                            |
+| Name                | Description                                                                                                                                                                                                                                                                             |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `vocab`             | The shared vocabulary. ~~Vocab~~                                                                                                                                                                                                                                                        |
+| `model`             | The Thinc [`Model`](https://thinc.ai/docs/api-model) wrapping the transformer. Usually you will want to use the [TransformerModel](/api/architectures#TransformerModel) layer for this. ~~Model[List[Doc], FullTransformerBatch]~~                                                      |
+| `annotation_setter` | Function that takes a batch of `Doc` objects and transformer outputs and stores the annotations on the `Doc`. The `Doc._.trf_data` attribute is set prior to calling the callback. By default, no additional annotations are set. ~~Callable[[List[Doc], FullTransformerBatch], None]~~ |
+| _keyword-only_      |                                                                                                                                                                                                                                                                                         |
+| `name`              | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~                                                                                                                                                                                     |
+| `max_batch_items`   | Maximum size of a padded batch. Defaults to `128*32`. ~~int~~                                                                                                                                                                                                                           |
 
 ## Transformer.\_\_call\_\_ {#call tag="method"}
 
@@ -383,9 +383,8 @@ return tensors that refer to a whole padded batch of documents. These tensors
 are wrapped into the
 [FullTransformerBatch](/api/transformer#fulltransformerbatch) object. The
 `FullTransformerBatch` then splits out the per-document data, which is handled
-by this class. Instances of this class
-are`typically assigned to the [Doc._.trf_data`](/api/transformer#custom-attributes)
-extension attribute.
+by this class. Instances of this class are typically assigned to the
+[`Doc._.trf_data`](/api/transformer#custom-attributes) extension attribute.
 
 | Name      | Description                                                                                                                                                                                                                                                                                                                                             |
 | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -447,13 +446,14 @@ overlap, and you can also omit sections of the Doc if they are not relevant.
 
 Span getters can be referenced in the `[components.transformer.model.get_spans]`
 block of the config to customize the sequences processed by the transformer. You
-can also register custom span getters using the `@spacy.registry.span_getters`
-decorator.
+can also register
+[custom span getters](/usage/embeddings-transformers#transformers-training-custom-settings)
+using the `@spacy.registry.span_getters` decorator.
 
 > #### Example
 >
 > ```python
-> @spacy.registry.span_getters("sent_spans.v1")
+> @spacy.registry.span_getters("custom_sent_spans")
 > def configure_get_sent_spans() -> Callable:
 >     def get_sent_spans(docs: Iterable[Doc]) -> List[List[Span]]:
 >         return [list(doc.sents) for doc in docs]
@@ -472,7 +472,7 @@ decorator.
 >
 > ```ini
 > [transformer.model.get_spans]
-> @span_getters = "doc_spans.v1"
+> @span_getters = "spacy-transformers.doc_spans.v1"
 > ```
 
 Create a span getter that uses the whole document as its spans. This is the best
@@ -485,7 +485,7 @@ texts.
 >
 > ```ini
 > [transformer.model.get_spans]
-> @span_getters = "sent_spans.v1"
+> @span_getters = "spacy-transformers.sent_spans.v1"
 > ```
 
 Create a span getter that uses sentence boundary markers to extract the spans.
@@ -500,7 +500,7 @@ more meaningful windows to attend over.
 >
 > ```ini
 > [transformer.model.get_spans]
-> @span_getters = "strided_spans.v1"
+> @span_getters = "spacy-transformers.strided_spans.v1"
 > window = 128
 > stride = 96
 > ```
@@ -518,7 +518,7 @@ right context.
 
 ## Annotation setters {#annotation_setters tag="registered functions" source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/annotation_setters.py"}
 
-Annotation setters are functions that that take a batch of `Doc` objects and a
+Annotation setters are functions that take a batch of `Doc` objects and a
 [`FullTransformerBatch`](/api/transformer#fulltransformerbatch) and can set
 additional annotations on the `Doc`, e.g. to set custom or built-in attributes.
 You can register custom annotation setters using the
@@ -551,6 +551,6 @@ The following built-in functions are available:
 The component sets the following
 [custom extension attributes](/usage/processing-pipeline#custom-components-attributes):
 
-| Name           | Description                                                              |
-| -------------- | ------------------------------------------------------------------------ |
-| `Doc.trf_data` | Transformer tokens and outputs for the `Doc` object. ~~TransformerData~~ |
+| Name             | Description                                                              |
+| ---------------- | ------------------------------------------------------------------------ |
+| `Doc._.trf_data` | Transformer tokens and outputs for the `Doc` object. ~~TransformerData~~ |
diff --git a/website/docs/models/index.md b/website/docs/models/index.md
index d5f87d3b5..64e719f37 100644
--- a/website/docs/models/index.md
+++ b/website/docs/models/index.md
@@ -1,6 +1,6 @@
 ---
-title: Models
-teaser: Downloadable pretrained models for spaCy
+title: Trained Models & Pipelines
+teaser: Downloadable trained pipelines and weights for spaCy
 menu:
   - ['Quickstart', 'quickstart']
   - ['Conventions', 'conventions']
@@ -8,15 +8,15 @@ menu:
 
 <!-- Update page, refer to new /api/architectures and training docs -->
 
-The models directory includes two types of pretrained models:
+This directory includes two types of packages:
 
-1. **Core models:** General-purpose pretrained models to predict named entities,
-   part-of-speech tags and syntactic dependencies. Can be used out-of-the-box
-   and fine-tuned on more specific data.
-2. **Starter models:** Transfer learning starter packs with pretrained weights
-   you can initialize your models with to achieve better accuracy. They can
+1. **Trained pipelines:** General-purpose spaCy pipelines to predict named
+   entities, part-of-speech tags and syntactic dependencies. Can be used
+   out-of-the-box and fine-tuned on more specific data.
+2. **Starters:** Transfer learning starter packs with pretrained weights you can
+   initialize your pipeline models with to achieve better accuracy. They can
    include word vectors (which will be used as features during training) or
-   other pretrained representations like BERT. These models don't include
+   other pretrained representations like BERT. These packages don't include
    components for specific tasks like NER or text classification and are
    intended to be used as base models when training your own models.
 
@@ -28,43 +28,42 @@ import QuickstartModels from 'widgets/quickstart-models.js'
 
 <Infobox title="Installation and usage" emoji="📖">
 
-For more details on how to use models with spaCy, see the
-[usage guide on models](/usage/models).
+For more details on how to use trained pipelines with spaCy, see the
+[usage guide](/usage/models).
 
 </Infobox>
 
-## Model naming conventions {#conventions}
+## Package naming conventions {#conventions}
 
-In general, spaCy expects all model packages to follow the naming convention of
-`[lang`\_[name]]. For spaCy's models, we also chose to divide the name into
-three components:
+In general, spaCy expects all pipeline packages to follow the naming convention
+of `[lang`\_[name]]. For spaCy's pipelines, we also chose to divide the name
+into three components:
 
-1. **Type:** Model capabilities (e.g. `core` for general-purpose model with
+1. **Type:** Capabilities (e.g. `core` for general-purpose pipeline with
    vocabulary, syntax, entities and word vectors, or `depent` for only vocab,
    syntax and entities).
-2. **Genre:** Type of text the model is trained on, e.g. `web` or `news`.
-3. **Size:** Model size indicator, `sm`, `md` or `lg`.
+2. **Genre:** Type of text the pipeline is trained on, e.g. `web` or `news`.
+3. **Size:** Package size indicator, `sm`, `md` or `lg`.
 
 For example, [`en_core_web_sm`](/models/en#en_core_web_sm) is a small English
-model trained on written web text (blogs, news, comments), that includes
+pipeline trained on written web text (blogs, news, comments), that includes
 vocabulary, vectors, syntax and entities.
 
-### Model versioning {#model-versioning}
+### Package versioning {#model-versioning}
 
-Additionally, the model versioning reflects both the compatibility with spaCy,
-as well as the major and minor model version. A model version `a.b.c` translates
-to:
+Additionally, the pipeline package versioning reflects both the compatibility
+with spaCy, as well as the major and minor version. A package version `a.b.c`
+translates to:
 
 - `a`: **spaCy major version**. For example, `2` for spaCy v2.x.
-- `b`: **Model major version**. Models with a different major version can't be
-  loaded by the same code. For example, changing the width of the model, adding
-  hidden layers or changing the activation changes the model major version.
-- `c`: **Model minor version**. Same model structure, but different parameter
-  values, e.g. from being trained on different data, for different numbers of
-  iterations, etc.
+- `b`: **Package major version**. Pipelines with a different major version can't
+  be loaded by the same code. For example, changing the width of the model,
+  adding hidden layers or changing the activation changes the major version.
+- `c`: **Package minor version**. Same pipeline structure, but different
+  parameter values, e.g. from being trained on different data, for different
+  numbers of iterations, etc.
 
 For a detailed compatibility overview, see the
-[`compatibility.json`](https://github.com/explosion/spacy-models/tree/master/compatibility.json)
-in the models repository. This is also the source of spaCy's internal
-compatibility check, performed when you run the [`download`](/api/cli#download)
-command.
+[`compatibility.json`](https://github.com/explosion/spacy-models/tree/master/compatibility.json).
+This is also the source of spaCy's internal compatibility check, performed when
+you run the [`download`](/api/cli#download) command.
diff --git a/website/docs/usage/101/_pipelines.md b/website/docs/usage/101/_pipelines.md
index 0aa821223..9a63ee42d 100644
--- a/website/docs/usage/101/_pipelines.md
+++ b/website/docs/usage/101/_pipelines.md
@@ -1,9 +1,9 @@
 When you call `nlp` on a text, spaCy first tokenizes the text to produce a `Doc`
 object. The `Doc` is then processed in several different steps – this is also
 referred to as the **processing pipeline**. The pipeline used by the
-[default models](/models) typically include a tagger, a lemmatizer, a parser and
-an entity recognizer. Each pipeline component returns the processed `Doc`, which
-is then passed on to the next component.
+[trained pipelines](/models) typically include a tagger, a lemmatizer, a parser
+and an entity recognizer. Each pipeline component returns the processed `Doc`,
+which is then passed on to the next component.
 
 ![The processing pipeline](../../images/pipeline.svg)
 
@@ -23,14 +23,15 @@ is then passed on to the next component.
 | **textcat**           | [`TextCategorizer`](/api/textcategorizer)                          | `Doc.cats`                                                | Assign document labels.                          |
 | **custom**            | [custom components](/usage/processing-pipelines#custom-components) | `Doc._.xxx`, `Token._.xxx`, `Span._.xxx`                  | Assign custom attributes, methods or properties. |
 
-The processing pipeline always **depends on the statistical model** and its
-capabilities. For example, a pipeline can only include an entity recognizer
-component if the model includes data to make predictions of entity labels. This
-is why each model will specify the pipeline to use in its meta data and
-[config](/usage/training#config), as a simple list containing the component
-names:
+The capabilities of a processing pipeline always depend on the components, their
+models and how they were trained. For example, a pipeline for named entity
+recognition needs to include a trained named entity recognizer component with a
+statistical model and weights that enable it to **make predictions** of entity
+labels. This is why each pipeline specifies its components and their settings in
+the [config](/usage/training#config):
 
 ```ini
+[nlp]
 pipeline = ["tagger", "parser", "ner"]
 ```
 
diff --git a/website/docs/usage/101/_pos-deps.md b/website/docs/usage/101/_pos-deps.md
index 1e8960edf..a531b245e 100644
--- a/website/docs/usage/101/_pos-deps.md
+++ b/website/docs/usage/101/_pos-deps.md
@@ -1,9 +1,9 @@
 After tokenization, spaCy can **parse** and **tag** a given `Doc`. This is where
-the statistical model comes in, which enables spaCy to **make a prediction** of
-which tag or label most likely applies in this context. A model consists of
-binary data and is produced by showing a system enough examples for it to make
-predictions that generalize across the language – for example, a word following
-"the" in English is most likely a noun.
+the trained pipeline and its statistical models come in, which enable spaCy to
+**make predictions** of which tag or label most likely applies in this context.
+A trained component includes binary data that is produced by showing a system
+enough examples for it to make predictions that generalize across the language –
+for example, a word following "the" in English is most likely a noun.
 
 Linguistic annotations are available as
 [`Token` attributes](/api/token#attributes). Like many NLP libraries, spaCy
@@ -25,7 +25,8 @@ for token in doc:
 
 > - **Text:** The original word text.
 > - **Lemma:** The base form of the word.
-> - **POS:** The simple [UPOS](https://universaldependencies.org/docs/u/pos/) part-of-speech tag.
+> - **POS:** The simple [UPOS](https://universaldependencies.org/docs/u/pos/)
+>   part-of-speech tag.
 > - **Tag:** The detailed part-of-speech tag.
 > - **Dep:** Syntactic dependency, i.e. the relation between tokens.
 > - **Shape:** The word shape – capitalization, punctuation, digits.
diff --git a/website/docs/usage/101/_serialization.md b/website/docs/usage/101/_serialization.md
index 01a9c39d1..ce34ea6e9 100644
--- a/website/docs/usage/101/_serialization.md
+++ b/website/docs/usage/101/_serialization.md
@@ -1,9 +1,9 @@
 If you've been modifying the pipeline, vocabulary, vectors and entities, or made
-updates to the model, you'll eventually want to **save your progress** – for
-example, everything that's in your `nlp` object. This means you'll have to
-translate its contents and structure into a format that can be saved, like a
-file or a byte string. This process is called serialization. spaCy comes with
-**built-in serialization methods** and supports the
+updates to the component models, you'll eventually want to **save your
+progress** – for example, everything that's in your `nlp` object. This means
+you'll have to translate its contents and structure into a format that can be
+saved, like a file or a byte string. This process is called serialization. spaCy
+comes with **built-in serialization methods** and supports the
 [Pickle protocol](https://www.diveinto.org/python3/serializing.html#dump).
 
 > #### What's pickle?
diff --git a/website/docs/usage/101/_training.md b/website/docs/usage/101/_training.md
index 4573f5ea3..b73a83d6a 100644
--- a/website/docs/usage/101/_training.md
+++ b/website/docs/usage/101/_training.md
@@ -1,25 +1,25 @@
 spaCy's tagger, parser, text categorizer and many other components are powered
 by **statistical models**. Every "decision" these components make – for example,
 which part-of-speech tag to assign, or whether a word is a named entity – is a
-**prediction** based on the model's current **weight values**. The weight
-values are estimated based on examples the model has seen
-during **training**. To train a model, you first need training data – examples
-of text, and the labels you want the model to predict. This could be a
-part-of-speech tag, a named entity or any other information.
+**prediction** based on the model's current **weight values**. The weight values
+are estimated based on examples the model has seen during **training**. To train
+a model, you first need training data – examples of text, and the labels you
+want the model to predict. This could be a part-of-speech tag, a named entity or
+any other information.
 
-Training is an iterative process in which the model's predictions are compared 
+Training is an iterative process in which the model's predictions are compared
 against the reference annotations in order to estimate the **gradient of the
 loss**. The gradient of the loss is then used to calculate the gradient of the
 weights through [backpropagation](https://thinc.ai/backprop101). The gradients
-indicate how the weight values should be changed so that the model's
-predictions become more similar to the reference labels over time. 
+indicate how the weight values should be changed so that the model's predictions
+become more similar to the reference labels over time.
 
 > - **Training data:** Examples and their annotations.
 > - **Text:** The input text the model should predict a label for.
 > - **Label:** The label the model should predict.
 > - **Gradient:** The direction and rate of change for a numeric value.
->   Minimising the gradient of the weights should result in predictions that
->   are closer to the reference labels on the training data.
+>   Minimising the gradient of the weights should result in predictions that are
+>   closer to the reference labels on the training data.
 
 ![The training process](../../images/training.svg)
 
diff --git a/website/docs/usage/101/_vectors-similarity.md b/website/docs/usage/101/_vectors-similarity.md
index 92df1b331..cf5b70af2 100644
--- a/website/docs/usage/101/_vectors-similarity.md
+++ b/website/docs/usage/101/_vectors-similarity.md
@@ -24,12 +24,12 @@ array([2.02280000e-01,  -7.66180009e-02,   3.70319992e-01,
 
 <Infobox title="Important note" variant="warning">
 
-To make them compact and fast, spaCy's small [models](/models) (all packages
-that end in `sm`) **don't ship with word vectors**, and only include
+To make them compact and fast, spaCy's small [pipeline packages](/models) (all
+packages that end in `sm`) **don't ship with word vectors**, and only include
 context-sensitive **tensors**. This means you can still use the `similarity()`
 methods to compare documents, spans and tokens – but the result won't be as
 good, and individual tokens won't have any vectors assigned. So in order to use
-_real_ word vectors, you need to download a larger model:
+_real_ word vectors, you need to download a larger pipeline package:
 
 ```diff
 - python -m spacy download en_core_web_sm
@@ -38,11 +38,11 @@ _real_ word vectors, you need to download a larger model:
 
 </Infobox>
 
-Models that come with built-in word vectors make them available as the
-[`Token.vector`](/api/token#vector) attribute. [`Doc.vector`](/api/doc#vector)
-and [`Span.vector`](/api/span#vector) will default to an average of their token
-vectors. You can also check if a token has a vector assigned, and get the L2
-norm, which can be used to normalize vectors.
+Pipeline packages that come with built-in word vectors make them available as
+the [`Token.vector`](/api/token#vector) attribute.
+[`Doc.vector`](/api/doc#vector) and [`Span.vector`](/api/span#vector) will
+default to an average of their token vectors. You can also check if a token has
+a vector assigned, and get the L2 norm, which can be used to normalize vectors.
 
 ```python
 ### {executable="true"}
@@ -62,12 +62,12 @@ for token in tokens:
 > - **OOV**: Out-of-vocabulary
 
 The words "dog", "cat" and "banana" are all pretty common in English, so they're
-part of the model's vocabulary, and come with a vector. The word "afskfsd" on
+part of the pipeline's vocabulary, and come with a vector. The word "afskfsd" on
 the other hand is a lot less common and out-of-vocabulary – so its vector
 representation consists of 300 dimensions of `0`, which means it's practically
 nonexistent. If your application will benefit from a **large vocabulary** with
-more vectors, you should consider using one of the larger models or loading in a
-full vector package, for example,
+more vectors, you should consider using one of the larger pipeline packages or
+loading in a full vector package, for example,
 [`en_vectors_web_lg`](/models/en-starters#en_vectors_web_lg), which includes
 over **1 million unique vectors**.
 
@@ -82,7 +82,7 @@ Each [`Doc`](/api/doc), [`Span`](/api/span), [`Token`](/api/token) and
 method that lets you compare it with another object, and determine the
 similarity. Of course similarity is always subjective – whether two words, spans
 or documents are similar really depends on how you're looking at it. spaCy's
-similarity model usually assumes a pretty general-purpose definition of
+similarity implementation usually assumes a pretty general-purpose definition of
 similarity.
 
 > #### 📝 Things to try
@@ -99,7 +99,7 @@ similarity.
 ### {executable="true"}
 import spacy
 
-nlp = spacy.load("en_core_web_md")  # make sure to use larger model!
+nlp = spacy.load("en_core_web_md")  # make sure to use larger package!
 doc1 = nlp("I like salty fries and hamburgers.")
 doc2 = nlp("Fast food tastes very good.")
 
@@ -143,10 +143,9 @@ us that builds on top of spaCy and lets you train and query more interesting and
 detailed word vectors. It combines noun phrases like "fast food" or "fair game"
 and includes the part-of-speech tags and entity labels. The library also
 includes annotation recipes for our annotation tool [Prodigy](https://prodi.gy)
-that let you evaluate vector models and create terminology lists. For more
-details, check out
-[our blog post](https://explosion.ai/blog/sense2vec-reloaded). To explore the
-semantic similarities across all Reddit comments of 2015 and 2019, see the
-[interactive demo](https://explosion.ai/demos/sense2vec).
+that let you evaluate vectors and create terminology lists. For more details,
+check out [our blog post](https://explosion.ai/blog/sense2vec-reloaded). To
+explore the semantic similarities across all Reddit comments of 2015 and 2019,
+see the [interactive demo](https://explosion.ai/demos/sense2vec).
 
 </Infobox>
diff --git a/website/docs/usage/embeddings-transformers.md b/website/docs/usage/embeddings-transformers.md
index 75be71845..abd92a8ac 100644
--- a/website/docs/usage/embeddings-transformers.md
+++ b/website/docs/usage/embeddings-transformers.md
@@ -225,7 +225,7 @@ transformers as subnetworks directly, you can also use them via the
 
 ![The processing pipeline with the transformer component](../images/pipeline_transformer.svg)
 
-By default, the `Transformer` component sets the
+The `Transformer` component sets the
 [`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
 which lets you access the transformers outputs at runtime.
 
@@ -251,13 +251,14 @@ for doc in nlp.pipe(["some text", "some other text"]):
     tokvecs = doc._.trf_data.tensors[-1]
 ```
 
-You can customize how the [`Transformer`](/api/transformer) component sets
-annotations onto the [`Doc`](/api/doc), by changing the `annotation_setter`.
-This callback will be called with the raw input and output data for the whole
-batch, along with the batch of `Doc` objects, allowing you to implement whatever
-you need. The annotation setter is called with a batch of [`Doc`](/api/doc)
-objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
-containing the transformers data for the batch.
+You can also customize how the [`Transformer`](/api/transformer) component sets
+annotations onto the [`Doc`](/api/doc), by specifying a custom
+`annotation_setter`. This callback will be called with the raw input and output
+data for the whole batch, along with the batch of `Doc` objects, allowing you to
+implement whatever you need. The annotation setter is called with a batch of
+[`Doc`](/api/doc) objects and a
+[`FullTransformerBatch`](/api/transformer#fulltransformerbatch) containing the
+transformers data for the batch.
 
 ```python
 def custom_annotation_setter(docs, trf_data):
@@ -303,7 +304,7 @@ component:
 >
 > ```python
 > from spacy_transformers import Transformer, TransformerModel
-> from spacy_transformers.annotation_setters import configure_trfdata_setter
+> from spacy_transformers.annotation_setters import null_annotation_setter
 > from spacy_transformers.span_getters import get_doc_spans
 >
 > trf = Transformer(
@@ -313,7 +314,7 @@ component:
 >         get_spans=get_doc_spans,
 >         tokenizer_config={"use_fast": True},
 >     ),
->     annotation_setter=configure_trfdata_setter(),
+>     annotation_setter=null_annotation_setter,
 >     max_batch_items=4096,
 > )
 > ```
@@ -330,10 +331,10 @@ name = "bert-base-cased"
 tokenizer_config = {"use_fast": true}
 
 [components.transformer.model.get_spans]
-@span_getters = "doc_spans.v1"
+@span_getters = "spacy-transformers.doc_spans.v1"
 
 [components.transformer.annotation_setter]
-@annotation_setters = "spacy-transformers.trfdata_setter.v1"
+@annotation_setters = "spacy-transformers.null_annotation_setter.v1"
 
 ```
 
@@ -368,8 +369,9 @@ all defaults.
 
 To change any of the settings, you can edit the `config.cfg` and re-run the
 training. To change any of the functions, like the span getter, you can replace
-the name of the referenced function – e.g. `@span_getters = "sent_spans.v1"` to
-process sentences. You can also register your own functions using the
+the name of the referenced function – e.g.
+`@span_getters = "spacy-transformers.sent_spans.v1"` to process sentences. You
+can also register your own functions using the
 [`span_getters` registry](/api/top-level#registry). For instance, the following
 custom function returns [`Span`](/api/span) objects following sentence
 boundaries, unless a sentence succeeds a certain amount of tokens, in which case
@@ -577,7 +579,12 @@ def MyCustomVectors(
 
 ## Pretraining {#pretraining}
 
-<!-- TODO: write -->
+- explain general concept and idea (short!)
+- present it as a separate lightweight mechanism for pretraining the tok2vec
+  layer
+- advantages (could also be pros/cons table)
+- explain how it generates a separate file (!) and how it depends on the same
+  vectors
 
 > #### Raw text format
 >
diff --git a/website/docs/usage/index.md b/website/docs/usage/index.md
index 76858213c..ee5fd0a3b 100644
--- a/website/docs/usage/index.md
+++ b/website/docs/usage/index.md
@@ -35,10 +35,10 @@ Using pip, spaCy releases are available as source packages and binary wheels.
 $ pip install -U spacy
 ```
 
-> #### Download models
+> #### Download pipelines
 >
-> After installation you need to download a language model. For more info and
-> available models, see the [docs on models](/models).
+> After installation you typically want to download a trained pipeline. For more
+> info and available packages, see the [models directory](/models).
 >
 > ```cli
 > $ python -m spacy download en_core_web_sm
@@ -54,7 +54,7 @@ To install additional data tables for lemmatization you can run
 [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
 separately. The lookups package is needed to provide normalization and
 lemmatization data for new models and to lemmatize in languages that don't yet
-come with pretrained models and aren't powered by third-party libraries.
+come with trained pipelines and aren't powered by third-party libraries.
 
 </Infobox>
 
@@ -88,23 +88,21 @@ and pull requests to the recipe and setup are always appreciated.
 > spaCy v2.x to v3.x may still require some changes to your code base. For
 > details see the sections on [backwards incompatibilities](/usage/v3#incompat)
 > and [migrating](/usage/v3#migrating). Also remember to download the new
-> models, and retrain your own models.
+> trained pipelines, and retrain your own pipelines.
 
 When updating to a newer version of spaCy, it's generally recommended to start
 with a clean virtual environment. If you're upgrading to a new major version,
-make sure you have the latest **compatible models** installed, and that there
-are no old and incompatible model packages left over in your environment, as
-this can often lead to unexpected results and errors. If you've trained your own
-models, keep in mind that your train and runtime inputs must match. This means
-you'll have to **retrain your models** with the new version.
+make sure you have the latest **compatible trained pipelines** installed, and
+that there are no old and incompatible packages left over in your environment,
+as this can often lead to unexpected results and errors. If you've trained your
+own models, keep in mind that your train and runtime inputs must match. This
+means you'll have to **retrain your pipelines** with the new version.
 
 spaCy also provides a [`validate`](/api/cli#validate) command, which lets you
-verify that all installed models are compatible with your spaCy version. If
-incompatible models are found, tips and installation instructions are printed.
-The command is also useful to detect out-of-sync model links resulting from
-links created in different virtual environments. It's recommended to run the
-command with `python -m` to make sure you're executing the correct version of
-spaCy.
+verify that all installed pipeline packages are compatible with your spaCy
+version. If incompatible packages are found, tips and installation instructions
+are printed. It's recommended to run the command with `python -m` to make sure
+you're executing the correct version of spaCy.
 
 ```cli
 $ pip install -U spacy
@@ -132,8 +130,8 @@ $ pip install -U spacy[cuda92]
 Once you have a GPU-enabled installation, the best way to activate it is to call
 [`spacy.prefer_gpu`](/api/top-level#spacy.prefer_gpu) or
 [`spacy.require_gpu()`](/api/top-level#spacy.require_gpu) somewhere in your
-script before any models have been loaded. `require_gpu` will raise an error if
-no GPU is available.
+script before any pipelines have been loaded. `require_gpu` will raise an error
+if no GPU is available.
 
 ```python
 import spacy
@@ -238,16 +236,16 @@ installing, loading and using spaCy, as well as their solutions.
 <Accordion title="No compatible model found" id="compatible-model">
 
 ```
-No compatible model found for [lang] (spaCy vX.X.X).
+No compatible package found for [lang] (spaCy vX.X.X).
 ```
 
-This usually means that the model you're trying to download does not exist, or
-isn't available for your version of spaCy. Check the
+This usually means that the trained pipeline you're trying to download does not
+exist, or isn't available for your version of spaCy. Check the
 [compatibility table](https://github.com/explosion/spacy-models/tree/master/compatibility.json)
-to see which models are available for your spaCy version. If you're using an old
-version, consider upgrading to the latest release. Note that while spaCy
+to see which packages are available for your spaCy version. If you're using an
+old version, consider upgrading to the latest release. Note that while spaCy
 supports tokenization for [a variety of languages](/usage/models#languages), not
-all of them come with statistical models. To only use the tokenizer, import the
+all of them come with trained pipelines. To only use the tokenizer, import the
 language's `Language` class instead, for example
 `from spacy.lang.fr import French`.
 
@@ -259,7 +257,7 @@ language's `Language` class instead, for example
 no such option: --no-cache-dir
 ```
 
-The `download` command uses pip to install the models and sets the
+The `download` command uses pip to install the pipeline packages and sets the
 `--no-cache-dir` flag to prevent it from requiring too much memory.
 [This setting](https://pip.pypa.io/en/stable/reference/pip_install/#caching)
 requires pip v6.0 or newer. Run `pip install -U pip` to upgrade to the latest
@@ -323,19 +321,19 @@ also run `which python` to find out where your Python executable is located.
 
 </Accordion>
 
-<Accordion title="Import error: No module named [model]" id="import-error-models">
+<Accordion title="Import error: No module named [name]" id="import-error-models">
 
 ```
 ImportError: No module named 'en_core_web_sm'
 ```
 
-As of spaCy v1.7, all models can be installed as Python packages. This means
-that they'll become importable modules of your application. If this fails, it's
-usually a sign that the package is not installed in the current environment. Run
-`pip list` or `pip freeze` to check which model packages you have installed, and
-install the [correct models](/models) if necessary. If you're importing a model
-manually at the top of a file, make sure to use the name of the package, not the
-shortcut link you've created.
+As of spaCy v1.7, all trained pipelines can be installed as Python packages.
+This means that they'll become importable modules of your application. If this
+fails, it's usually a sign that the package is not installed in the current
+environment. Run `pip list` or `pip freeze` to check which pipeline packages you
+have installed, and install the [correct package](/models) if necessary. If
+you're importing a package manually at the top of a file, make sure to use the
+full name of the package.
 
 </Accordion>
 
diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md
index aa398f752..e24b776c8 100644
--- a/website/docs/usage/layers-architectures.md
+++ b/website/docs/usage/layers-architectures.md
@@ -3,57 +3,79 @@ title: Layers and Model Architectures
 teaser: Power spaCy components with custom neural networks
 menu:
   - ['Type Signatures', 'type-sigs']
-  - ['Defining Sublayers', 'sublayers']
+  - ['Swapping Architectures', 'swap-architectures']
   - ['PyTorch & TensorFlow', 'frameworks']
+  - ['Thinc Models', 'thinc']
   - ['Trainable Components', 'components']
 next: /usage/projects
 ---
 
-​A **model architecture** is a function that wires up a
-[Thinc `Model`](https://thinc.ai/docs/api-model) instance, which you can then
-use in a component or as a layer of a larger network. You can use Thinc as a
-thin wrapper around frameworks such as PyTorch, TensorFlow or MXNet, or you can
-implement your logic in Thinc directly. ​ spaCy's built-in components will never
-construct their `Model` instances themselves, so you won't have to subclass the
-component to change its model architecture. You can just **update the config**
-so that it refers to a different registered function. Once the component has
-been created, its model instance has already been assigned, so you cannot change
-its model architecture. The architecture is like a recipe for the network, and
-you can't change the recipe once the dish has already been prepared. You have to
-make a new one.
+> #### Example
+>
+> ```python
+> from thinc.api import Model, chain
+>
+> @spacy.registry.architectures.register("model.v1")
+> def build_model(width: int, classes: int) -> Model:
+>     tok2vec = build_tok2vec(width)
+>     output_layer = build_output_layer(width, classes)
+>     model = chain(tok2vec, output_layer)
+>     return model
+> ```
 
-![Diagram of a pipeline component with its model](../images/layers-architectures.svg)
+A **model architecture** is a function that wires up a
+[Thinc `Model`](https://thinc.ai/docs/api-model) instance. It describes the
+neural network that is run internally as part of a component in a spaCy
+pipeline. To define the actual architecture, you can implement your logic in
+Thinc directly, or you can use Thinc as a thin wrapper around frameworks such as
+PyTorch, TensorFlow and MXNet. Each Model can also be used as a sublayer of a
+larger network, allowing you to freely combine implementations from different
+frameworks into one `Thinc` Model.
+
+spaCy's built-in components require a `Model` instance to be passed to them via
+the config system. To change the model architecture of an existing component,
+you just need to [**update the config**](#swap-architectures) so that it refers
+to a different registered function. Once the component has been created from
+this config, you won't be able to change it anymore. The architecture is like a
+recipe for the network, and you can't change the recipe once the dish has
+already been prepared. You have to make a new one.
+
+```ini
+### config.cfg (excerpt)
+[components.tagger]
+factory = "tagger"
+
+[components.tagger.model]
+@architectures = "model.v1"
+width = 512
+classes = 16
+```
 
 ## Type signatures {#type-sigs}
 
-<!-- TODO: update example, maybe simplify definition? -->
-
 > #### Example
 >
 > ```python
-> @spacy.registry.architectures.register("spacy.Tagger.v1")
-> def build_tagger_model(
->     tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None
-> ) -> Model[List[Doc], List[Floats2d]]:
->     t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
->     output_layer = Softmax(nO, t2v_width, init_W=zero_init)
->     softmax = with_array(output_layer)
->     model = chain(tok2vec, softmax)
->     model.set_ref("tok2vec", tok2vec)
->     model.set_ref("softmax", output_layer)
->     model.set_ref("output_layer", output_layer)
+> from typing import List
+> from thinc.api import Model, chain
+> from thinc.types import Floats2d
+> def chain_model(
+>     tok2vec: Model[List[Doc], List[Floats2d]],
+>     layer1: Model[List[Floats2d], Floats2d],
+>     layer2: Model[Floats2d, Floats2d]
+> ) -> Model[List[Doc], Floats2d]:
+>     model = chain(tok2vec, layer1, layer2)
 >     return model
 > ```
 
-​ The Thinc `Model` class is a **generic type** that can specify its input and
+The Thinc `Model` class is a **generic type** that can specify its input and
 output types. Python uses a square-bracket notation for this, so the type
 ~~Model[List, Dict]~~ says that each batch of inputs to the model will be a
-list, and the outputs will be a dictionary. Both `typing.List` and `typing.Dict`
-are also generics, allowing you to be more specific about the data. For
-instance, you can write ~~Model[List[Doc], Dict[str, float]]~~ to specify that
-the model expects a list of [`Doc`](/api/doc) objects as input, and returns a
-dictionary mapping strings to floats. Some of the most common types you'll see
-are: ​
+list, and the outputs will be a dictionary. You can be even more specific and
+write for instance~~Model[List[Doc], Dict[str, float]]~~ to specify that the
+model expects a list of [`Doc`](/api/doc) objects as input, and returns a
+dictionary mapping of strings to floats. Some of the most common types you'll
+see are: ​
 
 | Type               | Description                                                                                          |
 | ------------------ | ---------------------------------------------------------------------------------------------------- |
@@ -62,7 +84,7 @@ are: ​
 | ~~Ints2d~~         | A two-dimensional `numpy` or `cupy` array of integers. Common dtypes include uint64, int32 and int8. |
 | ~~List[Floats2d]~~ | A list of two-dimensional arrays, generally with one array per `Doc` and one row per token.          |
 | ~~Ragged~~         | A container to handle variable-length sequence data in an unpadded contiguous array.                 |
-| ~~Padded~~         | A container to handle variable-length sequence data in a passed contiguous array.                    |
+| ~~Padded~~         | A container to handle variable-length sequence data in a padded contiguous array.                    |
 
 The model type signatures help you figure out which model architectures and
 components can **fit together**. For instance, the
@@ -78,10 +100,10 @@ interchangeably. There are many other ways they could be incompatible. However,
 if the types don't match, they almost surely _won't_ be compatible. This little
 bit of validation goes a long way, especially if you
 [configure your editor](https://thinc.ai/docs/usage-type-checking) or other
-tools to highlight these errors early. Thinc will also verify that your types
-match correctly when your config file is processed at the beginning of training.
+tools to highlight these errors early. The config file is also validated at the
+beginning of training, to verify that all the types match correctly.
 
-<Infobox title="Tip: Static type checking in your editor" emoji="💡">
+<Accordion title="Tip: Static type checking in your editor">
 
 If you're using a modern editor like Visual Studio Code, you can
 [set up `mypy`](https://thinc.ai/docs/usage-type-checking#install) with the
@@ -90,86 +112,144 @@ code.
 
 [![](../images/thinc_mypy.jpg)](https://thinc.ai/docs/usage-type-checking#linting)
 
-</Infobox>
+</Accordion>
 
-## Defining sublayers {#sublayers}
+## Swapping model architectures {#swap-architectures}
 
-​ Model architecture functions often accept **sublayers as arguments**, so that
+If no model is specified for the [`TextCategorizer`](/api/textcategorizer), the
+[TextCatEnsemble](/api/architectures#TextCatEnsemble) architecture is used by
+default. This architecture combines a simpel bag-of-words model with a neural
+network, usually resulting in the most accurate results, but at the cost of
+speed. The config file for this model would look something like this:
+
+```ini
+### config.cfg (excerpt)
+[components.textcat]
+factory = "textcat"
+labels = []
+
+[components.textcat.model]
+@architectures = "spacy.TextCatEnsemble.v1"
+exclusive_classes = false
+pretrained_vectors = null
+width = 64
+conv_depth = 2
+embed_size = 2000
+window_size = 1
+ngram_size = 1
+dropout = 0
+nO = null
+```
+
+spaCy has two additional built-in `textcat` architectures, and you can easily
+use those by swapping out the definition of the textcat's model. For instance,
+to use the simple and fast bag-of-words model
+[TextCatBOW](/api/architectures#TextCatBOW), you can change the config to:
+
+```ini
+### config.cfg (excerpt) {highlight="6-10"}
+[components.textcat]
+factory = "textcat"
+labels = []
+
+[components.textcat.model]
+@architectures = "spacy.TextCatBOW.v1"
+exclusive_classes = false
+ngram_size = 1
+no_output_layer = false
+nO = null
+```
+
+For details on all pre-defined architectures shipped with spaCy and how to
+configure them, check out the [model architectures](/api/architectures)
+documentation.
+
+### Defining sublayers {#sublayers}
+
+Model architecture functions often accept **sublayers as arguments**, so that
 you can try **substituting a different layer** into the network. Depending on
 how the architecture function is structured, you might be able to define your
 network structure entirely through the [config system](/usage/training#config),
-using layers that have already been defined. ​The
-[transformers documentation](/usage/embeddings-transformers#transformers)
-section shows a common example of swapping in a different sublayer.
+using layers that have already been defined. ​
 
 In most neural network models for NLP, the most important parts of the network
 are what we refer to as the
-[embed and encode](https://explosion.ai/blog/embed-encode-attend-predict) steps.
+[embed and encode](https://explosion.ai/blog/deep-learning-formula-nlp) steps.
 These steps together compute dense, context-sensitive representations of the
-tokens. Most of spaCy's default architectures accept a
-[`tok2vec` embedding layer](/api/architectures#tok2vec-arch) as an argument, so
-you can control this important part of the network separately. This makes it
-easy to **switch between** transformer, CNN, BiLSTM or other feature extraction
-approaches. And if you want to define your own solution, all you need to do is
-register a ~~Model[List[Doc], List[Floats2d]]~~ architecture function, and
-you'll be able to try it out in any of spaCy components. ​
+tokens, and their combination forms a typical
+[`Tok2Vec`](/api/architectures#Tok2Vec) layer:
 
-<!-- TODO: example of switching sublayers -->
+```ini
+### config.cfg (excerpt)
+[components.tok2vec]
+factory = "tok2vec"
 
-### Registering new architectures
+[components.tok2vec.model]
+@architectures = "spacy.Tok2Vec.v1"
 
-- Recap concept, link to config docs. ​
+[components.tok2vec.model.embed]
+@architectures = "spacy.MultiHashEmbed.v1"
+# ...
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v1"
+# ...
+```
+
+By defining these sublayers specifically, it becomes straightforward to swap out
+a sublayer for another one, for instance changing the first sublayer to a
+character embedding with the [CharacterEmbed](/api/architectures#CharacterEmbed)
+architecture:
+
+```ini
+### config.cfg (excerpt)
+[components.tok2vec.model.embed]
+@architectures = "spacy.CharacterEmbed.v1"
+# ...
+
+[components.tok2vec.model.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v1"
+# ...
+```
+
+Most of spaCy's default architectures accept a `tok2vec` layer as a sublayer
+within the larger task-specific neural network. This makes it easy to **switch
+between** transformer, CNN, BiLSTM or other feature extraction approaches. The
+[transformers documentation](/usage/embeddings-transformers#training-custom-model)
+section shows an example of swapping out a model's standard `tok2vec` layer with
+a transformer. And if you want to define your own solution, all you need to do
+is register a ~~Model[List[Doc], List[Floats2d]]~~ architecture function, and
+you'll be able to try it out in any of the spaCy components. ​
 
 ## Wrapping PyTorch, TensorFlow and other frameworks {#frameworks}
 
-<!-- TODO: this is copied over from the Thinc docs and we probably want to shorten it and make it more spaCy-specific -->
+Thinc allows you to [wrap models](https://thinc.ai/docs/usage-frameworks)
+written in other machine learning frameworks like PyTorch, TensorFlow and MXNet
+using a unified [`Model`](https://thinc.ai/docs/api-model) API. As well as
+**wrapping whole models**, Thinc lets you call into an external framework for
+just **part of your model**: you can have a model where you use PyTorch just for
+the transformer layers, using "native" Thinc layers to do fiddly input and
+output transformations and add on task-specific "heads", as efficiency is less
+of a consideration for those parts of the network.
 
-Thinc allows you to wrap models written in other machine learning frameworks
-like PyTorch, TensorFlow and MXNet using a unified
-[`Model`](https://thinc.ai/docs/api-model) API. As well as **wrapping whole
-models**, Thinc lets you call into an external framework for just **part of your
-model**: you can have a model where you use PyTorch just for the transformer
-layers, using "native" Thinc layers to do fiddly input and output
-transformations and add on task-specific "heads", as efficiency is less of a
-consideration for those parts of the network.
+<!-- TODO: custom tagger implemented in PyTorch, wrapped as Thinc model, link off to project (with notebook?) -->
 
-Thinc uses a special class, [`Shim`](https://thinc.ai/docs/api-model#shim), to
-hold references to external objects. This allows each wrapper space to define a
-custom type, with whatever attributes and methods are helpful, to assist in
-managing the communication between Thinc and the external library. The
-[`Model`](https://thinc.ai/docs/api-model#model) class holds `shim` instances in
-a separate list, and communicates with the shims about updates, serialization,
-changes of device, etc.
+## Implementing models in Thinc {#thinc}
 
-The wrapper will receive each batch of inputs, convert them into a suitable form
-for the underlying model instance, and pass them over to the shim, which will
-**manage the actual communication** with the model. The output is then passed
-back into the wrapper, and converted for use in the rest of the network. The
-equivalent procedure happens during backpropagation. Array conversion is handled
-via the [DLPack](https://github.com/dmlc/dlpack) standard wherever possible, so
-that data can be passed between the frameworks **without copying the data back**
-to the host device unnecessarily.
-
-| Framework      | Wrapper layer                                                             | Shim                                                      | DLPack          |
-| -------------- | ------------------------------------------------------------------------- | --------------------------------------------------------- | --------------- |
-| **PyTorch**    | [`PyTorchWrapper`](https://thinc.ai/docs/api-layers#pytorchwrapper)       | [`PyTorchShim`](https://thinc.ai/docs/api-model#shims)    | ✅              |
-| **TensorFlow** | [`TensorFlowWrapper`](https://thinc.ai/docs/api-layers#tensorflowwrapper) | [`TensorFlowShim`](https://thinc.ai/docs/api-model#shims) | ❌ <sup>1</sup> |
-| **MXNet**      | [`MXNetWrapper`](https://thinc.ai/docs/api-layers#mxnetwrapper)           | [`MXNetShim`](https://thinc.ai/docs/api-model#shims)      | ✅              |
-
-1. DLPack support in TensorFlow is now
-   [available](<(https://github.com/tensorflow/tensorflow/issues/24453)>) but
-   still experimental.
-
-<!-- TODO:
-- Explain concept
-- Link off to notebook ​
--->
+<!-- TODO: use same example as above, custom tagger, but implemented in Thinc, link off to Thinc docs where appropriate -->
 
 ## Models for trainable components {#components}
 
+<!-- TODO:
+
 - Interaction with `predict`, `get_loss` and `set_annotations`
 - Initialization life-cycle with `begin_training`.
-- Link to relation extraction notebook.
+
+Example: relation extraction component (implemented as project template)
+
+-->
+
+![Diagram of a pipeline component with its model](../images/layers-architectures.svg)
 
 ```python
 def update(self, examples):
diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
index 726cf0521..ff08d547c 100644
--- a/website/docs/usage/linguistic-features.md
+++ b/website/docs/usage/linguistic-features.md
@@ -132,7 +132,7 @@ language can extend the `Lemmatizer` as part of its
 ### {executable="true"}
 import spacy
 
-# English models include a rule-based lemmatizer
+# English pipelines include a rule-based lemmatizer
 nlp = spacy.load("en_core_web_sm")
 lemmatizer = nlp.get_pipe("lemmatizer")
 print(lemmatizer.mode)  # 'rule'
@@ -156,14 +156,14 @@ component.
 
 The data for spaCy's lemmatizers is distributed in the package
 [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). The
-provided models already include all the required tables, but if you are creating
-new models, you'll probably want to install `spacy-lookups-data` to provide the
-data when the lemmatizer is initialized.
+provided trained pipelines already include all the required tables, but if you
+are creating new pipelines, you'll probably want to install `spacy-lookups-data`
+to provide the data when the lemmatizer is initialized.
 
 ### Lookup lemmatizer {#lemmatizer-lookup}
 
-For models without a tagger or morphologizer, a lookup lemmatizer can be added
-to the pipeline as long as a lookup table is provided, typically through
+For pipelines without a tagger or morphologizer, a lookup lemmatizer can be
+added to the pipeline as long as a lookup table is provided, typically through
 [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). The
 lookup lemmatizer looks up the token surface form in the lookup table without
 reference to the token's part-of-speech or context.
@@ -178,9 +178,9 @@ nlp.add_pipe("lemmatizer", config={"mode": "lookup"})
 
 ### Rule-based lemmatizer {#lemmatizer-rule}
 
-When training models that include a component that assigns POS (a morphologizer
-or a tagger with a [POS mapping](#mappings-exceptions)), a rule-based lemmatizer
-can be added using rule tables from
+When training pipelines that include a component that assigns part-of-speech
+tags (a morphologizer or a tagger with a [POS mapping](#mappings-exceptions)), a
+rule-based lemmatizer can be added using rule tables from
 [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data):
 
 ```python
@@ -366,10 +366,10 @@ sequence of tokens. You can walk up the tree with the
 
 > #### Projective vs. non-projective
 >
-> For the [default English model](/models/en), the parse tree is **projective**,
-> which means that there are no crossing brackets. The tokens returned by
-> `.subtree` are therefore guaranteed to be contiguous. This is not true for the
-> German model, which has many
+> For the [default English pipelines](/models/en), the parse tree is
+> **projective**, which means that there are no crossing brackets. The tokens
+> returned by `.subtree` are therefore guaranteed to be contiguous. This is not
+> true for the German pipelines, which have many
 > [non-projective dependencies](https://explosion.ai/blog/german-model#word-order).
 
 ```python
@@ -497,26 +497,27 @@ displaCy in our [online demo](https://explosion.ai/demos/displacy)..
 
 ### Disabling the parser {#disabling}
 
-In the [default models](/models), the parser is loaded and enabled as part of
-the [standard processing pipeline](/usage/processing-pipelines). If you don't
-need any of the syntactic information, you should disable the parser. Disabling
-the parser will make spaCy load and run much faster. If you want to load the
-parser, but need to disable it for specific documents, you can also control its
-use on the `nlp` object.
+In the [trained pipelines](/models) provided by spaCy, the parser is loaded and
+enabled by default as part of the
+[standard processing pipeline](/usage/processing-pipelines). If you don't need
+any of the syntactic information, you should disable the parser. Disabling the
+parser will make spaCy load and run much faster. If you want to load the parser,
+but need to disable it for specific documents, you can also control its use on
+the `nlp` object. For more details, see the usage guide on
+[disabling pipeline components](/usage/processing-pipelines/#disabling).
 
 ```python
 nlp = spacy.load("en_core_web_sm", disable=["parser"])
-nlp = English().from_disk("/model", disable=["parser"])
-doc = nlp("I don't want parsed", disable=["parser"])
 ```
 
 ## Named Entity Recognition {#named-entities}
 
 spaCy features an extremely fast statistical entity recognition system, that
-assigns labels to contiguous spans of tokens. The default model identifies a
-variety of named and numeric entities, including companies, locations,
-organizations and products. You can add arbitrary classes to the entity
-recognition system, and update the model with new examples.
+assigns labels to contiguous spans of tokens. The default
+[trained pipelines](/models) can indentify a variety of named and numeric
+entities, including companies, locations, organizations and products. You can
+add arbitrary classes to the entity recognition system, and update the model
+with new examples.
 
 ### Named Entity Recognition 101 {#named-entities-101}
 
@@ -669,7 +670,7 @@ responsibility for ensuring that the data is left in a consistent state.
 
 <Infobox title="Annotation scheme">
 
-For details on the entity types available in spaCy's pretrained models, see the
+For details on the entity types available in spaCy's trained pipelines, see the
 "label scheme" sections of the individual models in the
 [models directory](/models).
 
@@ -710,9 +711,8 @@ import DisplacyEntHtml from 'images/displacy-ent2.html'
 To ground the named entities into the "real world", spaCy provides functionality
 to perform entity linking, which resolves a textual entity to a unique
 identifier from a knowledge base (KB). You can create your own
-[`KnowledgeBase`](/api/kb) and
-[train a new Entity Linking model](/usage/training#entity-linker) using that
-custom-made KB.
+[`KnowledgeBase`](/api/kb) and [train](/usage/training) a new
+[`EntityLinker`](/api/entitylinker) using that custom knowledge base.
 
 ### Accessing entity identifiers {#entity-linking-accessing model="entity linking"}
 
@@ -724,7 +724,7 @@ object, or the `ent_kb_id` and `ent_kb_id_` attributes of a
 ```python
 import spacy
 
-nlp = spacy.load("my_custom_el_model")
+nlp = spacy.load("my_custom_el_pipeline")
 doc = nlp("Ada Lovelace was born in London")
 
 # Document level
@@ -1042,13 +1042,15 @@ function that behaves the same way.
 
 <Infobox title="Important note" variant="warning">
 
-If you're using a statistical model, writing to the
+If you've loaded a trained pipeline, writing to the
 [`nlp.Defaults`](/api/language#defaults) or `English.Defaults` directly won't
-work, since the regular expressions are read from the model and will be compiled
-when you load it. If you modify `nlp.Defaults`, you'll only see the effect if
-you call [`spacy.blank`](/api/top-level#spacy.blank). If you want to modify the
-tokenizer loaded from a statistical model, you should modify `nlp.tokenizer`
-directly.
+work, since the regular expressions are read from the pipeline data and will be
+compiled when you load it. If you modify `nlp.Defaults`, you'll only see the
+effect if you call [`spacy.blank`](/api/top-level#spacy.blank). If you want to
+modify the tokenizer loaded from a trained pipeline, you should modify
+`nlp.tokenizer` directly. If you're training your own pipeline, you can register
+[callbacks](/usage/training/#custom-code-nlp-callbacks) to modify the `nlp`
+object before training.
 
 </Infobox>
 
@@ -1218,11 +1220,11 @@ print(doc.text, [token.text for token in doc])
 
 <Infobox title="Important note on tokenization and models" variant="warning">
 
-Keep in mind that your model's result may be less accurate if the tokenization
+Keep in mind that your models' results may be less accurate if the tokenization
 during training differs from the tokenization at runtime. So if you modify a
-pretrained model's tokenization afterwards, it may produce very different
-predictions. You should therefore train your model with the **same tokenizer**
-it will be using at runtime. See the docs on
+trained pipeline's tokenization afterwards, it may produce very different
+predictions. You should therefore train your pipeline with the **same
+tokenizer** it will be using at runtime. See the docs on
 [training with custom tokenization](#custom-tokenizer-training) for details.
 
 </Infobox>
@@ -1231,7 +1233,7 @@ it will be using at runtime. See the docs on
 
 spaCy's [training config](/usage/training#config) describe the settings,
 hyperparameters, pipeline and tokenizer used for constructing and training the
-model. The `[nlp.tokenizer]` block refers to a **registered function** that
+pipeline. The `[nlp.tokenizer]` block refers to a **registered function** that
 takes the `nlp` object and returns a tokenizer. Here, we're registering a
 function called `whitespace_tokenizer` in the
 [`@tokenizers` registry](/api/registry). To make sure spaCy knows how to
@@ -1626,11 +1628,11 @@ spaCy provides four alternatives for sentence segmentation:
 
 Unlike other libraries, spaCy uses the dependency parse to determine sentence
 boundaries. This is usually the most accurate approach, but it requires a
-**statistical model** that provides accurate predictions. If your texts are
+**trained pipeline** that provides accurate predictions. If your texts are
 closer to general-purpose news or web text, this should work well out-of-the-box
-with spaCy's provided models. For social media or conversational text that
-doesn't follow the same rules, your application may benefit from a custom model
-or rule-based component.
+with spaCy's provided trained pipelines. For social media or conversational text
+that doesn't follow the same rules, your application may benefit from a custom
+trained or rule-based component.
 
 ```python
 ### {executable="true"}
@@ -1652,8 +1654,8 @@ parses consistent with the sentence boundaries.
 The [`SentenceRecognizer`](/api/sentencerecognizer) is a simple statistical
 component that only provides sentence boundaries. Along with being faster and
 smaller than the parser, its primary advantage is that it's easier to train
-custom models because it only requires annotated sentence boundaries rather than
-full dependency parses.
+because it only requires annotated sentence boundaries rather than full
+dependency parses.
 
 <!-- TODO: update/confirm usage once we have final models trained -->
 
@@ -1685,7 +1687,7 @@ need sentence boundaries without dependency parses.
 import spacy
 from spacy.lang.en import English
 
-nlp = English()  # just the language with no model
+nlp = English()  # just the language with no pipeline
 nlp.add_pipe("sentencizer")
 doc = nlp("This is a sentence. This is another sentence.")
 for sent in doc.sents:
@@ -1827,11 +1829,11 @@ or Tomas Mikolov's original
 [Word2vec implementation](https://code.google.com/archive/p/word2vec/). Most
 word vector libraries output an easy-to-read text-based format, where each line
 consists of the word followed by its vector. For everyday use, we want to
-convert the vectors model into a binary format that loads faster and takes up
-less space on disk. The easiest way to do this is the
-[`init model`](/api/cli#init-model) command-line utility. This will output a
-spaCy model in the directory `/tmp/la_vectors_wiki_lg`, giving you access to
-some nice Latin vectors. You can then pass the directory path to
+convert the vectors into a binary format that loads faster and takes up less
+space on disk. The easiest way to do this is the
+[`init vocab`](/api/cli#init-vocab) command-line utility. This will output a
+blank spaCy pipeline in the directory `/tmp/la_vectors_wiki_lg`, giving you
+access to some nice Latin vectors. You can then pass the directory path to
 [`spacy.load`](/api/top-level#spacy.load).
 
 > #### Usage example
@@ -1845,7 +1847,7 @@ some nice Latin vectors. You can then pass the directory path to
 
 ```cli
 $ wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.la.300.vec.gz
-$ python -m spacy init model en /tmp/la_vectors_wiki_lg --vectors-loc cc.la.300.vec.gz
+$ python -m spacy init vocab en /tmp/la_vectors_wiki_lg --vectors-loc cc.la.300.vec.gz
 ```
 
 <Accordion title="How to optimize vector coverage" id="custom-vectors-coverage" spaced>
@@ -1853,13 +1855,13 @@ $ python -m spacy init model en /tmp/la_vectors_wiki_lg --vectors-loc cc.la.300.
 To help you strike a good balance between coverage and memory usage, spaCy's
 [`Vectors`](/api/vectors) class lets you map **multiple keys** to the **same
 row** of the table. If you're using the
-[`spacy init model`](/api/cli#init-model) command to create a vocabulary,
+[`spacy init vocab`](/api/cli#init-vocab) command to create a vocabulary,
 pruning the vectors will be taken care of automatically if you set the
 `--prune-vectors` flag. You can also do it manually in the following steps:
 
-1. Start with a **word vectors model** that covers a huge vocabulary. For
+1. Start with a **word vectors package** that covers a huge vocabulary. For
    instance, the [`en_vectors_web_lg`](/models/en-starters#en_vectors_web_lg)
-   model provides 300-dimensional GloVe vectors for over 1 million terms of
+   starter provides 300-dimensional GloVe vectors for over 1 million terms of
    English.
 2. If your vocabulary has values set for the `Lexeme.prob` attribute, the
    lexemes will be sorted by descending probability to determine which vectors
@@ -1900,17 +1902,17 @@ the two words.
 In the example above, the vector for "Shore" was removed and remapped to the
 vector of "coast", which is deemed about 73% similar. "Leaving" was remapped to
 the vector of "leaving", which is identical. If you're using the
-[`init model`](/api/cli#init-model) command, you can set the `--prune-vectors`
+[`init vocab`](/api/cli#init-vocab) command, you can set the `--prune-vectors`
 option to easily reduce the size of the vectors as you add them to a spaCy
-model:
+pipeline:
 
 ```cli
-$ python -m spacy init model en /tmp/la_vectors_web_md --vectors-loc la.300d.vec.tgz --prune-vectors 10000
+$ python -m spacy init vocab en /tmp/la_vectors_web_md --vectors-loc la.300d.vec.tgz --prune-vectors 10000
 ```
 
-This will create a spaCy model with vectors for the first 10,000 words in the
-vectors model. All other words in the vectors model are mapped to the closest
-vector among those retained.
+This will create a blank spaCy pipeline with vectors for the first 10,000 words
+in the vectors. All other words in the vectors are mapped to the closest vector
+among those retained.
 
 </Accordion>
 
@@ -1925,8 +1927,8 @@ possible. You can modify the vectors via the [`Vocab`](/api/vocab) or
 if you have vectors in an arbitrary format, as you can read in the vectors with
 your own logic, and just set them with a simple loop. This method is likely to
 be slower than approaches that work with the whole vectors table at once, but
-it's a great approach for once-off conversions before you save out your model to
-disk.
+it's a great approach for once-off conversions before you save out your `nlp`
+object to disk.
 
 ```python
 ### Adding vectors
@@ -1978,14 +1980,14 @@ print(nlp2.lang, [token.is_stop for token in nlp2("custom stop")])
 The [`@spacy.registry.languages`](/api/top-level#registry) decorator lets you
 register a custom language class and assign it a string name. This means that
 you can call [`spacy.blank`](/api/top-level#spacy.blank) with your custom
-language name, and even train models with it and refer to it in your
+language name, and even train pipelines with it and refer to it in your
 [training config](/usage/training#config).
 
 > #### Config usage
 >
 > After registering your custom language class using the `languages` registry,
 > you can refer to it in your [training config](/usage/training#config). This
-> means spaCy will train your model using the custom subclass.
+> means spaCy will train your pipeline using the custom subclass.
 >
 > ```ini
 > [nlp]
diff --git a/website/docs/usage/models.md b/website/docs/usage/models.md
index ec0e02297..9b1e96e4e 100644
--- a/website/docs/usage/models.md
+++ b/website/docs/usage/models.md
@@ -8,25 +8,24 @@ menu:
   - ['Production Use', 'production']
 ---
 
-spaCy's models can be installed as **Python packages**. This means that they're
-a component of your application, just like any other module. They're versioned
-and can be defined as a dependency in your `requirements.txt`. Models can be
-installed from a download URL or a local directory, manually or via
-[pip](https://pypi.python.org/pypi/pip). Their data can be located anywhere on
-your file system.
+spaCy's trained pipelines can be installed as **Python packages**. This means
+that they're a component of your application, just like any other module.
+They're versioned and can be defined as a dependency in your `requirements.txt`.
+Trained pipelines can be installed from a download URL or a local directory,
+manually or via [pip](https://pypi.python.org/pypi/pip). Their data can be
+located anywhere on your file system.
 
 > #### Important note
 >
-> If you're upgrading to spaCy v3.x, you need to **download the new models**. If
-> you've trained statistical models that use spaCy's annotations, you should
-> **retrain your models** after updating spaCy. If you don't retrain, you may
-> suffer train/test skew, which might decrease your accuracy.
+> If you're upgrading to spaCy v3.x, you need to **download the new pipeline
+> packages**. If you've trained your own pipelines, you need to **retrain** them
+> after updating spaCy.
 
 ## Quickstart {hidden="true"}
 
 import QuickstartModels from 'widgets/quickstart-models.js'
 
-<QuickstartModels title="Quickstart" id="quickstart" description="Install a default model, get the code to load it from within spaCy and an example to test it. For more options, see the section on available models below." />
+<QuickstartModels title="Quickstart" id="quickstart" description="Install a default trained pipeline package, get the code to load it from within spaCy and an example to test it. For more options, see the section on available packages below." />
 
 ## Language support {#languages}
 
@@ -34,14 +33,14 @@ spaCy currently provides support for the following languages. You can help by
 [improving the existing language data](/usage/adding-languages#language-data)
 and extending the tokenization patterns.
 [See here](https://github.com/explosion/spaCy/issues/3056) for details on how to
-contribute to model development.
+contribute to development.
 
 > #### Usage note
 >
-> If a model is available for a language, you can download it using the
-> [`spacy download`](/api/cli#download) command. In order to use languages that
-> don't yet come with a model, you have to import them directly, or use
-> [`spacy.blank`](/api/top-level#spacy.blank):
+> If a trained pipeline is available for a language, you can download it using
+> the [`spacy download`](/api/cli#download) command. In order to use languages
+> that don't yet come with a trained pipeline, you have to import them directly,
+> or use [`spacy.blank`](/api/top-level#spacy.blank):
 >
 > ```python
 > from spacy.lang.fi import Finnish
@@ -73,13 +72,13 @@ import Languages from 'widgets/languages.js'
 > nlp = spacy.blank("xx")
 > ```
 
-spaCy also supports models trained on more than one language. This is especially
-useful for named entity recognition. The language ID used for multi-language or
-language-neutral models is `xx`. The language class, a generic subclass
-containing only the base language data, can be found in
+spaCy also supports pipelines trained on more than one language. This is
+especially useful for named entity recognition. The language ID used for
+multi-language or language-neutral pipelines is `xx`. The language class, a
+generic subclass containing only the base language data, can be found in
 [`lang/xx`](https://github.com/explosion/spaCy/tree/master/spacy/lang/xx).
 
-To train a model using the neutral multi-language class, you can set
+To train a pipeline using the neutral multi-language class, you can set
 `lang = "xx"` in your [training config](/usage/training#config). You can also
 import the `MultiLanguage` class directly, or call
 [`spacy.blank("xx")`](/api/top-level#spacy.blank) for lazy-loading.
@@ -111,7 +110,7 @@ The Chinese language class supports three word segmentation options:
 3. **PKUSeg**: As of spaCy v2.3.0, support for
    [PKUSeg](https://github.com/lancopku/PKUSeg-python) has been added to support
    better segmentation for Chinese OntoNotes and the provided
-   [Chinese models](/models/zh). Enable PKUSeg with the tokenizer option
+   [Chinese pipelines](/models/zh). Enable PKUSeg with the tokenizer option
    `{"segmenter": "pkuseg"}`.
 
 <Infobox variant="warning">
@@ -169,9 +168,9 @@ nlp.tokenizer.pkuseg_update_user_dict([], reset=True)
 
 </Accordion>
 
-<Accordion title="Details on pretrained and custom Chinese models" spaced>
+<Accordion title="Details on trained and custom Chinese pipelines" spaced>
 
-The [Chinese models](/models/zh) provided by spaCy include a custom `pkuseg`
+The [Chinese pipelines](/models/zh) provided by spaCy include a custom `pkuseg`
 model trained only on
 [Chinese OntoNotes 5.0](https://catalog.ldc.upenn.edu/LDC2013T19), since the
 models provided by `pkuseg` include data restricted to research use. For
@@ -208,29 +207,29 @@ nlp = Chinese(meta={"tokenizer": {"config": {"pkuseg_model": "/path/to/pkuseg_mo
 The Japanese language class uses
 [SudachiPy](https://github.com/WorksApplications/SudachiPy) for word
 segmentation and part-of-speech tagging. The default Japanese language class and
-the provided Japanese models use SudachiPy split mode `A`. The `meta` argument
-of the `Japanese` language class can be used to configure the split mode to `A`,
-`B` or `C`.
+the provided Japanese pipelines use SudachiPy split mode `A`. The `meta`
+argument of the `Japanese` language class can be used to configure the split
+mode to `A`, `B` or `C`.
 
 <Infobox variant="warning">
 
 If you run into errors related to `sudachipy`, which is currently under active
 development, we suggest downgrading to `sudachipy==0.4.5`, which is the version
-used for training the current [Japanese models](/models/ja).
+used for training the current [Japanese pipelines](/models/ja).
 
 </Infobox>
 
-## Installing and using models {#download}
+## Installing and using trained pipelines {#download}
 
-The easiest way to download a model is via spaCy's
+The easiest way to download a trained pipeline is via spaCy's
 [`download`](/api/cli#download) command. It takes care of finding the
-best-matching model compatible with your spaCy installation.
+best-matching package compatible with your spaCy installation.
 
 > #### Important note for v3.0
 >
-> Note that as of spaCy v3.0, model shortcut links that create (potentially
+> Note that as of spaCy v3.0, shortcut links like `en` that create (potentially
 > brittle) symlinks in your spaCy installation are **deprecated**. To download
-> and load an installed model, use its full name:
+> and load an installed pipeline package, use its full name:
 >
 > ```diff
 > - python -m spacy download en
@@ -243,14 +242,14 @@ best-matching model compatible with your spaCy installation.
 > ```
 
 ```cli
-# Download best-matching version of a model for your spaCy installation
+# Download best-matching version of a package for your spaCy installation
 $ python -m spacy download en_core_web_sm
 
-# Download exact model version
+# Download exact package version
 $ python -m spacy download en_core_web_sm-3.0.0 --direct
 ```
 
-The download command will [install the model](/usage/models#download-pip) via
+The download command will [install the package](/usage/models#download-pip) via
 pip and place the package in your `site-packages` directory.
 
 ```cli
@@ -266,11 +265,11 @@ doc = nlp("This is a sentence.")
 
 ### Installation via pip {#download-pip}
 
-To download a model directly using [pip](https://pypi.python.org/pypi/pip),
-point `pip install` to the URL or local path of the archive file. To find the
-direct link to a model, head over to the
-[model releases](https://github.com/explosion/spacy-models/releases), right
-click on the archive link and copy it to your clipboard.
+To download a trained pipeline directly using
+[pip](https://pypi.python.org/pypi/pip), point `pip install` to the URL or local
+path of the archive file. To find the direct link to a package, head over to the
+[releases](https://github.com/explosion/spacy-models/releases), right click on
+the archive link and copy it to your clipboard.
 
 ```bash
 # With external URL
@@ -280,60 +279,61 @@ $ pip install https://github.com/explosion/spacy-models/releases/download/en_cor
 $ pip install /Users/you/en_core_web_sm-3.0.0.tar.gz
 ```
 
-By default, this will install the model into your `site-packages` directory. You
-can then use `spacy.load()` to load it via its package name or
+By default, this will install the pipeline package into your `site-packages`
+directory. You can then use `spacy.load` to load it via its package name or
 [import it](#usage-import) explicitly as a module. If you need to download
-models as part of an automated process, we recommend using pip with a direct
-link, instead of relying on spaCy's [`download`](/api/cli#download) command.
+pipeline packages as part of an automated process, we recommend using pip with a
+direct link, instead of relying on spaCy's [`download`](/api/cli#download)
+command.
 
 You can also add the direct download link to your application's
 `requirements.txt`. For more details, see the section on
-[working with models in production](#production).
+[working with pipeline packages in production](#production).
 
 ### Manual download and installation {#download-manual}
 
 In some cases, you might prefer downloading the data manually, for example to
-place it into a custom directory. You can download the model via your browser
+place it into a custom directory. You can download the package via your browser
 from the [latest releases](https://github.com/explosion/spacy-models/releases),
 or configure your own download script using the URL of the archive file. The
-archive consists of a model directory that contains another directory with the
-model data.
+archive consists of a package directory that contains another directory with the
+pipeline data.
 
 ```yaml
 ### Directory structure {highlight="6"}
 └── en_core_web_md-3.0.0.tar.gz       # downloaded archive
     ├── setup.py                      # setup file for pip installation
-    ├── meta.json                     # copy of model meta
-    └── en_core_web_md                # 📦 model package
+    ├── meta.json                     # copy of pipeline meta
+    └── en_core_web_md                # 📦 pipeline package
         ├── __init__.py               # init for pip installation
-        └── en_core_web_md-3.0.0      # model data
-            ├── config.cfg            # model config
-            ├── meta.json             # model meta
+        └── en_core_web_md-3.0.0      # pipeline data
+            ├── config.cfg            # pipeline config
+            ├── meta.json             # pipeline meta
             └── ...                   # directories with component data
 ```
 
-You can place the **model package directory** anywhere on your local file
+You can place the **pipeline package directory** anywhere on your local file
 system.
 
-### Using models with spaCy {#usage}
+### Using trained pipelines with spaCy {#usage}
 
-To load a model, use [`spacy.load`](/api/top-level#spacy.load) with the model's
-package name or a path to the data directory:
+To load a pipeline package, use [`spacy.load`](/api/top-level#spacy.load) with
+the package name or a path to the data directory:
 
 > #### Important note for v3.0
 >
-> Note that as of spaCy v3.0, model shortcut links that create (potentially
-> brittle) symlinks in your spaCy installation are **deprecated**. To load an
-> installed model, use its full name:
+> Note that as of spaCy v3.0, shortcut links like `en` that create (potentially
+> brittle) symlinks in your spaCy installation are **deprecated**. To download
+> and load an installed pipeline package, use its full name:
 >
 > ```diff
-> - nlp = spacy.load("en")
-> + nlp = spacy.load("en_core_web_sm")
+> - python -m spacy download en
+> + python -m spacy dowmload en_core_web_sm
 > ```
 
 ```python
 import spacy
-nlp = spacy.load("en_core_web_sm")           # load model package "en_core_web_sm"
+nlp = spacy.load("en_core_web_sm")           # load package "en_core_web_sm"
 nlp = spacy.load("/path/to/en_core_web_sm")  # load package from a directory
 
 doc = nlp("This is a sentence.")
@@ -342,17 +342,18 @@ doc = nlp("This is a sentence.")
 <Infobox title="Tip: Preview model info" emoji="💡">
 
 You can use the [`info`](/api/cli#info) command or
-[`spacy.info()`](/api/top-level#spacy.info) method to print a model's meta data
-before loading it. Each `Language` object with a loaded model also exposes the
-model's meta data as the attribute `meta`. For example, `nlp.meta['version']`
-will return the model's version.
+[`spacy.info()`](/api/top-level#spacy.info) method to print a pipeline
+packages's meta data before loading it. Each `Language` object with a loaded
+pipeline also exposes the pipeline's meta data as the attribute `meta`. For
+example, `nlp.meta['version']` will return the package version.
 
 </Infobox>
 
-### Importing models as modules {#usage-import}
+### Importing pipeline packages as modules {#usage-import}
 
-If you've installed a model via spaCy's downloader, or directly via pip, you can
-also `import` it and then call its `load()` method with no arguments:
+If you've installed a trained pipeline via [`spacy download`](/api/cli#download)
+or directly via pip, you can also `import` it and then call its `load()` method
+with no arguments:
 
 ```python
 ### {executable="true"}
@@ -362,51 +363,38 @@ nlp = en_core_web_sm.load()
 doc = nlp("This is a sentence.")
 ```
 
-How you choose to load your models ultimately depends on personal preference.
-However, **for larger code bases**, we usually recommend native imports, as this
-will make it easier to integrate models with your existing build process,
-continuous integration workflow and testing framework. It'll also prevent you
-from ever trying to load a model that is not installed, as your code will raise
-an `ImportError` immediately, instead of failing somewhere down the line when
-calling `spacy.load()`.
+How you choose to load your trained pipelines ultimately depends on personal
+preference. However, **for larger code bases**, we usually recommend native
+imports, as this will make it easier to integrate pipeline packages with your
+existing build process, continuous integration workflow and testing framework.
+It'll also prevent you from ever trying to load a package that is not installed,
+as your code will raise an `ImportError` immediately, instead of failing
+somewhere down the line when calling `spacy.load()`. For more details, see the
+section on [working with pipeline packages in production](#production).
 
-For more details, see the section on
-[working with models in production](#production).
+## Using trained pipelines in production {#production}
 
-### Using your own models {#own-models}
-
-If you've trained your own model, for example for
-[additional languages](/usage/adding-languages) or
-[custom named entities](/usage/training#ner), you can save its state using the
-[`Language.to_disk()`](/api/language#to_disk) method. To make the model more
-convenient to deploy, we recommend wrapping it as a Python package.
-
-For more information and a detailed guide on how to package your model, see the
-documentation on [saving and loading models](/usage/saving-loading#models).
-
-## Using models in production {#production}
-
-If your application depends on one or more models, you'll usually want to
-integrate them into your continuous integration workflow and build process.
-While spaCy provides a range of useful helpers for downloading, linking and
-loading models, the underlying functionality is entirely based on native Python
-packages. This allows your application to handle a model like any other package
-dependency.
+If your application depends on one or more trained pipeline packages, you'll
+usually want to integrate them into your continuous integration workflow and
+build process. While spaCy provides a range of useful helpers for downloading
+and loading pipeline packages, the underlying functionality is entirely based on
+native Python packaging. This allows your application to handle a spaCy pipeline
+like any other package dependency.
 
 <!-- TODO: reference relevant spaCy project -->
 
-### Downloading and requiring model dependencies {#models-download}
+### Downloading and requiring package dependencies {#models-download}
 
 spaCy's built-in [`download`](/api/cli#download) command is mostly intended as a
 convenient, interactive wrapper. It performs compatibility checks and prints
-detailed error messages and warnings. However, if you're downloading models as
-part of an automated build process, this only adds an unnecessary layer of
-complexity. If you know which models your application needs, you should be
-specifying them directly.
+detailed error messages and warnings. However, if you're downloading pipeline
+packages as part of an automated build process, this only adds an unnecessary
+layer of complexity. If you know which packages your application needs, you
+should be specifying them directly.
 
-Because all models are valid Python packages, you can add them to your
+Because pipeline packages are valid Python packages, you can add them to your
 application's `requirements.txt`. If you're running your own internal PyPi
-installation, you can upload the models there. pip's
+installation, you can upload the pipeline packages there. pip's
 [requirements file format](https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format)
 supports both package names to download via a PyPi server, as well as direct
 URLs.
@@ -422,17 +410,17 @@ the download URL. This way, the package won't be re-downloaded and overwritten
 if it's already installed - just like when you're downloading a package from
 PyPi.
 
-All models are versioned and specify their spaCy dependency. This ensures
-cross-compatibility and lets you specify exact version requirements for each
-model. If you've trained your own model, you can use the
-[`package`](/api/cli#package) command to generate the required meta data and
-turn it into a loadable package.
+All pipeline packages are versioned and specify their spaCy dependency. This
+ensures cross-compatibility and lets you specify exact version requirements for
+each pipeline. If you've [trained](/usage/training) your own pipeline, you can
+use the [`spacy package`](/api/cli#package) command to generate the required
+meta data and turn it into a loadable package.
 
-### Loading and testing models {#models-loading}
+### Loading and testing pipeline packages {#models-loading}
 
-Models are regular Python packages, so you can also import them as a package
-using Python's native `import` syntax, and then call the `load` method to load
-the model data and return an `nlp` object:
+Pipeline packages are regular Python packages, so you can also import them as a
+package using Python's native `import` syntax, and then call the `load` method
+to load the data and return an `nlp` object:
 
 ```python
 import en_core_web_sm
@@ -440,16 +428,17 @@ nlp = en_core_web_sm.load()
 ```
 
 In general, this approach is recommended for larger code bases, as it's more
-"native", and doesn't depend on symlinks or rely on spaCy's loader to resolve
-string names to model packages. If a model can't be imported, Python will raise
-an `ImportError` immediately. And if a model is imported but not used, any
-linter will catch that.
+"native", and doesn't rely on spaCy's loader to resolve string names to
+packages. If a package can't be imported, Python will raise an `ImportError`
+immediately. And if a package is imported but not used, any linter will catch
+that.
 
 Similarly, it'll give you more flexibility when writing tests that require
-loading models. For example, instead of writing your own `try` and `except`
+loading pipelines. For example, instead of writing your own `try` and `except`
 logic around spaCy's loader, you can use
 [pytest](http://pytest.readthedocs.io/en/latest/)'s
 [`importorskip()`](https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip)
-method to only run a test if a specific model or model version is installed.
-Each model package exposes a `__version__` attribute which you can also use to
-perform your own version compatibility checks before loading a model.
+method to only run a test if a specific pipeline package or version is
+installed. Each pipeline package package exposes a `__version__` attribute which
+you can also use to perform your own version compatibility checks before loading
+it.
diff --git a/website/docs/usage/processing-pipelines.md b/website/docs/usage/processing-pipelines.md
index 3636aa3c2..2885d9f50 100644
--- a/website/docs/usage/processing-pipelines.md
+++ b/website/docs/usage/processing-pipelines.md
@@ -42,8 +42,8 @@ texts = ["This is a text", "These are lots of texts", "..."]
 - Only apply the **pipeline components you need**. Getting predictions from the
   model that you don't actually need adds up and becomes very inefficient at
   scale. To prevent this, use the `disable` keyword argument to disable
-  components you don't need – either when loading a model, or during processing
-  with `nlp.pipe`. See the section on
+  components you don't need – either when loading a pipeline, or during
+  processing with `nlp.pipe`. See the section on
   [disabling pipeline components](#disabling) for more details and examples.
 
 </Infobox>
@@ -95,7 +95,7 @@ spaCy makes it very easy to create your own pipelines consisting of reusable
 components – this includes spaCy's default tagger, parser and entity recognizer,
 but also your own custom processing functions. A pipeline component can be added
 to an already existing `nlp` object, specified when initializing a `Language`
-class, or defined within a [model package](/usage/saving-loading#models).
+class, or defined within a [pipeline package](/usage/saving-loading#models).
 
 > #### config.cfg (excerpt)
 >
@@ -115,7 +115,7 @@ class, or defined within a [model package](/usage/saving-loading#models).
 > # Settings for the parser component
 > ```
 
-When you load a model, spaCy first consults the model's
+When you load a pipeline, spaCy first consults the
 [`meta.json`](/usage/saving-loading#models) and
 [`config.cfg`](/usage/training#config). The config tells spaCy what language
 class to use, which components are in the pipeline, and how those components
@@ -131,8 +131,7 @@ should be created. spaCy will then do the following:
    component with with [`add_pipe`](/api/language#add_pipe). The settings are
    passed into the factory.
 3. Make the **model data** available to the `Language` class by calling
-   [`from_disk`](/api/language#from_disk) with the path to the model data
-   directory.
+   [`from_disk`](/api/language#from_disk) with the path to the data directory.
 
 So when you call this...
 
@@ -140,27 +139,27 @@ So when you call this...
 nlp = spacy.load("en_core_web_sm")
 ```
 
-... the model's `config.cfg` tells spaCy to use the language `"en"` and the
+... the pipeline's `config.cfg` tells spaCy to use the language `"en"` and the
 pipeline `["tagger", "parser", "ner"]`. spaCy will then initialize
 `spacy.lang.en.English`, and create each pipeline component and add it to the
-processing pipeline. It'll then load in the model's data from its data directory
+processing pipeline. It'll then load in the model data from the data directory
 and return the modified `Language` class for you to use as the `nlp` object.
 
 <Infobox title="Changed in v3.0" variant="warning">
 
 spaCy v3.0 introduces a `config.cfg`, which includes more detailed settings for
-the model pipeline, its components and the
-[training process](/usage/training#config). You can export the config of your
-current `nlp` object by calling [`nlp.config.to_disk`](/api/language#config).
+the pipeline, its components and the [training process](/usage/training#config).
+You can export the config of your current `nlp` object by calling
+[`nlp.config.to_disk`](/api/language#config).
 
 </Infobox>
 
-Fundamentally, a [spaCy model](/models) consists of three components: **the
-weights**, i.e. binary data loaded in from a directory, a **pipeline** of
+Fundamentally, a [spaCy pipeline package](/models) consists of three components:
+**the weights**, i.e. binary data loaded in from a directory, a **pipeline** of
 functions called in order, and **language data** like the tokenization rules and
-language-specific settings. For example, a Spanish NER model requires different
-weights, language data and pipeline components than an English parsing and
-tagging model. This is also why the pipeline state is always held by the
+language-specific settings. For example, a Spanish NER pipeline requires
+different weights, language data and components than an English parsing and
+tagging pipeline. This is also why the pipeline state is always held by the
 `Language` class. [`spacy.load`](/api/top-level#spacy.load) puts this all
 together and returns an instance of `Language` with a pipeline set and access to
 the binary data:
@@ -175,7 +174,7 @@ cls = spacy.util.get_lang_class(lang)  # 1. Get Language class, e.g. English
 nlp = cls()                            # 2. Initialize it
 for name in pipeline:
     nlp.add_pipe(name)                 # 3. Add the component to the pipeline
-nlp.from_disk(model_data_path)         # 4. Load in the binary data
+nlp.from_disk(data_path)               # 4. Load in the binary data
 ```
 
 When you call `nlp` on a text, spaCy will **tokenize** it and then **call each
@@ -243,28 +242,29 @@ tagger or the parser, you can **disable or exclude** it. This can sometimes make
 a big difference and improve loading and inference speed. There are two
 different mechanisms you can use:
 
-1. **Disable:** The component and its data will be loaded with the model, but it
-   will be disabled by default and not run as part of the processing pipeline.
-   To run it, you can explicitly enable it by calling
+1. **Disable:** The component and its data will be loaded with the pipeline, but
+   it will be disabled by default and not run as part of the processing
+   pipeline. To run it, you can explicitly enable it by calling
    [`nlp.enable_pipe`](/api/language#enable_pipe). When you save out the `nlp`
    object, the disabled component will be included but disabled by default.
-2. **Exclude:** Don't load the component and its data with the model. Once the
-   model is loaded, there will be no reference to the excluded component.
+2. **Exclude:** Don't load the component and its data with the pipeline. Once
+   the pipeline is loaded, there will be no reference to the excluded component.
 
 Disabled and excluded component names can be provided to
 [`spacy.load`](/api/top-level#spacy.load) as a list.
 
 <!-- TODO: update with info on our models shipped with optional components -->
 
-> #### 💡 Models with optional components
+> #### 💡 Optional pipeline components
 >
-> The `disable` mechanism makes it easy to distribute models with optional
-> components that you can enable or disable at runtime. For instance, your model
-> may include a statistical _and_ a rule-based component for sentence
-> segmentation, and you can choose which one to run depending on your use case.
+> The `disable` mechanism makes it easy to distribute pipeline packages with
+> optional components that you can enable or disable at runtime. For instance,
+> your pipeline may include a statistical _and_ a rule-based component for
+> sentence segmentation, and you can choose which one to run depending on your
+> use case.
 
 ```python
-# Load the model without the entity recognizer
+# Load the pipeline without the entity recognizer
 nlp = spacy.load("en_core_web_sm", exclude=["ner"])
 
 # Load the tagger and parser but don't enable them
@@ -358,25 +358,25 @@ run as part of the pipeline.
 | `nlp.component_names` | All component names, including disabled components.              |
 | `nlp.disabled`        | Names of components that are currently disabled.                 |
 
-### Sourcing pipeline components from existing models {#sourced-components new="3"}
+### Sourcing components from existing pipelines {#sourced-components new="3"}
 
-Pipeline components that are independent can also be reused across models.
-Instead of adding a new blank component to a pipeline, you can also copy an
-existing component from a pretrained model by setting the `source` argument on
+Pipeline components that are independent can also be reused across pipelines.
+Instead of adding a new blank component, you can also copy an existing component
+from a trained pipeline by setting the `source` argument on
 [`nlp.add_pipe`](/api/language#add_pipe). The first argument will then be
 interpreted as the name of the component in the source pipeline – for instance,
 `"ner"`. This is especially useful for
-[training a model](/usage/training#config-components) because it lets you mix
-and match components and create fully custom model packages with updated
-pretrained components and new components trained on your data.
+[training a pipeline](/usage/training#config-components) because it lets you mix
+and match components and create fully custom pipeline packages with updated
+trained components and new components trained on your data.
 
-<Infobox variant="warning" title="Important note for pretrained components">
+<Infobox variant="warning" title="Important note for trained components">
 
-When reusing components across models, keep in mind that the **vocabulary**,
-**vectors** and model settings **must match**. If a pretrained model includes
+When reusing components across pipelines, keep in mind that the **vocabulary**,
+**vectors** and model settings **must match**. If a trained pipeline includes
 [word vectors](/usage/linguistic-features#vectors-similarity) and the component
-uses them as features, the model you copy it to needs to have the _same_ vectors
-available – otherwise, it won't be able to make the same predictions.
+uses them as features, the pipeline you copy it to needs to have the _same_
+vectors available – otherwise, it won't be able to make the same predictions.
 
 </Infobox>
 
@@ -384,7 +384,7 @@ available – otherwise, it won't be able to make the same predictions.
 >
 > Instead of providing a `factory`, component blocks in the training
 > [config](/usage/training#config) can also define a `source`. The string needs
-> to be a loadable spaCy model package or path. The
+> to be a loadable spaCy pipeline package or path. The
 >
 > ```ini
 > [components.ner]
@@ -404,11 +404,11 @@ available – otherwise, it won't be able to make the same predictions.
 ### {executable="true"}
 import spacy
 
-# The source model with different components
+# The source pipeline with different components
 source_nlp = spacy.load("en_core_web_sm")
 print(source_nlp.pipe_names)
 
-# Add only the entity recognizer to the new blank model
+# Add only the entity recognizer to the new blank pipeline
 nlp = spacy.blank("en")
 nlp.add_pipe("ner", source=source_nlp)
 print(nlp.pipe_names)
@@ -535,8 +535,8 @@ only being able to modify it afterwards.
 The [`@Language.component`](/api/language#component) decorator lets you turn a
 simple function into a pipeline component. It takes at least one argument, the
 **name** of the component factory. You can use this name to add an instance of
-your component to the pipeline. It can also be listed in your model config, so
-you can save, load and train models using your component.
+your component to the pipeline. It can also be listed in your pipeline config,
+so you can save, load and train pipelines using your component.
 
 Custom components can be added to the pipeline using the
 [`add_pipe`](/api/language#add_pipe) method. Optionally, you can either specify
@@ -838,16 +838,24 @@ If what you're passing in isn't JSON-serializable – e.g. a custom object like
 [model](#trainable-components) – saving out the component config becomes
 impossible because there's no way for spaCy to know _how_ that object was
 created, and what to do to create it again. This makes it much harder to save,
-load and train custom models with custom components. A simple solution is to
+load and train custom pipelines with custom components. A simple solution is to
 **register a function** that returns your resources. The
 [registry](/api/top-level#registry) lets you **map string names to functions**
 that create objects, so given a name and optional arguments, spaCy will know how
-to recreate the object. To register a function that returns a custom asset, you
-can use the `@spacy.registry.assets` decorator with a single argument, the name:
+to recreate the object. To register a function that returns your custom
+dictionary, you can use the `@spacy.registry.misc` decorator with a single
+argument, the name:
+
+> #### What's the misc registry?
+>
+> The [`registry`](/api/top-level#registry) provides different categories for
+> different types of functions – for example, model architectures, tokenizers or
+> batchers. `misc` is intended for miscellaneous functions that don't fit
+> anywhere else.
 
 ```python
 ### Registered function for assets {highlight="1"}
-@spacy.registry.assets("acronyms.slang_dict.v1")
+@spacy.registry.misc("acronyms.slang_dict.v1")
 def create_acronyms_slang_dict():
     dictionary = {"lol": "laughing out loud", "brb": "be right back"}
     dictionary.update({value: key for key, value in dictionary.items()})
@@ -856,9 +864,9 @@ def create_acronyms_slang_dict():
 
 In your `default_config` (and later in your
 [training config](/usage/training#config)), you can now refer to the function
-registered under the name `"acronyms.slang_dict.v1"` using the `@assets` key.
-This tells spaCy how to create the value, and when your component is created,
-the result of the registered function is passed in as the key `"dictionary"`.
+registered under the name `"acronyms.slang_dict.v1"` using the `@misc` key. This
+tells spaCy how to create the value, and when your component is created, the
+result of the registered function is passed in as the key `"dictionary"`.
 
 > #### config.cfg
 >
@@ -867,21 +875,21 @@ the result of the registered function is passed in as the key `"dictionary"`.
 > factory = "acronyms"
 >
 > [components.acronyms.dictionary]
-> @assets = "acronyms.slang_dict.v1"
+> @misc = "acronyms.slang_dict.v1"
 > ```
 
 ```diff
 - default_config = {"dictionary:" DICTIONARY}
-+ default_config = {"dictionary": {"@assets": "acronyms.slang_dict.v1"}}
++ default_config = {"dictionary": {"@misc": "acronyms.slang_dict.v1"}}
 ```
 
 Using a registered function also means that you can easily include your custom
-components in models that you [train](/usage/training). To make sure spaCy knows
-where to find your custom `@assets` function, you can pass in a Python file via
-the argument `--code`. If someone else is using your component, all they have to
-do to customize the data is to register their own function and swap out the
-name. Registered functions can also take **arguments** by the way that can be
-defined in the config as well – you can read more about this in the docs on
+components in pipelines that you [train](/usage/training). To make sure spaCy
+knows where to find your custom `@misc` function, you can pass in a Python file
+via the argument `--code`. If someone else is using your component, all they
+have to do to customize the data is to register their own function and swap out
+the name. Registered functions can also take **arguments** by the way that can
+be defined in the config as well – you can read more about this in the docs on
 [training with custom code](/usage/training#custom-code).
 
 ### Python type hints and pydantic validation {#type-hints new="3"}
@@ -1121,7 +1129,14 @@ loss is calculated and to add evaluation scores to the training output.
 | [`get_loss`](/api/pipe#get_loss)             | Return a tuple of the loss and the gradient for a batch of [`Example`](/api/example) objects.                                                                                                                                                                                                                      |
 | [`score`](/api/pipe#score)                   | Score a batch of [`Example`](/api/example) objects and return a dictionary of scores. The [`@Language.factory`](/api/language#factory) decorator can define the `default_socre_weights` of the component to decide which keys of the scores to display during training and how they count towards the final score. |
 
-<!-- TODO: link to (not yet created) page for defining models for trainable components -->
+<Infobox title="Custom trainable components and models" emoji="📖">
+
+For more details on how to implement your own trainable components and model
+architectures, and plug existing models implemented in PyTorch or TensorFlow
+into your spaCy pipeline, see the usage guide on
+[layers and model architectures](/usage/layers-architectures#components).
+
+</Infobox>
 
 ## Extension attributes {#custom-components-attributes new="2"}
 
@@ -1322,9 +1337,9 @@ While it's generally recommended to use the `Doc._`, `Span._` and `Token._`
 proxies to add your own custom attributes, spaCy offers a few exceptions to
 allow **customizing the built-in methods** like
 [`Doc.similarity`](/api/doc#similarity) or [`Doc.vector`](/api/doc#vector) with
-your own hooks, which can rely on statistical models you train yourself. For
-instance, you can provide your own on-the-fly sentence segmentation algorithm or
-document similarity method.
+your own hooks, which can rely on components you train yourself. For instance,
+you can provide your own on-the-fly sentence segmentation algorithm or document
+similarity method.
 
 Hooks let you customize some of the behaviors of the `Doc`, `Span` or `Token`
 objects by adding a component to the pipeline. For instance, to customize the
@@ -1456,13 +1471,13 @@ function that takes a `Doc`, modifies it and returns it.
   method. However, a third-party extension should **never silently overwrite
   built-ins**, or attributes set by other extensions.
 
-- If you're looking to publish a model that depends on a custom pipeline
-  component, you can either **require it** in the model package's dependencies,
-  or – if the component is specific and lightweight – choose to **ship it with
-  your model package**. Just make sure the
+- If you're looking to publish a pipeline package that depends on a custom
+  pipeline component, you can either **require it** in the package's
+  dependencies, or – if the component is specific and lightweight – choose to
+  **ship it with your pipeline package**. Just make sure the
   [`@Language.component`](/api/language#component) or
   [`@Language.factory`](/api/language#factory) decorator that registers the
-  custom component runs in your model's `__init__.py` or is exposed via an
+  custom component runs in your package's `__init__.py` or is exposed via an
   [entry point](/usage/saving-loading#entry-points).
 
 - Once you're ready to share your extension with others, make sure to **add docs
@@ -1511,9 +1526,9 @@ def custom_ner_wrapper(doc):
     return doc
 ```
 
-The `custom_ner_wrapper` can then be added to the pipeline of a blank model
-using [`nlp.add_pipe`](/api/language#add_pipe). You can also replace the
-existing entity recognizer of a pretrained model with
+The `custom_ner_wrapper` can then be added to a blank pipeline using
+[`nlp.add_pipe`](/api/language#add_pipe). You can also replace the existing
+entity recognizer of a trained pipeline with
 [`nlp.replace_pipe`](/api/language#replace_pipe).
 
 Here's another example of a custom model, `your_custom_model`, that takes a list
diff --git a/website/docs/usage/projects.md b/website/docs/usage/projects.md
index 620526280..b6688cd5d 100644
--- a/website/docs/usage/projects.md
+++ b/website/docs/usage/projects.md
@@ -20,17 +20,13 @@ menu:
 
 spaCy projects let you manage and share **end-to-end spaCy workflows** for
 different **use cases and domains**, and orchestrate training, packaging and
-serving your custom models. You can start off by cloning a pre-defined project
-template, adjust it to fit your needs, load in your data, train a model, export
-it as a Python package, upload your outputs to a remote storage and share your
-results with your team. spaCy projects can be used via the new
+serving your custom pipelines. You can start off by cloning a pre-defined
+project template, adjust it to fit your needs, load in your data, train a
+pipeline, export it as a Python package, upload your outputs to a remote storage
+and share your results with your team. spaCy projects can be used via the new
 [`spacy project`](/api/cli#project) command and we provide templates in our
 [`projects`](https://github.com/explosion/projects) repo.
 
-<!-- TODO: mention integrations -->
-
-<!-- TODO: decide how to introduce concept -->
-
 ![Illustration of project workflow and commands](../images/projects.svg)
 
 <!-- TODO:
@@ -51,7 +47,7 @@ production.
 <Grid narrow cols={3}>
 <Integration title="DVC" logo="dvc" url="#dvc">Manage and version your data</Integration>
 <Integration title="Prodigy" logo="prodigy" url="#prodigy">Create labelled training data</Integration>
-<Integration title="Streamlit" logo="streamlit" url="#streamlit">Visualize and demo your models</Integration>
+<Integration title="Streamlit" logo="streamlit" url="#streamlit">Visualize and demo your pipelines</Integration>
 <Integration title="FastAPI" logo="fastapi" url="#fastapi">Serve your models and host APIs</Integration>
 <Integration title="Ray" logo="ray" url="#ray">Distributed and parallel training</Integration>
 <Integration title="Weights &amp; Biases" logo="wandb" url="#wandb">Track your experiments and results</Integration>
@@ -66,8 +62,8 @@ production.
 
 The [`spacy project clone`](/api/cli#project-clone) command clones an existing
 project template and copies the files to a local directory. You can then run the
-project, e.g. to train a model and edit the commands and scripts to build fully
-custom workflows.
+project, e.g. to train a pipeline and edit the commands and scripts to build
+fully custom workflows.
 
 ```cli
 python -m spacy project clone some_example_project
@@ -162,12 +158,12 @@ script).
 > ```
 
 Workflows are series of commands that are run in order and often depend on each
-other. For instance, to generate a packaged model, you might start by converting
-your data, then run [`spacy train`](/api/cli#train) to train your model on the
-converted data and if that's successful, run [`spacy package`](/api/cli#package)
-to turn the best model artifact into an installable Python package. The
-following command runs the workflow named `all` defined in the `project.yml`,
-and executes the commands it specifies, in order:
+other. For instance, to generate a pipeline package, you might start by
+converting your data, then run [`spacy train`](/api/cli#train) to train your
+pipeline on the converted data and if that's successful, run
+[`spacy package`](/api/cli#package) to turn the best trained artifact into an
+installable Python package. The following command runs the workflow named `all`
+defined in the `project.yml`, and executes the commands it specifies, in order:
 
 ```cli
 $ python -m spacy project run all
@@ -191,11 +187,11 @@ project as a DVC repo.
 >   local: '/mnt/scratch/cache'
 > ```
 
-After training a model, you can optionally use the
+After training a pipeline, you can optionally use the
 [`spacy project push`](/api/cli#project-push) command to upload your outputs to
 a remote storage, using protocols like [S3](https://aws.amazon.com/s3/),
 [Google Cloud Storage](https://cloud.google.com/storage) or SSH. This can help
-you **export** your model packages, **share** work with your team, or **cache
+you **export** your pipeline packages, **share** work with your team, or **cache
 results** to avoid repeating work.
 
 ```cli
@@ -214,8 +210,8 @@ docs on [remote storage](#remote).
 The `project.yml` defines the assets a project depends on, like datasets and
 pretrained weights, as well as a series of commands that can be run separately
 or as a workflow – for instance, to preprocess the data, convert it to spaCy's
-format, train a model, evaluate it and export metrics, package it and spin up a
-quick web demo. It looks pretty similar to a config file used to define CI
+format, train a pipeline, evaluate it and export metrics, package it and spin up
+a quick web demo. It looks pretty similar to a config file used to define CI
 pipelines.
 
 <!-- TODO: update with better (final) example -->
@@ -324,17 +320,17 @@ others are running your project with the same data.
 
 Each command defined in the `project.yml` can optionally define a list of
 dependencies and outputs. These are the files the command requires and creates.
-For example, a command for training a model may depend on a
+For example, a command for training a pipeline may depend on a
 [`config.cfg`](/usage/training#config) and the training and evaluation data, and
-it will export a directory `model-best`, containing the best model, which you
-can then re-use in other commands.
+it will export a directory `model-best`, which you can then re-use in other
+commands.
 
 <!-- prettier-ignore -->
 ```yaml
 ### project.yml
 commands:
   - name: train
-    help: 'Train a spaCy model using the specified corpus and config'
+    help: 'Train a spaCy pipeline using the specified corpus and config'
     script:
       - 'python -m spacy train ./configs/config.cfg -o training/ --paths.train ./corpus/training.spacy --paths.dev ./corpus/evaluation.spacy'
     deps:
@@ -392,14 +388,14 @@ directory:
 ├── project.yml          # the project settings
 ├── project.lock         # lockfile that tracks inputs/outputs
 ├── assets/              # downloaded data assets
-├── configs/             # model config.cfg files used for training
+├── configs/             # pipeline config.cfg files used for training
 ├── corpus/              # output directory for training corpus
-├── metas/               # model meta.json templates used for packaging
+├── metas/               # pipeline meta.json templates used for packaging
 ├── metrics/             # output directory for evaluation metrics
 ├── notebooks/           # directory for Jupyter notebooks
-├── packages/            # output directory for model Python packages
+├── packages/            # output directory for pipeline Python packages
 ├── scripts/             # directory for scripts, e.g. referenced in commands
-├── training/            # output directory for trained models
+├── training/            # output directory for trained pipelines
 └── ...                  # any other files, like a requirements.txt etc.
 ```
 
@@ -426,7 +422,7 @@ report:
 ### project.yml
 commands:
   - name: test
-    help: 'Test the trained model'
+    help: 'Test the trained pipeline'
     script:
       - 'pip install pytest pytest-html'
       - 'python -m pytest ./scripts/tests --html=metrics/test-report.html'
@@ -440,8 +436,8 @@ commands:
 Adding `training/model-best` to the command's `deps` lets you ensure that the
 file is available. If not, spaCy will show an error and the command won't run.
 Setting `no_skip: true` means that the command will always run, even if the
-dependencies (the trained model) hasn't changed. This makes sense here, because
-you typically don't want to skip your tests.
+dependencies (the trained pipeline) haven't changed. This makes sense here,
+because you typically don't want to skip your tests.
 
 ### Writing custom scripts {#custom-scripts}
 
@@ -526,6 +522,15 @@ before or after it and re-running the `project document` command will **only
 update the auto-generated part**. This makes it easy to keep your documentation
 up to date.
 
+<Infobox variant="warning">
+
+Note that the contents of an existing file will be **replaced** if no existing
+auto-generated docs are found. If you want spaCy to ignore a file and not update
+it, you can add the comment marker `<!-- SPACY PROJECT: IGNORE -->` anywhere in
+your markup.
+
+</Infobox>
+
 ### Cloning from your own repo {#custom-repo}
 
 The [`spacy project clone`](/api/cli#project-clone) command lets you customize
@@ -545,7 +550,7 @@ notebooks with usage examples.
 
 <Infobox title="Important note about assets" variant="warning">
 
-It's typically not a good idea to check large data assets, trained models or
+It's typically not a good idea to check large data assets, trained pipelines or
 other artifacts into a Git repo and you should exclude them from your project
 template by adding a `.gitignore`. If you want to version your data and models,
 check out [Data Version Control](#dvc) (DVC), which integrates with spaCy
@@ -557,7 +562,7 @@ projects.
 
 You can persist your project outputs to a remote storage using the
 [`project push`](/api/cli#project-push) command. This can help you **export**
-your model packages, **share** work with your team, or **cache results** to
+your pipeline packages, **share** work with your team, or **cache results** to
 avoid repeating work. The [`project pull`](/api/cli#project-pull) command will
 download any outputs that are in the remote storage and aren't available
 locally.
@@ -613,7 +618,7 @@ For instance, let's say you had the following command in your `project.yml`:
 ```yaml
 ### project.yml
 - name: train
-  help: 'Train a spaCy model using the specified corpus and config'
+  help: 'Train a spaCy pipeline using the specified corpus and config'
   script:
     - 'spacy train ./config.cfg --output training/'
   deps:
@@ -805,8 +810,8 @@ mattis pretium.
 [Streamlit](https://streamlit.io) is a Python framework for building interactive
 data apps. The [`spacy-streamlit`](https://github.com/explosion/spacy-streamlit)
 package helps you integrate spaCy visualizations into your Streamlit apps and
-quickly spin up demos to explore your models interactively. It includes a full
-embedded visualizer, as well as individual components.
+quickly spin up demos to explore your pipelines interactively. It includes a
+full embedded visualizer, as well as individual components.
 
 ```bash
 $ pip install spacy_streamlit
@@ -820,11 +825,11 @@ $ pip install spacy_streamlit
 
 Using [`spacy-streamlit`](https://github.com/explosion/spacy-streamlit), your
 projects can easily define their own scripts that spin up an interactive
-visualizer, using the latest model you trained, or a selection of models so you
-can compare their results. The following script starts an
+visualizer, using the latest pipeline you trained, or a selection of pipelines
+so you can compare their results. The following script starts an
 [NER visualizer](/usage/visualizers#ent) and takes two positional command-line
-argument you can pass in from your `config.yml`: a comma-separated list of model
-paths and an example text to use as the default text.
+argument you can pass in from your `config.yml`: a comma-separated list of paths
+to load the pipelines from and an example text to use as the default text.
 
 ```python
 ### scripts/visualize.py
@@ -832,8 +837,8 @@ import spacy_streamlit
 import sys
 
 DEFAULT_TEXT = sys.argv[2] if len(sys.argv) >= 3 else ""
-MODELS = [name.strip() for name in sys.argv[1].split(",")]
-spacy_streamlit.visualize(MODELS, DEFAULT_TEXT, visualizers=["ner"])
+PIPELINES = [name.strip() for name in sys.argv[1].split(",")]
+spacy_streamlit.visualize(PIPELINES, DEFAULT_TEXT, visualizers=["ner"])
 ```
 
 > #### Example usage
@@ -847,7 +852,7 @@ spacy_streamlit.visualize(MODELS, DEFAULT_TEXT, visualizers=["ner"])
 ### project.yml
 commands:
   - name: visualize
-    help: "Visualize the model's output interactively using Streamlit"
+    help: "Visualize the pipeline's output interactively using Streamlit"
     script:
       - 'streamlit run ./scripts/visualize.py ./training/model-best "I like Adidas shoes."'
     deps:
@@ -870,8 +875,8 @@ mattis pretium.
 for building REST APIs with Python, based on Python
 [type hints](https://fastapi.tiangolo.com/python-types/). It's become a popular
 library for serving machine learning models and you can use it in your spaCy
-projects to quickly serve up a trained model and make it available behind a REST
-API.
+projects to quickly serve up a trained pipeline and make it available behind a
+REST API.
 
 ```python
 # TODO: show an example that addresses some of the main concerns for serving ML (workers etc.)
@@ -888,7 +893,7 @@ API.
 ### project.yml
 commands:
   - name: serve
-    help: "Serve the trained model with FastAPI"
+    help: "Serve the trained pipeline with FastAPI"
     script:
       - 'python ./scripts/serve.py ./training/model-best'
     deps:
@@ -914,4 +919,4 @@ mattis pretium.
 
 ### Weights & Biases {#wandb} <IntegrationLogo name="wandb" width={175} height="auto" align="right" />
 
-<!-- TODO: decide how we want this to work? Just send results plus config from spacy evaluate in a separate command/script? -->
+<!-- TODO: link to WandB logger, explain that it's built-in but that you can also do other cool stuff with WandB? And then include example project (still need to decide what we want to do here)  -->
diff --git a/website/docs/usage/rule-based-matching.md b/website/docs/usage/rule-based-matching.md
index 7fdce032e..fb54c9936 100644
--- a/website/docs/usage/rule-based-matching.md
+++ b/website/docs/usage/rule-based-matching.md
@@ -493,6 +493,39 @@ you prefer.
 | `i`       | Index of the current match (`matches[i`]). ~~int~~                                                                                                 |
 | `matches` | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. ~~ List[Tuple[int, int int]]~~ |
 
+### Creating spans from matches {#matcher-spans}
+
+Creating [`Span`](/api/span) objects from the returned matches is a very common
+use case. spaCy makes this easy by giving you access to the `start` and `end`
+token of each match, which you can use to construct a new span with an optional
+label. As of spaCy v3.0, you can also set `as_spans=True` when calling the
+matcher on a `Doc`, which will return a list of [`Span`](/api/span) objects
+using the `match_id` as the span label.
+
+```python
+### {executable="true"}
+import spacy
+from spacy.matcher import Matcher
+from spacy.tokens import Span
+
+nlp = spacy.blank("en")
+matcher = Matcher(nlp.vocab)
+matcher.add("PERSON", [[{"lower": "barack"}, {"lower": "obama"}]])
+doc = nlp("Barack Obama was the 44th president of the United States")
+
+# 1. Return (match_id, start, end) tuples
+matches = matcher(doc)
+for match_id, start, end in matches:
+    # Create the matched span and assign the match_id as a label
+    span = Span(doc, start, end, label=match_id)
+    print(span.text, span.label_)
+
+# 2. Return Span objects directly
+matches = matcher(doc, as_spans=True)
+for span in matches:
+    print(span.text, span.label_)
+```
+
 ### Using custom pipeline components {#matcher-pipeline}
 
 Let's say your data also contains some annoying pre-processing artifacts, like
@@ -726,7 +759,7 @@ whitespace, making them easy to match as well.
 from spacy.lang.en import English
 from spacy.matcher import Matcher
 
-nlp = English()  # We only want the tokenizer, so no need to load a model
+nlp = English()  # We only want the tokenizer, so no need to load a pipeline
 matcher = Matcher(nlp.vocab)
 
 pos_emoji = ["😀", "😃", "😂", "🤣", "😊", "😍"]  # Positive emoji
@@ -823,15 +856,6 @@ for token in doc:
     print(token.text, token._.is_hashtag)
 ```
 
-To process a stream of social media posts, we can use
-[`Language.pipe`](/api/language#pipe), which will return a stream of `Doc`
-objects that we can pass to [`Matcher.pipe`](/api/matcher#pipe).
-
-```python
-docs = nlp.pipe(LOTS_OF_TWEETS)
-matches = matcher.pipe(docs)
-```
-
 ## Efficient phrase matching {#phrasematcher}
 
 If you need to match large terminology lists, you can also use the
@@ -869,12 +893,13 @@ pattern covering the exact tokenization of the term.
 <Infobox title="Important note on creating patterns" variant="warning">
 
 To create the patterns, each phrase has to be processed with the `nlp` object.
-If you have a model loaded, doing this in a loop or list comprehension can
-easily become inefficient and slow. If you **only need the tokenization and
-lexical attributes**, you can run [`nlp.make_doc`](/api/language#make_doc)
-instead, which will only run the tokenizer. For an additional speed boost, you
-can also use the [`nlp.tokenizer.pipe`](/api/tokenizer#pipe) method, which will
-process the texts as a stream.
+If you have a trained pipeline loaded, doing this in a loop or list
+comprehension can easily become inefficient and slow. If you **only need the
+tokenization and lexical attributes**, you can run
+[`nlp.make_doc`](/api/language#make_doc) instead, which will only run the
+tokenizer. For an additional speed boost, you can also use the
+[`nlp.tokenizer.pipe`](/api/tokenizer#pipe) method, which will process the texts
+as a stream.
 
 ```diff
 - patterns = [nlp(term) for term in LOTS_OF_TERMS]
@@ -953,7 +978,7 @@ of an advantage over writing one or two token patterns.
 The [`EntityRuler`](/api/entityruler) is an exciting new component that lets you
 add named entities based on pattern dictionaries, and makes it easy to combine
 rule-based and statistical named entity recognition for even more powerful
-models.
+pipelines.
 
 ### Entity Patterns {#entityruler-patterns}
 
@@ -997,8 +1022,8 @@ doc = nlp("Apple is opening its first big office in San Francisco.")
 print([(ent.text, ent.label_) for ent in doc.ents])
 ```
 
-The entity ruler is designed to integrate with spaCy's existing statistical
-models and enhance the named entity recognizer. If it's added **before the
+The entity ruler is designed to integrate with spaCy's existing pipeline
+components and enhance the named entity recognizer. If it's added **before the
 `"ner"` component**, the entity recognizer will respect the existing entity
 spans and adjust its predictions around it. This can significantly improve
 accuracy in some cases. If it's added **after the `"ner"` component**, the
@@ -1087,20 +1112,20 @@ versa.
 </Infobox>
 
 When you save out an `nlp` object that has an `EntityRuler` added to its
-pipeline, its patterns are automatically exported to the model directory:
+pipeline, its patterns are automatically exported to the pipeline directory:
 
 ```python
 nlp = spacy.load("en_core_web_sm")
 ruler = nlp.add_pipe("entity_ruler")
 ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
-nlp.to_disk("/path/to/model")
+nlp.to_disk("/path/to/pipeline")
 ```
 
-The saved model now includes the `"entity_ruler"` in its
-[`config.cfg`](/api/data-formats#config) and the model directory contains a file
-`entityruler.jsonl` with the patterns. When you load the model back in, all
-pipeline components will be restored and deserialized – including the entity
-ruler. This lets you ship powerful model packages with binary weights _and_
+The saved pipeline now includes the `"entity_ruler"` in its
+[`config.cfg`](/api/data-formats#config) and the pipeline directory contains a
+file `entityruler.jsonl` with the patterns. When you load the pipeline back in,
+all pipeline components will be restored and deserialized – including the entity
+ruler. This lets you ship powerful pipeline packages with binary weights _and_
 rules included!
 
 ### Using a large number of phrase patterns {#entityruler-large-phrase-patterns new="2.2.4"}
@@ -1117,7 +1142,7 @@ of `"phrase_matcher_attr": "POS"` for the entity ruler.
 
 Running the full language pipeline across every pattern in a large list scales
 linearly and can therefore take a long time on large amounts of phrase patterns.
-As of spaCy 2.2.4 the `add_patterns` function has been refactored to use
+As of spaCy v2.2.4 the `add_patterns` function has been refactored to use
 nlp.pipe on all phrase patterns resulting in about a 10x-20x speed up with
 5,000-100,000 phrase patterns respectively. Even with this speedup (but
 especially if you're using an older version) the `add_patterns` function can
@@ -1144,7 +1169,7 @@ order to implement more abstract logic.
 
 ### Example: Expanding named entities {#models-rules-ner}
 
-When using the a pretrained
+When using a trained
 [named entity recognition](/usage/linguistic-features/#named-entities) model to
 extract information from your texts, you may find that the predicted span only
 includes parts of the entity you're looking for. Sometimes, this happens if
@@ -1154,15 +1179,15 @@ what you need for your application.
 
 > #### Where corpora come from
 >
-> Corpora used to train models from scratch are often produced in academia. They
-> contain text from various sources with linguistic features labeled manually by
-> human annotators (following a set of specific guidelines). The corpora are
-> then distributed with evaluation data, so other researchers can benchmark
-> their algorithms and everyone can report numbers on the same data. However,
-> most applications need to learn information that isn't contained in any
-> available corpus.
+> Corpora used to train pipelines from scratch are often produced in academia.
+> They contain text from various sources with linguistic features labeled
+> manually by human annotators (following a set of specific guidelines). The
+> corpora are then distributed with evaluation data, so other researchers can
+> benchmark their algorithms and everyone can report numbers on the same data.
+> However, most applications need to learn information that isn't contained in
+> any available corpus.
 
-For example, the corpus spaCy's [English models](/models/en) were trained on
+For example, the corpus spaCy's [English pipelines](/models/en) were trained on
 defines a `PERSON` entity as just the **person name**, without titles like "Mr."
 or "Dr.". This makes sense, because it makes it easier to resolve the entity
 type back to a knowledge base. But what if your application needs the full
diff --git a/website/docs/usage/saving-loading.md b/website/docs/usage/saving-loading.md
index 3f9435f5e..9955e7d84 100644
--- a/website/docs/usage/saving-loading.md
+++ b/website/docs/usage/saving-loading.md
@@ -4,7 +4,7 @@ menu:
   - ['Basics', 'basics']
   - ['Serialization Methods', 'serialization-methods']
   - ['Entry Points', 'entry-points']
-  - ['Models', 'models']
+  - ['Trained Pipelines', 'models']
 ---
 
 ## Basics {#basics hidden="true"}
@@ -25,10 +25,10 @@ can load in the data.
 > #### Saving the meta and config
 >
 > The [`nlp.meta`](/api/language#meta) attribute is a JSON-serializable
-> dictionary and contains all model meta information like the author and license
-> information. The [`nlp.config`](/api/language#config) attribute is a
+> dictionary and contains all pipeline meta information like the author and
+> license information. The [`nlp.config`](/api/language#config) attribute is a
 > dictionary containing the training configuration, pipeline component factories
-> and other settings. It is saved out with a model as the `config.cfg`.
+> and other settings. It is saved out with a pipeline as the `config.cfg`.
 
 ```python
 ### Serialize
@@ -45,12 +45,11 @@ for pipe_name in pipeline:
 nlp.from_bytes(bytes_data)
 ```
 
-This is also how spaCy does it under the hood when loading a model: it loads the
-model's `config.cfg` containing the language and pipeline information,
-initializes the language class, creates and adds the pipeline components based
-on the defined
-[factories](/usage/processing-pipeline#custom-components-factories) and _then_
-loads in the binary data. You can read more about this process
+This is also how spaCy does it under the hood when loading a pipeline: it loads
+the `config.cfg` containing the language and pipeline information, initializes
+the language class, creates and adds the pipeline components based on the
+defined [factories](/usage/processing-pipeline#custom-components-factories) and
+_then_ loads in the binary data. You can read more about this process
 [here](/usage/processing-pipelines#pipelines).
 
 ### Serializing Doc objects efficiently {#docs new="2.2"}
@@ -168,10 +167,10 @@ data = pickle.dumps(span_doc)
 ## Implementing serialization methods {#serialization-methods}
 
 When you call [`nlp.to_disk`](/api/language#to_disk),
-[`nlp.from_disk`](/api/language#from_disk) or load a model package, spaCy will
-iterate over the components in the pipeline, check if they expose a `to_disk` or
-`from_disk` method and if so, call it with the path to the model directory plus
-the string name of the component. For example, if you're calling
+[`nlp.from_disk`](/api/language#from_disk) or load a pipeline package, spaCy
+will iterate over the components in the pipeline, check if they expose a
+`to_disk` or `from_disk` method and if so, call it with the path to the pipeline
+directory plus the string name of the component. For example, if you're calling
 `nlp.to_disk("/path")`, the data for the named entity recognizer will be saved
 in `/path/ner`.
 
@@ -191,8 +190,8 @@ add to that data and saves and loads the data to and from a JSON file.
 > [source](https://github.com/explosion/spaCy/tree/master/spacy/pipeline/entityruler.py).
 > Patterns added to the component will be saved to a `.jsonl` file if the
 > pipeline is serialized to disk, and to a bytestring if the pipeline is
-> serialized to bytes. This allows saving out a model with a rule-based entity
-> recognizer and including all rules _with_ the model data.
+> serialized to bytes. This allows saving out a pipeline with a rule-based
+> entity recognizer and including all rules _with_ the component data.
 
 ```python
 ### {highlight="14-18,20-25"}
@@ -232,7 +231,7 @@ component's `to_disk` method.
 nlp = spacy.load("en_core_web_sm")
 my_component = nlp.add_pipe("my_component")
 my_component.add({"hello": "world"})
-nlp.to_disk("/path/to/model")
+nlp.to_disk("/path/to/pipeline")
 ```
 
 The contents of the directory would then look like this.
@@ -241,15 +240,15 @@ file `data.json` in its subdirectory:
 
 ```yaml
 ### Directory structure {highlight="2-3"}
-└── /path/to/model
+└── /path/to/pipeline
     ├── my_component     # data serialized by "my_component"
     │   └── data.json
     ├── ner              # data for "ner" component
     ├── parser           # data for "parser" component
     ├── tagger           # data for "tagger" component
-    ├── vocab            # model vocabulary
-    ├── meta.json        # model meta.json
-    ├── config.cfg       # model config
+    ├── vocab            # pipeline vocabulary
+    ├── meta.json        # pipeline meta.json
+    ├── config.cfg       # pipeline config
     └── tokenizer        # tokenization rules
 ```
 
@@ -258,18 +257,19 @@ When you load the data back in, spaCy will call the custom component's
 contents of `data.json`, convert them to a Python object and restore the
 component state. The same works for other types of data, of course – for
 instance, you could add a
-[wrapper for a model](/usage/processing-pipelines#wrapping-models-libraries)
-trained with a different library like TensorFlow or PyTorch and make spaCy load
-its weights automatically when you load the model package.
+[wrapper for a model](/usage/layers-architectures#frameworks) trained with a
+different library like TensorFlow or PyTorch and make spaCy load its weights
+automatically when you load the pipeline package.
 
 <Infobox title="Important note on loading custom components" variant="warning">
 
-When you load back a model with custom components, make sure that the components
-are **available** and that the [`@Language.component`](/api/language#component)
-or [`@Language.factory`](/api/language#factory) decorators are executed _before_
-your model is loaded back. Otherwise, spaCy won't know how to resolve the string
-name of a component factory like `"my_component"` back to a function. For more
-details, see the documentation on
+When you load back a pipeline with custom components, make sure that the
+components are **available** and that the
+[`@Language.component`](/api/language#component) or
+[`@Language.factory`](/api/language#factory) decorators are executed _before_
+your pipeline is loaded back. Otherwise, spaCy won't know how to resolve the
+string name of a component factory like `"my_component"` back to a function. For
+more details, see the documentation on
 [adding factories](/usage/processing-pipelines#custom-components-factories) or
 use [entry points](#entry-points) to make your extension package expose your
 custom components to spaCy automatically.
@@ -297,18 +297,19 @@ installed in the same environment – that's it.
 
 ### Custom components via entry points {#entry-points-components}
 
-When you load a model, spaCy will generally use the model's `config.cfg` to set
-up the language class and construct the pipeline. The pipeline is specified as a
+When you load a pipeline, spaCy will generally use its `config.cfg` to set up
+the language class and construct the pipeline. The pipeline is specified as a
 list of strings, e.g. `pipeline = ["tagger", "paser", "ner"]`. For each of those
 strings, spaCy will call `nlp.add_pipe` and look up the name in all factories
 defined by the decorators [`@Language.component`](/api/language#component) and
 [`@Language.factory`](/api/language#factory). This means that you have to import
-your custom components _before_ loading the model.
+your custom components _before_ loading the pipeline.
 
-Using entry points, model packages and extension packages can define their own
-`"spacy_factories"`, which will be loaded automatically in the background when
-the `Language` class is initialized. So if a user has your package installed,
-they'll be able to use your components – even if they **don't import them**!
+Using entry points, pipeline packages and extension packages can define their
+own `"spacy_factories"`, which will be loaded automatically in the background
+when the `Language` class is initialized. So if a user has your package
+installed, they'll be able to use your components – even if they **don't import
+them**!
 
 To stick with the theme of
 [this entry points blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
@@ -343,10 +344,10 @@ def snek_component(doc):
 
 Since it's a very complex and sophisticated module, you want to split it off
 into its own package so you can version it and upload it to PyPi. You also want
-your custom model to be able to define `pipeline = ["snek"]` in its
+your custom package to be able to define `pipeline = ["snek"]` in its
 `config.cfg`. For that, you need to be able to tell spaCy where to find the
 component `"snek"`. If you don't do this, spaCy will raise an error when you try
-to load the model because there's no built-in `"snek"` component. To add an
+to load the pipeline because there's no built-in `"snek"` component. To add an
 entry to the factories, you can now expose it in your `setup.py` via the
 `entry_points` dictionary:
 
@@ -380,7 +381,7 @@ $ python setup.py develop
 spaCy is now able to create the pipeline component `"snek"` – even though you
 never imported `snek_component`. When you save the
 [`nlp.config`](/api/language#config) to disk, it includes an entry for your
-`"snek"` component and any model you train with this config will include the
+`"snek"` component and any pipeline you train with this config will include the
 component and know how to load it – if your `snek` package is installed.
 
 > #### config.cfg (excerpt)
@@ -449,9 +450,9 @@ entry_points={
 
 The factory can also implement other pipeline component like `to_disk` and
 `from_disk` for serialization, or even `update` to make the component trainable.
-If a component exposes a `from_disk` method and is included in a model's
-pipeline, spaCy will call it on load. This lets you ship custom data with your
-model. When you save out a model using `nlp.to_disk` and the component exposes a
+If a component exposes a `from_disk` method and is included in a pipeline, spaCy
+will call it on load. This lets you ship custom data with your pipeline package.
+When you save out a pipeline using `nlp.to_disk` and the component exposes a
 `to_disk` method, it will be called with the disk path.
 
 ```python
@@ -467,8 +468,8 @@ def from_disk(self, path, exclude=tuple()):
     return self
 ```
 
-The above example will serialize the current snake in a `snek.txt` in the model
-data directory. When a model using the `snek` component is loaded, it will open
+The above example will serialize the current snake in a `snek.txt` in the data
+directory. When a pipeline using the `snek` component is loaded, it will open
 the `snek.txt` and make it available to the component.
 
 ### Custom language classes via entry points {#entry-points-languages}
@@ -476,7 +477,7 @@ the `snek.txt` and make it available to the component.
 To stay with the theme of the previous example and
 [this blog post on entry points](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
 let's imagine you wanted to implement your own `SnekLanguage` class for your
-custom model – but you don't necessarily want to modify spaCy's code to add a
+custom pipeline – but you don't necessarily want to modify spaCy's code to add a
 language. In your package, you could then implement the following
 [custom language subclass](/usage/linguistic-features#language-subclass):
 
@@ -510,10 +511,10 @@ setup(
 ```
 
 In spaCy, you can then load the custom `snk` language and it will be resolved to
-`SnekLanguage` via the custom entry point. This is especially relevant for model
-packages you train, which could then specify `lang = snk` in their `config.cfg`
-without spaCy raising an error because the language is not available in the core
-library.
+`SnekLanguage` via the custom entry point. This is especially relevant for
+pipeline packages you [train](/usage/training), which could then specify
+`lang = snk` in their `config.cfg` without spaCy raising an error because the
+language is not available in the core library.
 
 ### Custom displaCy colors via entry points {#entry-points-displacy new="2.2"}
 
@@ -526,7 +527,7 @@ values.
 
 > #### Domain-specific NER labels
 >
-> Good examples of models with domain-specific label schemes are
+> Good examples of pipelines with domain-specific label schemes are
 > [scispaCy](/universe/project/scispacy) and
 > [Blackstone](/universe/project/blackstone).
 
@@ -559,24 +560,23 @@ import DisplaCyEntSnekHtml from 'images/displacy-ent-snek.html'
 
 <Iframe title="displaCy visualization of entities" html={DisplaCyEntSnekHtml} height={100} />
 
-## Saving, loading and distributing models {#models}
+## Saving, loading and distributing trained pipelines {#models}
 
-After training your model, you'll usually want to save its state, and load it
+After training your pipeline, you'll usually want to save its state, and load it
 back later. You can do this with the [`Language.to_disk`](/api/language#to_disk)
 method:
 
 ```python
-nlp.to_disk("./en_example_model")
+nlp.to_disk("./en_example_pipeline")
 ```
 
 The directory will be created if it doesn't exist, and the whole pipeline data,
-model meta and model configuration will be written out. To make the model more
-convenient to deploy, we recommend wrapping it as a
-[Python package](/api/cli#package).
+meta and configuration will be written out. To make the pipeline more convenient
+to deploy, we recommend wrapping it as a [Python package](/api/cli#package).
 
 <Accordion title="What’s the difference between the config.cfg and meta.json?" spaced id="models-meta-vs-config">
 
-When you save a model in spaCy v3.0+, two files will be exported: a
+When you save a pipeline in spaCy v3.0+, two files will be exported: a
 [`config.cfg`](/api/data-formats#config) based on
 [`nlp.config`](/api/language#config) and a [`meta.json`](/api/data-formats#meta)
 based on [`nlp.meta`](/api/language#meta).
@@ -587,42 +587,42 @@ based on [`nlp.meta`](/api/language#meta).
   [pipeline components](/usage/processing-pipelines#custom-components) or
   [model architectures](/api/architectures). Given a config, spaCy is able
   reconstruct the whole tree of objects and the `nlp` object. An exported config
-  can also be used to [train a model](/usage/training#conig) with the same
+  can also be used to [train a pipeline](/usage/training#config) with the same
   settings.
-- **meta**: Meta information about the model and the Python package, such as the
-  author information, license, version, data sources and label scheme. This is
-  mostly used for documentation purposes and for packaging models. It has no
-  impact on the functionality of the `nlp` object.
+- **meta**: Meta information about the pipeline and the Python package, such as
+  the author information, license, version, data sources and label scheme. This
+  is mostly used for documentation purposes and for packaging pipelines. It has
+  no impact on the functionality of the `nlp` object.
 
 </Accordion>
 
-### Generating a model package {#models-generating}
+### Generating a pipeline package {#models-generating}
 
 <Infobox title="Important note" variant="warning">
 
-The model packages are **not suitable** for the public
+Pipeline packages are typically **not suitable** for the public
 [pypi.python.org](https://pypi.python.org) directory, which is not designed for
 binary data and files over 50 MB. However, if your company is running an
-**internal installation** of PyPi, publishing your models on there can be a
-convenient way to share them with your team.
+**internal installation** of PyPi, publishing your pipeline packages on there
+can be a convenient way to share them with your team.
 
 </Infobox>
 
 spaCy comes with a handy CLI command that will create all required files, and
-walk you through generating the meta data. You can also create the meta.json
-manually and place it in the model data directory, or supply a path to it using
-the `--meta` flag. For more info on this, see the [`package`](/api/cli#package)
+walk you through generating the meta data. You can also create the `meta.json`
+manually and place it in the data directory, or supply a path to it using the
+`--meta` flag. For more info on this, see the [`package`](/api/cli#package)
 docs.
 
 > #### meta.json (example)
 >
 > ```json
 > {
->   "name": "example_model",
+>   "name": "example_pipeline",
 >   "lang": "en",
 >   "version": "1.0.0",
 >   "spacy_version": ">=2.0.0,<3.0.0",
->   "description": "Example model for spaCy",
+>   "description": "Example pipeline for spaCy",
 >   "author": "You",
 >   "email": "you@example.com",
 >   "license": "CC BY-SA 3.0"
@@ -630,27 +630,27 @@ docs.
 > ```
 
 ```cli
-$ python -m spacy package ./en_example_model ./my_models
+$ python -m spacy package ./en_example_pipeline ./my_pipelines
 ```
 
-This command will create a model package directory and will run
+This command will create a pipeline package directory and will run
 `python setup.py sdist` in that directory to create `.tar.gz` archive of your
-model package that can be installed using `pip install`.
+package that can be installed using `pip install`.
 
 ```yaml
 ### Directory structure
 └── /
-    ├── MANIFEST.in                        # to include meta.json
-    ├── meta.json                          # model meta data
-    ├── setup.py                           # setup file for pip installation
-    ├── en_example_model                   # model directory
-    │    ├── __init__.py                   # init for pip installation
-    │    └── en_example_model-1.0.0        # model data
-    │        ├── config.cfg                # model config
-    │        ├── meta.json                 # model meta
-    │        └── ...                       # directories with component data
+    ├── MANIFEST.in                           # to include meta.json
+    ├── meta.json                             # pipeline meta data
+    ├── setup.py                              # setup file for pip installation
+    ├── en_example_pipeline                   # pipeline directory
+    │    ├── __init__.py                      # init for pip installation
+    │    └── en_example_pipeline-1.0.0        # pipeline data
+    │        ├── config.cfg                   # pipeline config
+    │        ├── meta.json                    # pipeline meta
+    │        └── ...                          # directories with component data
     └── dist
-        └── en_example_model-1.0.0.tar.gz  # installable package
+        └── en_example_pipeline-1.0.0.tar.gz  # installable package
 ```
 
 You can also find templates for all files in the
@@ -659,16 +659,15 @@ If you're creating the package manually, keep in mind that the directories need
 to be named according to the naming conventions of `lang_name` and
 `lang_name-version`.
 
-### Customizing the model setup {#models-custom}
+### Customizing the package setup {#models-custom}
 
-The `load()` method that comes with our model package templates will take care
-of putting all this together and returning a `Language` object with the loaded
-pipeline and data. If your model requires custom
-[pipeline components](/usage/processing-pipelines) or a custom language class,
-you can also **ship the code with your model** and include it in the
-`__init__.py` – for example, to register custom
-[pipeline components](/usage/processing-pipelines#custom-components) before the
-`nlp` object is created.
+The `load()` method that comes with our pipeline package templates will take
+care of putting all this together and returning a `Language` object with the
+loaded pipeline and data. If your pipeline requires
+[custom components](/usage/processing-pipelines#custom-components) or a custom
+language class, you can also **ship the code with your package** and include it
+in the `__init__.py` – for example, to register component before the `nlp`
+object is created.
 
 <Infobox variant="warning" title="Important note on making manual edits">
 
@@ -682,16 +681,16 @@ spaCy to export the current state of its `nlp` objects via
 
 </Infobox>
 
-### Loading a custom model package {#loading}
+### Loading a custom pipeline package {#loading}
 
-To load a model from a data directory, you can use
+To load a pipeline from a data directory, you can use
 [`spacy.load()`](/api/top-level#spacy.load) with the local path. This will look
 for a `config.cfg` in the directory and use the `lang` and `pipeline` settings
 to initialize a `Language` class with a processing pipeline and load in the
 model data.
 
 ```python
-nlp = spacy.load("/path/to/model")
+nlp = spacy.load("/path/to/pipeline")
 ```
 
 If you want to **load only the binary data**, you'll have to create a `Language`
diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
index 8ea6a6ca0..82fec4b6a 100644
--- a/website/docs/usage/spacy-101.md
+++ b/website/docs/usage/spacy-101.md
@@ -130,14 +130,15 @@ related to more general machine learning functionality.
 ### Statistical models {#statistical-models}
 
 While some of spaCy's features work independently, others require
-[ statistical models](/models) to be loaded, which enable spaCy to **predict**
-linguistic annotations – for example, whether a word is a verb or a noun. spaCy
-currently offers statistical models for a variety of languages, which can be
-installed as individual Python modules. Models can differ in size, speed, memory
-usage, accuracy and the data they include. The model you choose always depends
-on your use case and the texts you're working with. For a general-purpose use
-case, the small, default models are always a good start. They typically include
-the following components:
+[trained pipelines](/models) to be loaded, which enable spaCy to **predict**
+linguistic annotations – for example, whether a word is a verb or a noun. A
+trained pipeline can consist of multiple components that use a statistical model
+trained on labeled data. spaCy currently offers trained pipelines for a variety
+of languages, which can be installed as individual Python modules. Pipeline
+packages can differ in size, speed, memory usage, accuracy and the data they
+include. The package you choose always depends on your use case and the texts
+you're working with. For a general-purpose use case, the small, default packages
+are always a good start. They typically include the following components:
 
 - **Binary weights** for the part-of-speech tagger, dependency parser and named
   entity recognizer to predict those annotations in context.
@@ -146,8 +147,9 @@ the following components:
 - **Data files** like lemmatization rules and lookup tables.
 - **Word vectors**, i.e. multi-dimensional meaning representations of words that
   let you determine how similar they are to each other.
-- **Configuration** options, like the language and processing pipeline settings,
-  to put spaCy in the correct state when you load in the model.
+- **Configuration** options, like the language and processing pipeline settings
+  and model implementations to use, to put spaCy in the correct state when you
+  load the pipeline.
 
 ## Linguistic annotations {#annotations}
 
@@ -158,7 +160,7 @@ analyzing text, it makes a huge difference whether a noun is the subject of a
 sentence, or the object – or whether "google" is used as a verb, or refers to
 the website or company in a specific context.
 
-> #### Loading models
+> #### Loading pipelines
 >
 > ```cli
 > $ python -m spacy download en_core_web_sm
@@ -167,11 +169,11 @@ the website or company in a specific context.
 > >>> nlp = spacy.load("en_core_web_sm")
 > ```
 
-Once you've [downloaded and installed](/usage/models) a model, you can load it
-via [`spacy.load()`](/api/top-level#spacy.load). This will return a `Language`
-object containing all components and data needed to process text. We usually
-call it `nlp`. Calling the `nlp` object on a string of text will return a
-processed `Doc`:
+Once you've [downloaded and installed](/usage/models) a trained pipeline, you
+can load it via [`spacy.load`](/api/top-level#spacy.load). This will return a
+`Language` object containing all components and data needed to process text. We
+usually call it `nlp`. Calling the `nlp` object on a string of text will return
+a processed `Doc`:
 
 ```python
 ### {executable="true"}
@@ -233,7 +235,7 @@ To learn more about entity recognition in spaCy, how to **add your own
 entities** to a document and how to **train and update** the entity predictions
 of a model, see the usage guides on
 [named entity recognition](/usage/linguistic-features#named-entities) and
-[training the named entity recognizer](/usage/training#ner).
+[training pipelines](/usage/training).
 
 </Infobox>
 
@@ -346,7 +348,7 @@ The mapping of words to hashes doesn't depend on any state. To make sure each
 value is unique, spaCy uses a
 [hash function](https://en.wikipedia.org/wiki/Hash_function) to calculate the
 hash **based on the word string**. This also means that the hash for "coffee"
-will always be the same, no matter which model you're using or how you've
+will always be the same, no matter which pipeline you're using or how you've
 configured spaCy.
 
 However, hashes **cannot be reversed** and there's no way to resolve
@@ -391,7 +393,7 @@ import Serialization101 from 'usage/101/\_serialization.md'
 
 <Infobox title="Saving and loading" emoji="📖">
 
-To learn more about how to **save and load your own models**, see the usage
+To learn more about how to **save and load your own pipelines**, see the usage
 guide on [saving and loading](/usage/saving-loading#models).
 
 </Infobox>
@@ -402,9 +404,9 @@ import Training101 from 'usage/101/\_training.md'
 
 <Training101 />
 
-<Infobox title="Training statistical models" emoji="📖">
+<Infobox title="Training pipelines and models" emoji="📖">
 
-To learn more about **training and updating** models, how to create training
+To learn more about **training and updating** pipelines, how to create training
 data and how to improve spaCy's named entity recognition models, see the usage
 guides on [training](/usage/training).
 
@@ -454,8 +456,8 @@ via the following platforms:
   practices**.
 - [GitHub issue tracker](https://github.com/explosion/spaCy/issues): **Bug
   reports** and **improvement suggestions**, i.e. everything that's likely
-  spaCy's fault. This also includes problems with the models beyond statistical
-  imprecisions, like patterns that point to a bug.
+  spaCy's fault. This also includes problems with the trained pipelines beyond
+  statistical imprecisions, like patterns that point to a bug.
 
 <Infobox title="Important note" variant="warning">
 
@@ -484,10 +486,10 @@ Another way of getting involved is to help us improve the
 happen to speak one of the languages currently in
 [alpha support](/usage/models#languages). Even adding simple tokenizer
 exceptions, stop words or lemmatizer data can make a big difference. It will
-also make it easier for us to provide a statistical model for the language in
-the future. Submitting a test that documents a bug or performance issue, or
-covers functionality that's especially important for your application is also
-very helpful. This way, you'll also make sure we never accidentally introduce
+also make it easier for us to provide a trained pipeline for the language in the
+future. Submitting a test that documents a bug or performance issue, or covers
+functionality that's especially important for your application is also very
+helpful. This way, you'll also make sure we never accidentally introduce
 regressions to the parts of the library that you care about the most.
 
 **For more details on the types of contributions we're looking for, the code
diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
index 59766bada..9c18e4606 100644
--- a/website/docs/usage/training.md
+++ b/website/docs/usage/training.md
@@ -1,5 +1,6 @@
 ---
-title: Training Models
+title: Training Pipelines & Models
+teaser: Train and update components on your own data and integrate custom models
 next: /usage/layers-architectures
 menu:
   - ['Introduction', 'basics']
@@ -10,7 +11,7 @@ menu:
   - ['Internal API', 'api']
 ---
 
-## Introduction to training models {#basics hidden="true"}
+## Introduction to training {#basics hidden="true"}
 
 import Training101 from 'usage/101/\_training.md'
 
@@ -25,13 +26,13 @@ new, active learning-powered annotation tool we've developed. Prodigy is fast
 and extensible, and comes with a modern **web application** that helps you
 collect training data faster. It integrates seamlessly with spaCy, pre-selects
 the **most relevant examples** for annotation, and lets you train and evaluate
-ready-to-use spaCy models.
+ready-to-use spaCy pipelines.
 
 </Infobox>
 
 ## Quickstart {#quickstart tag="new"}
 
-The recommended way to train your spaCy models is via the
+The recommended way to train your spaCy pipelines is via the
 [`spacy train`](/api/cli#train) command on the command line. It only needs a
 single [`config.cfg`](#config) **configuration file** that includes all settings
 and hyperparameters. You can optionally [overwrite](#config-overrides) settings
@@ -94,9 +95,9 @@ $ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy
 ## Training config {#config}
 
 Training config files include all **settings and hyperparameters** for training
-your model. Instead of providing lots of arguments on the command line, you only
-need to pass your `config.cfg` file to [`spacy train`](/api/cli#train). Under
-the hood, the training config uses the
+your pipeline. Instead of providing lots of arguments on the command line, you
+only need to pass your `config.cfg` file to [`spacy train`](/api/cli#train).
+Under the hood, the training config uses the
 [configuration system](https://thinc.ai/docs/usage-config) provided by our
 machine learning library [Thinc](https://thinc.ai). This also makes it easy to
 integrate custom models and architectures, written in your framework of choice.
@@ -178,27 +179,26 @@ $ python -m spacy train config.cfg --paths.train ./corpus/train.spacy --paths.de
 ```
 
 Only existing sections and values in the config can be overwritten. At the end
-of the training, the final filled `config.cfg` is exported with your model, so
-you'll always have a record of the settings that were used, including your
+of the training, the final filled `config.cfg` is exported with your pipeline,
+so you'll always have a record of the settings that were used, including your
 overrides. Overrides are added before [variables](#config-interpolation) are
 resolved, by the way – so if you need to use a value in multiple places,
 reference it across your config and override it on the CLI once.
 
 ### Defining pipeline components {#config-components}
 
-When you train a model, you typically train a
-[pipeline](/usage/processing-pipelines) of **one or more components**. The
-`[components]` block in the config defines the available pipeline components and
-how they should be created – either by a built-in or custom
-[factory](/usage/processing-pipelines#built-in), or
+You typically train a [pipeline](/usage/processing-pipelines) of **one or more
+components**. The `[components]` block in the config defines the available
+pipeline components and how they should be created – either by a built-in or
+custom [factory](/usage/processing-pipelines#built-in), or
 [sourced](/usage/processing-pipelines#sourced-components) from an existing
-pretrained model. For example, `[components.parser]` defines the component named
+trained pipeline. For example, `[components.parser]` defines the component named
 `"parser"` in the pipeline. There are different ways you might want to treat
 your components during training, and the most common scenarios are:
 
 1. Train a **new component** from scratch on your data.
-2. Update an existing **pretrained component** with more examples.
-3. Include an existing pretrained component without updating it.
+2. Update an existing **trained component** with more examples.
+3. Include an existing trained component without updating it.
 4. Include a non-trainable component, like a rule-based
    [`EntityRuler`](/api/entityruler) or [`Sentencizer`](/api/sentencizer), or a
    fully [custom component](/usage/processing-pipelines#custom-components).
@@ -209,16 +209,16 @@ If a component block defines a `factory`, spaCy will look it up in the
 new component from scratch. All settings defined in the config block will be
 passed to the component factory as arguments. This lets you configure the model
 settings and hyperparameters. If a component block defines a `source`, the
-component will be copied over from an existing pretrained model, with its
+component will be copied over from an existing trained pipeline, with its
 existing weights. This lets you include an already trained component in your
-model pipeline, or update a pretrained component with more data specific to your
-use case.
+pipeline, or update a trained component with more data specific to your use
+case.
 
 ```ini
 ### config.cfg (excerpt)
 [components]
 
-# "parser" and "ner" are sourced from a pretrained model
+# "parser" and "ner" are sourced from a trained pipeline
 [components.parser]
 source = "en_core_web_sm"
 
@@ -243,7 +243,7 @@ weights and [resume training](/api/language#resume_training).
 
 If you don't want a component to be updated, you can **freeze** it by adding it
 to the `frozen_components` list in the `[training]` block. Frozen components are
-**not updated** during training and are included in the final trained model
+**not updated** during training and are included in the final trained pipeline
 as-is.
 
 > #### Note on frozen components
@@ -252,8 +252,8 @@ as-is.
 > still **run** during training and evaluation. This is very important, because
 > they may still impact your model's performance – for instance, a sentence
 > boundary detector can impact what the parser or entity recognizer considers a
-> valid parse. So the evaluation results should always reflect what your model
-> will produce at runtime.
+> valid parse. So the evaluation results should always reflect what your
+> pipeline will produce at runtime.
 
 ```ini
 [nlp]
@@ -377,7 +377,8 @@ A **model architecture** is a function that wires up a Thinc
 component or as a layer of a larger network. You can use Thinc as a thin
 [wrapper around frameworks](https://thinc.ai/docs/usage-frameworks) such as
 PyTorch, TensorFlow or MXNet, or you can implement your logic in Thinc
-[directly](https://thinc.ai/docs/usage-models).
+[directly](https://thinc.ai/docs/usage-models). For more details and examples,
+see the usage guide on [layers and architectures](/usage/layers-architectures).
 
 spaCy's built-in components will never construct their `Model` instances
 themselves, so you won't have to subclass the component to change its model
@@ -395,15 +396,13 @@ different tasks. For example:
 | [TransitionBasedParser](/api/architectures#TransitionBasedParser) | Build a [transition-based parser](https://explosion.ai/blog/parsing-english-in-python) model used in the default [`EntityRecognizer`](/api/entityrecognizer) and [`DependencyParser`](/api/dependencyparser). ~~Model[List[Docs], List[List[Floats2d]]]~~ |
 | [TextCatEnsemble](/api/architectures#TextCatEnsemble)             | Stacked ensemble of a bag-of-words model and a neural network model with an internal CNN embedding layer. Used in the default [`TextCategorizer`](/api/textcategorizer). ~~Model[List[Doc], Floats2d]~~                                                   |
 
-<!-- TODO: link to not yet existing usage page on custom architectures etc. -->
-
 ### Metrics, training output and weighted scores {#metrics}
 
-When you train a model using the [`spacy train`](/api/cli#train) command, you'll
-see a table showing the metrics after each pass over the data. The available
-metrics **depend on the pipeline components**. Pipeline components also define
-which scores are shown and how they should be **weighted in the final score**
-that decides about the best model.
+When you train a pipeline using the [`spacy train`](/api/cli#train) command,
+you'll see a table showing the metrics after each pass over the data. The
+available metrics **depend on the pipeline components**. Pipeline components
+also define which scores are shown and how they should be **weighted in the
+final score** that decides about the best model.
 
 The `training.score_weights` setting in your `config.cfg` lets you customize the
 scores shown in the table and how they should be weighted. In this example, the
@@ -416,8 +415,8 @@ score.
 >
 > At the end of your training process, you typically want to select the **best
 > model** – but what "best" means depends on the available components and your
-> specific use case. For instance, you may prefer a model with higher NER and
-> lower POS tagging accuracy over a model with lower NER and higher POS
+> specific use case. For instance, you may prefer a pipeline with higher NER and
+> lower POS tagging accuracy over a pipeline with lower NER and higher POS
 > accuracy. You can express this preference in the score weights, e.g. by
 > assigning `ents_f` (NER F-score) a higher weight.
 
@@ -474,11 +473,9 @@ Each custom function can have any numbers of arguments that are passed in via
 the [config](#config), just the built-in functions. If your function defines
 **default argument values**, spaCy is able to auto-fill your config when you run
 [`init fill-config`](/api/cli#init-fill-config). If you want to make sure that a
-given parameter is always explicitely set in the config, avoid setting a default
+given parameter is always explicitly set in the config, avoid setting a default
 value for it.
 
-<!-- TODO: possibly link to new (not yet created) page on creating models ? -->
-
 ### Training with custom code {#custom-code}
 
 > #### Example
@@ -491,8 +488,8 @@ The [`spacy train`](/api/cli#train) recipe lets you specify an optional argument
 `--code` that points to a Python file. The file is imported before training and
 allows you to add custom functions and architectures to the function registry
 that can then be referenced from your `config.cfg`. This lets you train spaCy
-models with custom components, without having to re-implement the whole training
-workflow.
+pipelines with custom components, without having to re-implement the whole
+training workflow.
 
 #### Example: Modifying the nlp object {#custom-code-nlp-callbacks}
 
@@ -605,12 +602,73 @@ to your Python file. Before loading the config, spaCy will import the
 $ python -m spacy train config.cfg --output ./output --code ./functions.py
 ```
 
+#### Example: Custom logging function {#custom-logging}
+
+During training, the results of each step are passed to a logger function. By
+default, these results are written to the console with the
+[`ConsoleLogger`](/api/top-level#ConsoleLogger). There is also built-in support
+for writing the log files to [Weights & Biases](https://www.wandb.com/) with the
+[`WandbLogger`](/api/top-level#WandbLogger). The logger function receives a
+**dictionary** with the following keys:
+
+| Key            | Value                                                                                          |
+| -------------- | ---------------------------------------------------------------------------------------------- |
+| `epoch`        | How many passes over the data have been completed. ~~int~~                                     |
+| `step`         | How many steps have been completed. ~~int~~                                                    |
+| `score`        | The main score from the last evaluation, measured on the dev set. ~~float~~                    |
+| `other_scores` | The other scores from the last evaluation, measured on the dev set. ~~Dict[str, Any]~~         |
+| `losses`       | The accumulated training losses, keyed by component name. ~~Dict[str, float]~~                 |
+| `checkpoints`  | A list of previous results, where each result is a (score, step, epoch) tuple. ~~List[Tuple]~~ |
+
+You can easily implement and plug in your own logger that records the training
+results in a custom way, or sends them to an experiment management tracker of
+your choice. In this example, the function `my_custom_logger.v1` writes the
+tabular results to a file:
+
+> ```ini
+> ### config.cfg (excerpt)
+> [training.logger]
+> @loggers = "my_custom_logger.v1"
+> log_path = "my_file.tab"
+> ```
+
+```python
+### functions.py
+from typing import Tuple, Callable, Dict, Any
+import spacy
+from pathlib import Path
+
+@spacy.registry.loggers("my_custom_logger.v1")
+def custom_logger(log_path):
+    def setup_logger(nlp: "Language") -> Tuple[Callable, Callable]:
+        with Path(log_path).open("w") as file_:
+            file_.write("step\\t")
+            file_.write("score\\t")
+            for pipe in nlp.pipe_names:
+                file_.write(f"loss_{pipe}\\t")
+            file_.write("\\n")
+
+        def log_step(info: Dict[str, Any]):
+            with Path(log_path).open("a") as file_:
+                file_.write(f"{info['step']}\\t")
+                file_.write(f"{info['score']}\\t")
+                for pipe in nlp.pipe_names:
+                    file_.write(f"{info['losses'][pipe]}\\t")
+                file_.write("\\n")
+
+        def finalize():
+            pass
+
+        return log_step, finalize
+
+    return setup_logger
+```
+
 #### Example: Custom batch size schedule {#custom-code-schedule}
 
-For example, let's say you've implemented your own batch size schedule to use
-during training. The `@spacy.registry.schedules` decorator lets you register
-that function in the `schedules` [registry](/api/top-level#registry) and assign
-it a string name:
+You can also implement your own batch size schedule to use during training. The
+`@spacy.registry.schedules` decorator lets you register that function in the
+`schedules` [registry](/api/top-level#registry) and assign it a string name:
 
 > #### Why the version in the name?
 >
@@ -744,18 +802,46 @@ def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Examp
 
 ### Defining custom architectures {#custom-architectures}
 
-<!-- TODO: this should probably move to new section on models -->
+Built-in pipeline components such as the tagger or named entity recognizer are
+constructed with default neural network [models](/api/architectures). You can
+change the model architecture entirely by implementing your own custom models
+and providing those in the config when creating the pipeline component. See the
+documentation on [layers and model architectures](/usage/layers-architectures)
+for more details.
+
+> ```ini
+> ### config.cfg
+> [components.tagger]
+> factory = "tagger"
+>
+> [components.tagger.model]
+> @architectures = "custom_neural_network.v1"
+> output_width = 512
+> ```
+
+```python
+### functions.py
+from typing import List
+from thinc.types import Floats2d
+from thinc.api import Model
+import spacy
+from spacy.tokens import Doc
+
+@spacy.registry.architectures("custom_neural_network.v1")
+def MyModel(output_width: int) -> Model[List[Doc], List[Floats2d]]:
+    return create_model(output_width)
+```
 
 ## Internal training API {#api}
 
 <Infobox variant="warning">
 
 spaCy gives you full control over the training loop. However, for most use
-cases, it's recommended to train your models via the
+cases, it's recommended to train your pipelines via the
 [`spacy train`](/api/cli#train) command with a [`config.cfg`](#config) to keep
 track of your settings and hyperparameters, instead of writing your own training
 scripts from scratch. [Custom registered functions](#custom-code) should
-typically give you everything you need to train fully custom models with
+typically give you everything you need to train fully custom pipelines with
 [`spacy train`](/api/cli#train).
 
 </Infobox>
@@ -788,8 +874,8 @@ their assigned part-of-speech tags.
 > #### About the tag map
 >
 > The tag map is part of the vocabulary and defines the annotation scheme. If
-> you're training a new language model, this will let you map the tags present
-> in the treebank you train on to spaCy's tag scheme:
+> you're training a new pipeline, this will let you map the tags present in the
+> treebank you train on to spaCy's tag scheme:
 >
 > ```python
 > tag_map = {"N": {"pos": "NOUN"}, "V": {"pos": "VERB"}}
@@ -838,15 +924,16 @@ it harder for the model to memorize the training data. For example, a `0.25`
 dropout means that each feature or internal representation has a 1/4 likelihood
 of being dropped.
 
-> - [`nlp`](/api/language): The `nlp` object with the model.
+> - [`nlp`](/api/language): The `nlp` object with the pipeline components and
+>   their models.
 > - [`nlp.begin_training`](/api/language#begin_training): Start the training and
->   return an optimizer to update the model's weights.
+>   return an optimizer to update the component model weights.
 > - [`Optimizer`](https://thinc.ai/docs/api-optimizers): Function that holds
 >   state between updates.
-> - [`nlp.update`](/api/language#update): Update model with examples.
+> - [`nlp.update`](/api/language#update): Update component models with examples.
 > - [`Example`](/api/example): object holding predictions and gold-standard
 >   annotations.
-> - [`nlp.to_disk`](/api/language#to_disk): Save the updated model to a
+> - [`nlp.to_disk`](/api/language#to_disk): Save the updated pipeline to a
 >   directory.
 
 ```python
@@ -858,7 +945,7 @@ for itn in range(100):
         doc = nlp.make_doc(raw_text)
         example = Example.from_dict(doc, {"entities": entity_offsets})
         nlp.update([example], sgd=optimizer)
-nlp.to_disk("/model")
+nlp.to_disk("/output")
 ```
 
 The [`nlp.update`](/api/language#update) method takes the following arguments:
diff --git a/website/docs/usage/v3.md b/website/docs/usage/v3.md
index d5fea9fee..45ed7b0c8 100644
--- a/website/docs/usage/v3.md
+++ b/website/docs/usage/v3.md
@@ -42,7 +42,7 @@ menu:
 
 <Infobox title="Details & Documentation" emoji="📖" list>
 
-- **Usage:** [Training models](/usage/training)
+- **Usage:** [Training pipelines and models](/usage/training)
 - **Thinc:** [Thinc's config system](https://thinc.ai/docs/usage-config),
   [`Config`](https://thinc.ai/docs/api-config#config)
 - **CLI:** [`train`](/api/cli#train), [`pretrain`](/api/cli#pretrain),
@@ -59,14 +59,14 @@ menu:
 <Infobox title="Details & Documentation" emoji="📖" list>
 
 - **Usage:** [Embeddings & Transformers](/usage/embeddings-transformers),
-  [Training models](/usage/training)
+  [Training pipelines and models](/usage/training)
 - **API:** [`Transformer`](/api/transformer),
   [`TransformerData`](/api/transformer#transformerdata),
   [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
 - **Architectures: ** [TransformerModel](/api/architectures#TransformerModel),
-  [Tok2VecListener](/api/architectures#transformers-Tok2VecListener),
+  [TransformerListener](/api/architectures#TransformerListener),
   [Tok2VecTransformer](/api/architectures#Tok2VecTransformer)
-- **Models:** [`en_core_trf_lg_sm`](/models/en)
+- **Trained Pipelines:** [`en_core_trf_lg_sm`](/models/en)
 - **Implementation:**
   [`spacy-transformers`](https://github.com/explosion/spacy-transformers)
 
@@ -76,8 +76,7 @@ menu:
 
 <Infobox title="Details & Documentation" emoji="📖" list>
 
-<!-- TODO: link to new custom models page -->
-
+- **Usage: ** [Layers and architectures](/usage/layers-architectures)
 - **Thinc: **
   [Wrapping PyTorch, TensorFlow & MXNet](https://thinc.ai/docs/usage-frameworks)
 - **API:** [Model architectures](/api/architectures), [`Pipe`](/api/pipe)
@@ -102,10 +101,10 @@ menu:
 
 spaCy projects let you manage and share **end-to-end spaCy workflows** for
 different **use cases and domains**, and orchestrate training, packaging and
-serving your custom models. You can start off by cloning a pre-defined project
-template, adjust it to fit your needs, load in your data, train a model, export
-it as a Python package, upload your outputs to a remote storage and share your
-results with your team.
+serving your custom pipelines. You can start off by cloning a pre-defined
+project template, adjust it to fit your needs, load in your data, train a
+pipeline, export it as a Python package, upload your outputs to a remote storage
+and share your results with your team.
 
 ![Illustration of project workflow and commands](../images/projects.svg)
 
@@ -121,14 +120,14 @@ data, [Streamlit](/usage/projects#streamlit) for building interactive apps,
 
 The easiest way to get started with an end-to-end training process is to clone a
 [project](/usage/projects) template. Projects let you manage multi-step
-workflows, from data preprocessing to training and packaging your model.
+workflows, from data preprocessing to training and packaging your pipeline.
 
 </Project>-->
 
 <Infobox title="Details & Documentation" emoji="📖" list>
 
 - **Usage:** [spaCy projects](/usage/projects),
-  [Training models](/usage/training)
+  [Training pipelines and models](/usage/training)
 - **CLI:** [`project`](/api/cli#project), [`train`](/api/cli#train)
 - **Templates:** [`projects`](https://github.com/explosion/projects)
 
@@ -183,7 +182,7 @@ now easier and more convenient. The `@Language.component` and
 `@Language.factory` decorators let you register your component, define its
 default configuration and meta data, like the attribute values it assigns and
 requires. Any custom component can be included during training, and sourcing
-components from existing pretrained models lets you **mix and match custom
+components from existing trained pipelines lets you **mix and match custom
 pipelines**. The `nlp.analyze_pipes` method outputs structured information about
 the current pipeline and its components, including the attributes they assign,
 the scores they compute during training and whether any required attributes
@@ -257,7 +256,7 @@ The following methods, attributes and commands are new in spaCy v3.0.
 | [`Language.select_pipes`](/api/language#select_pipes)                                                                           | Context manager for enabling or disabling specific pipeline components for a block.                                                                                                              |
 | [`Language.disable_pipe`](/api/language#disable_pipe), [`Language.enable_pipe`](/api/language#enable_pipe)                      | Disable or enable a loaded pipeline component (but don't remove it).                                                                                                                             |
 | [`Language.analyze_pipes`](/api/language#analyze_pipes)                                                                         | [Analyze](/usage/processing-pipelines#analysis) components and their interdependencies.                                                                                                          |
-| [`Language.resume_training`](/api/language#resume_training)                                                                     | Experimental: continue training a pretrained model and initialize "rehearsal" for components that implement a `rehearse` method to prevent catastrophic forgetting.                              |
+| [`Language.resume_training`](/api/language#resume_training)                                                                     | Experimental: continue training a trained pipeline and initialize "rehearsal" for components that implement a `rehearse` method to prevent catastrophic forgetting.                              |
 | [`@Language.factory`](/api/language#factory), [`@Language.component`](/api/language#component)                                  | Decorators for [registering](/usage/processing-pipelines#custom-components) pipeline component factories and simple stateless component functions.                                               |
 | [`Language.has_factory`](/api/language#has_factory)                                                                             | Check whether a component factory is registered on a language class.s                                                                                                                            |
 | [`Language.get_factory_meta`](/api/language#get_factory_meta), [`Language.get_pipe_meta`](/api/language#get_factory_meta)       | Get the [`FactoryMeta`](/api/language#factorymeta) with component metadata for a factory or instance name.                                                                                       |
@@ -266,8 +265,8 @@ The following methods, attributes and commands are new in spaCy v3.0.
 | [`Language.disabled`](/api/language#attributes)                                                                                 | Names of disabled components that are not run as part of the pipeline.                                                                                                                           |
 | [`Pipe.score`](/api/pipe#score)                                                                                                 | Method on pipeline components that returns a dictionary of evaluation scores.                                                                                                                    |
 | [`registry`](/api/top-level#registry)                                                                                           | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config).                                                                                  |
-| [`util.load_meta`](/api/top-level#util.load_meta), [`util.load_config`](/api/top-level#util.load_config)                        | Updated helpers for loading a model's [`meta.json`](/api/data-formats#meta) and [`config.cfg`](/api/data-formats#config).                                                                        |
-| [`util.get_installed_models`](/api/top-level#util.get_installed_models)                                                         | Names of all models installed in the environment.                                                                                                                                                |
+| [`util.load_meta`](/api/top-level#util.load_meta), [`util.load_config`](/api/top-level#util.load_config)                        | Updated helpers for loading a pipeline's [`meta.json`](/api/data-formats#meta) and [`config.cfg`](/api/data-formats#config).                                                                     |
+| [`util.get_installed_models`](/api/top-level#util.get_installed_models)                                                         | Names of all pipeline packages installed in the environment.                                                                                                                                     |
 | [`init config`](/api/cli#init-config), [`init fill-config`](/api/cli#init-fill-config), [`debug config`](/api/cli#debug-config) | CLI commands for initializing, auto-filling and debugging [training configs](/usage/training).                                                                                                   |
 | [`project`](/api/cli#project)                                                                                                   | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects).                                                                                                       |
 
@@ -280,9 +279,9 @@ The following methods, attributes and commands are new in spaCy v3.0.
 To help you get started with spaCy v3.0 and the new features, we've added
 several new or rewritten documentation pages, including a new usage guide on
 [embeddings, transformers and transfer learning](/usage/embeddings-transformers),
-a guide on [training models](/usage/training) rewritten from scratch, a page
-explaining the new [spaCy projects](/usage/projects) and updated usage
-documentation on
+a guide on [training pipelines and models](/usage/training) rewritten from
+scratch, a page explaining the new [spaCy projects](/usage/projects) and updated
+usage documentation on
 [custom pipeline components](/usage/processing-pipelines#custom-components).
 We've also added a bunch of new illustrations and new API reference pages
 documenting spaCy's machine learning [model architectures](/api/architectures)
@@ -335,15 +334,15 @@ Note that spaCy v3.0 now requires **Python 3.6+**.
 
 ### API changes {#incompat-api}
 
-- Model symlinks, the `link` command and shortcut names are now deprecated.
-  There can be many [different models](/models) and not just one "English
-  model", so you should always use the full model name like
+- Pipeline package symlinks, the `link` command and shortcut names are now
+  deprecated. There can be many [different trained pipelines](/models) and not
+  just one "English model", so you should always use the full package name like
   [`en_core_web_sm`](/models/en) explicitly.
-- A model's [`meta.json`](/api/data-formats#meta) is now only used to provide
-  meta information like the model name, author, license and labels. It's **not**
-  used to construct the processing pipeline anymore. This is all defined in the
-  [`config.cfg`](/api/data-formats#config), which also includes all settings
-  used to train the model.
+- A pipeline's [`meta.json`](/api/data-formats#meta) is now only used to provide
+  meta information like the package name, author, license and labels. It's
+  **not** used to construct the processing pipeline anymore. This is all defined
+  in the [`config.cfg`](/api/data-formats#config), which also includes all
+  settings used to train the pipeline.
 - The [`train`](/api/cli#train) and [`pretrain`](/api/cli#pretrain) commands now
   only take a `config.cfg` file containing the full
   [training config](/usage/training#config).
@@ -389,10 +388,11 @@ Note that spaCy v3.0 now requires **Python 3.6+**.
 | `GoldParse`                                              | [`Example`](/api/example)                                                                                    |
 | `GoldCorpus`                                             | [`Corpus`](/api/corpus)                                                                                      |
 | `KnowledgeBase.load_bulk`, `KnowledgeBase.dump`          | [`KnowledgeBase.from_disk`](/api/kb#from_disk), [`KnowledgeBase.to_disk`](/api/kb#to_disk)                   |
-| `spacy init-model`                                       | [`spacy init model`](/api/cli#init-model)                                                                    |
+| `Matcher.pipe`, `PhraseMatcher.pipe`                     | not needed                                                                                                   |
+| `spacy init-model`                                       | [`spacy init vocab`](/api/cli#init-vocab)                                                                    |
 | `spacy debug-data`                                       | [`spacy debug data`](/api/cli#debug-data)                                                                    |
 | `spacy profile`                                          | [`spacy debug profile`](/api/cli#debug-profile)                                                              |
-| `spacy link`, `util.set_data_path`, `util.get_data_path` | not needed, model symlinks are deprecated                                                                    |
+| `spacy link`, `util.set_data_path`, `util.get_data_path` | not needed, symlinks are deprecated                                                                          |
 
 The following deprecated methods, attributes and arguments were removed in v3.0.
 Most of them have been **deprecated for a while** and many would previously
@@ -413,12 +413,13 @@ on them.
 
 ## Migrating from v2.x {#migrating}
 
-### Downloading and loading models {#migrating-downloading-models}
+### Downloading and loading trained pipelines {#migrating-downloading-models}
 
-Model symlinks and shortcuts like `en` are now officially deprecated. There are
-[many different models](/models) with different capabilities and not just one
-"English model". In order to download and load a model, you should always use
-its full name – for instance, [`en_core_web_sm`](/models/en#en_core_web_sm).
+Symlinks and shortcuts like `en` are now officially deprecated. There are
+[many different trained pipelines](/models) with different capabilities and not
+just one "English model". In order to download and load a package, you should
+always use its full name – for instance,
+[`en_core_web_sm`](/models/en#en_core_web_sm).
 
 ```diff
 - python -m spacy download en
@@ -521,12 +522,12 @@ and you typically shouldn't have to use it in your code.
 + parser = nlp.add_pipe("parser")
 ```
 
-If you need to add a component from an existing pretrained model, you can now
+If you need to add a component from an existing trained pipeline, you can now
 use the `source` argument on [`nlp.add_pipe`](/api/language#add_pipe). This will
 check that the component is compatible, and take care of porting over all
-config. During training, you can also reference existing pretrained components
-in your [config](/usage/training#config-components) and decide whether or not
-they should be updated with more data.
+config. During training, you can also reference existing trained components in
+your [config](/usage/training#config-components) and decide whether or not they
+should be updated with more data.
 
 > #### config.cfg (excerpt)
 >
@@ -566,6 +567,20 @@ patterns = [nlp("health care reform"), nlp("healthcare reform")]
 + matcher.add("HEALTH", patterns, on_match=on_match)
 ```
 
+### Migrating attributes in tokenizer exceptions {#migrating-tokenizer-exceptions}
+
+Tokenizer exceptions are now only allowed to set `ORTH` and `NORM` values as
+part of the token attributes. Exceptions for other attributes such as `TAG` and
+`LEMMA` should be moved to an [`AttributeRuler`](/api/attributeruler) component:
+
+```diff
+nlp = spacy.blank("en")
+- nlp.tokenizer.add_special_case("don't", [{"ORTH": "do"}, {"ORTH": "n't", "LEMMA": "not"}])
++ nlp.tokenizer.add_special_case("don't", [{"ORTH": "do"}, {"ORTH": "n't"}])
++ ruler = nlp.add_pipe("attribute_ruler")
++ ruler.add(patterns=[[{"ORTH": "n't"}]], attrs={"LEMMA": "not"})
+```
+
 ### Migrating tag maps and morph rules {#migrating-training-mappings-exceptions}
 
 Instead of defining a `tag_map` and `morph_rules` in the language data, spaCy
@@ -584,13 +599,13 @@ nlp = spacy.blank("en")
 + ruler.load_from_tag_map(YOUR_TAG_MAP)
 ```
 
-### Training models {#migrating-training}
+### Training pipelines and models {#migrating-training}
 
-To train your models, you should now pretty much always use the
+To train your pipelines, you should now pretty much always use the
 [`spacy train`](/api/cli#train) CLI. You shouldn't have to put together your own
 training scripts anymore, unless you _really_ want to. The training commands now
 use a [flexible config file](/usage/training#config) that describes all training
-settings and hyperparameters, as well as your pipeline, model components and
+settings and hyperparameters, as well as your pipeline, components and
 architectures to use. The `--code` argument lets you pass in code containing
 [custom registered functions](/usage/training#custom-code) that you can
 reference in your config. To get started, check out the
@@ -601,7 +616,7 @@ reference in your config. To get started, check out the
 spaCy v3.0 uses a new
 [binary training data format](/api/data-formats#binary-training) created by
 serializing a [`DocBin`](/api/docbin), which represents a collection of `Doc`
-objects. This means that you can train spaCy models using the same format it
+objects. This means that you can train spaCy pipelines using the same format it
 outputs: annotated `Doc` objects. The binary format is extremely **efficient in
 storage**, especially when packing multiple documents together. You can convert
 your existing JSON-formatted data using the [`spacy convert`](/api/cli#convert)
@@ -640,7 +655,7 @@ values. You can then use the auto-generated `config.cfg` for training:
 
 The easiest way to get started with an end-to-end training process is to clone a
 [project](/usage/projects) template. Projects let you manage multi-step
-workflows, from data preprocessing to training and packaging your model.
+workflows, from data preprocessing to training and packaging your pipeline.
 
 </Project>
 
@@ -713,7 +728,7 @@ setting up the label scheme.
 + nlp.begin_training(lambda: examples)
 ```
 
-#### Packaging models {#migrating-training-packaging}
+#### Packaging trained pipelines {#migrating-training-packaging}
 
 The [`spacy package`](/api/cli#package) command now automatically builds the
 installable `.tar.gz` sdist of the Python package, so you don't have to run this
@@ -721,8 +736,8 @@ step manually anymore. You can disable the behavior by setting the `--no-sdist`
 flag.
 
 ```diff
-python -m spacy package ./model ./packages
-- cd /output/en_model-0.0.0
+python -m spacy package ./output ./packages
+- cd /output/en_pipeline-0.0.0
 - python setup.py sdist
 ```
 
diff --git a/website/docs/usage/visualizers.md b/website/docs/usage/visualizers.md
index 4ba0112b6..6754cabab 100644
--- a/website/docs/usage/visualizers.md
+++ b/website/docs/usage/visualizers.md
@@ -23,10 +23,10 @@ The quickest way to visualize `Doc` is to use
 [`displacy.serve`](/api/top-level#displacy.serve). This will spin up a simple
 web server and let you view the result straight from your browser. displaCy can
 either take a single `Doc` or a list of `Doc` objects as its first argument.
-This lets you construct them however you like – using any model or modifications
-you like. If you're using [Streamlit](https://streamlit.io), check out the
-[`spacy-streamlit`](https://github.com/explosion/spacy-streamlit) package that
-helps you integrate spaCy visualizations into your apps!
+This lets you construct them however you like – using any pipeline or
+modifications you like. If you're using [Streamlit](https://streamlit.io), check
+out the [`spacy-streamlit`](https://github.com/explosion/spacy-streamlit)
+package that helps you integrate spaCy visualizations into your apps!
 
 ## Visualizing the dependency parse {#dep}
 
@@ -131,8 +131,8 @@ example, you can choose to display `PERSON` entities. Internally, the visualizer
 knows nothing about available entity types and will render whichever spans and
 labels it receives. This makes it especially easy to work with custom entity
 types. By default, displaCy comes with colors for all entity types used by
-[spaCy models](/models). If you're using custom entity types, you can use the
-`colors` setting to add your own colors for them.
+[trained spaCy pipelines](/models). If you're using custom entity types, you can
+use the `colors` setting to add your own colors for them.
 
 > #### Options example
 >
@@ -176,7 +176,7 @@ visualizations will be included as HTML.
 
 ```python
 ### Jupyter example
-# Don't forget to install a model, e.g.: python -m spacy download en
+# Don't forget to install a trained pipeline, e.g.: python -m spacy download en
 
 # In[1]:
 import spacy
diff --git a/website/gatsby-config.js b/website/gatsby-config.js
index 752628749..6c67de6ea 100644
--- a/website/gatsby-config.js
+++ b/website/gatsby-config.js
@@ -140,15 +140,6 @@ module.exports = {
                 icon: favicon,
             },
         },
-        {
-            resolve: `gatsby-plugin-google-analytics`,
-            options: {
-                trackingId: site.analytics,
-                head: false,
-                anonymize: true,
-                respectDNT: true,
-            },
-        },
         {
             resolve: `gatsby-plugin-plausible`,
             options: { domain },
diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json
index 94fbc2492..76d5e63d6 100644
--- a/website/meta/sidebars.json
+++ b/website/meta/sidebars.json
@@ -50,11 +50,11 @@
                 "items": [{ "text": "Overview", "url": "/models" }]
             },
             {
-                "label": "Core Models",
+                "label": "Trained Pipelines",
                 "items": []
             },
             {
-                "label": "Starter Models",
+                "label": "Starter Packages",
                 "items": []
             }
         ]
diff --git a/website/meta/site.json b/website/meta/site.json
index 7e6f4c692..d1162edf9 100644
--- a/website/meta/site.json
+++ b/website/meta/site.json
@@ -17,7 +17,6 @@
         "github": "explosion"
     },
     "theme": "#09a3d5",
-    "analytics": "UA-58931649-1",
     "newsletter": {
         "user": "spacy.us12",
         "id": "83b0498b1e7fa3c91ce68c3f1",
diff --git a/website/package.json b/website/package.json
index 0ba7159fe..40018f532 100644
--- a/website/package.json
+++ b/website/package.json
@@ -20,7 +20,6 @@
         "gatsby-image": "^2.0.29",
         "gatsby-mdx": "^0.3.6",
         "gatsby-plugin-catch-links": "^2.0.11",
-        "gatsby-plugin-google-analytics": "^2.0.14",
         "gatsby-plugin-manifest": "^2.0.17",
         "gatsby-plugin-offline": "^2.0.24",
         "gatsby-plugin-plausible": "0.0.6",
diff --git a/website/src/components/tag.js b/website/src/components/tag.js
index c7ef20919..3f2b4e994 100644
--- a/website/src/components/tag.js
+++ b/website/src/components/tag.js
@@ -23,7 +23,7 @@ export default function Tag({ spaced = false, variant, tooltip, children }) {
         )
     }
     if (variant === 'model') {
-        const tooltipText = `To use this functionality, spaCy needs a model to be installed that supports the following capabilities: ${children}`
+        const tooltipText = `To use this functionality, spaCy needs a trained pipeline that supports the following capabilities: ${children}`
         return (
             <TagTemplate spaced={spaced} tooltip={tooltipText}>
                 Needs model
diff --git a/website/src/templates/models.js b/website/src/templates/models.js
index 3c5e9d2a4..1bcdd4859 100644
--- a/website/src/templates/models.js
+++ b/website/src/templates/models.js
@@ -37,14 +37,15 @@ const MODEL_META = {
     ents_r: 'Entities (recall)',
     cpu: 'words per second on CPU',
     gpu: 'words per second on GPU',
-    pipeline: 'Processing pipeline components in order',
+    pipeline: 'Active processing pipeline components in order',
+    components: 'All processing pipeline components (including disabled components)',
     sources: 'Sources of training data',
     vecs:
-        'Word vectors included in the model. Models that only support context vectors compute similarity via the tensors shared with the pipeline.',
+        'Word vectors included in the package. Packages that only support context vectors compute similarity via the tensors shared with the pipeline.',
     benchmark_parser: 'Syntax accuracy',
     benchmark_ner: 'NER accuracy',
     benchmark_speed: 'Speed',
-    compat: 'Latest compatible model version for your spaCy installation',
+    compat: 'Latest compatible package version for your spaCy installation',
 }
 
 const LABEL_SCHEME_META = {
@@ -178,6 +179,7 @@ const Model = ({ name, langId, langName, baseUrl, repo, compatibility, hasExampl
         { label: 'Type', tag: type, content: MODEL_META[type] },
         { label: 'Genre', tag: genre, content: MODEL_META[genre] },
         { label: 'Size', tag: size, content: meta.sizeFull },
+        { label: 'Components', content: components, help: MODEL_META.components },
         { label: 'Pipeline', content: pipeline, help: MODEL_META.pipeline },
         { label: 'Vectors', content: meta.vectors, help: MODEL_META.vecs },
         { label: 'Sources', content: sources, help: MODEL_META.sources },
@@ -355,7 +357,7 @@ const Models = ({ pageContext, repo, children }) => {
     }, [initialized, baseUrl])
 
     const modelTitle = title
-    const modelTeaser = `Available pretrained statistical models for ${title}`
+    const modelTeaser = `Available trained pipelines for ${title}`
 
     const starterTitle = `${title} starters`
     const starterTeaser = `Available transfer learning starter packs for ${title}`
diff --git a/website/src/widgets/quickstart-install.js b/website/src/widgets/quickstart-install.js
index 9d993c969..d6f7960d0 100644
--- a/website/src/widgets/quickstart-install.js
+++ b/website/src/widgets/quickstart-install.js
@@ -43,7 +43,7 @@ const DATA = [
             {
                 id: 'transformers',
                 title: 'Transformers',
-                help: 'Use transformers like BERT to train your spaCy models',
+                help: 'Use transformers like BERT to train your spaCy pipelines',
             },
             {
                 id: 'lookups',
@@ -63,7 +63,7 @@ const QuickstartInstall = ({ id, title }) => (
                 ...DATA,
                 {
                     id: 'models',
-                    title: 'Models',
+                    title: 'Trained Pipelines',
                     multiple: true,
                     options: models.map(({ code, name }) => ({ id: code, title: name })),
                 },
diff --git a/website/src/widgets/quickstart-models.js b/website/src/widgets/quickstart-models.js
index f7505cefa..d19ff9e52 100644
--- a/website/src/widgets/quickstart-models.js
+++ b/website/src/widgets/quickstart-models.js
@@ -15,13 +15,13 @@ const data = [
             {
                 id: 'spacy',
                 title: 'Use spacy.load()',
-                help: "Use spaCy's built-in loader to load the model by name.",
+                help: "Use spaCy's built-in loader to load the package by name",
                 checked: true,
             },
             {
                 id: 'module',
                 title: 'Import as module',
-                help: 'Import the model explicitly as a Python module.',
+                help: 'Import the package explicitly as a Python module',
             },
         ],
     },