Merge pull request #13107 from adrianeboyd/chore/update-develop-from-master-v3.8-1

Update develop from master for v3.8

.github/workflows/tests.yml

@@ -58,7 +58,7 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python_version: ["3.11", "3.12.0-rc.2"]
+        python_version: ["3.12"]
         include:
           - os: windows-latest
             python_version: "3.7"
@@ -68,6 +68,8 @@ jobs:
             python_version: "3.9"
           - os: windows-latest
             python_version: "3.10"
+          - os: macos-latest
+            python_version: "3.11"
 
     runs-on: ${{ matrix.os }}
 
@@ -115,22 +117,22 @@ jobs:
       - name: Test import
         run: python -W error -c "import spacy"
 
-#      - name: "Test download CLI"
+      - name: "Test download CLI"
-#        run: |
+        run: |
-#          python -m spacy download ca_core_news_sm
+          python -m spacy download ca_core_news_sm
-#          python -m spacy download ca_core_news_md
+          python -m spacy download ca_core_news_md
-#          python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
+          python -c "import spacy; nlp=spacy.load('ca_core_news_sm'); doc=nlp('test')"
-#        if: matrix.python_version == '3.9'
+        if: matrix.python_version == '3.9'
-#
+
-#      - name: "Test download_url in info CLI"
+      - name: "Test download_url in info CLI"
-#        run: |
+        run: |
-#          python -W error -m spacy info ca_core_news_sm | grep -q download_url
+          python -W error -m spacy info ca_core_news_sm | grep -q download_url
-#        if: matrix.python_version == '3.9'
+        if: matrix.python_version == '3.9'
-#
+
-#      - name: "Test no warnings on load (#11713)"
+      - name: "Test no warnings on load (#11713)"
-#        run: |
+        run: |
-#          python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
+          python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
-#        if: matrix.python_version == '3.9'
+        if: matrix.python_version == '3.9'
 
       - name: "Test convert CLI"
         run: |
@@ -154,17 +156,17 @@ jobs:
           python -m spacy train ner.cfg --paths.train ner-token-per-line-conll2003.spacy --paths.dev ner-token-per-line-conll2003.spacy --training.max_steps 10 --gpu-id -1
         if: matrix.python_version == '3.9'
 
-#      - name: "Test assemble CLI"
+      - name: "Test assemble CLI"
-#        run: |
+        run: |
-#          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
+          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_sm'}; config.to_disk('ner_source_sm.cfg')"
-#          PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
+          PYTHONWARNINGS="error,ignore::DeprecationWarning" python -m spacy assemble ner_source_sm.cfg output_dir
-#        if: matrix.python_version == '3.9'
+        if: matrix.python_version == '3.9'
-#
+
-#      - name: "Test assemble CLI vectors warning"
+      - name: "Test assemble CLI vectors warning"
-#        run: |
+        run: |
-#          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
+          python -c "import spacy; config = spacy.util.load_config('ner.cfg'); config['components']['ner'] = {'source': 'ca_core_news_md'}; config.to_disk('ner_source_md.cfg')"
-#          python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
+          python -m spacy assemble ner_source_md.cfg output_dir 2>&1 | grep -q W113
-#        if: matrix.python_version == '3.9'
+        if: matrix.python_version == '3.9'
 
       - name: "Install test requirements"
         run: |

LICENSE

@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (C) 2016-2022 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
+Copyright (C) 2016-2023 ExplosionAI GmbH, 2016 spaCy GmbH, 2015 Matthew Honnibal
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -16,7 +16,7 @@ model packaging, deployment and workflow management. spaCy is commercial
 open-source software, released under the
 [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
 
-💫 **Version 3.6 out now!**
+💫 **Version 3.7 out now!**
 [Check out the release notes here.](https://github.com/explosion/spaCy/releases)
 
 [![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml)

requirements.txt

@@ -10,7 +10,6 @@ wasabi>=0.9.1,<1.2.0
 srsly>=2.4.3,<3.0.0
 catalogue>=2.0.6,<2.1.0
 typer>=0.3.0,<0.10.0
-pathy>=0.10.0
 smart-open>=5.2.1,<7.0.0
 weasel>=0.1.0,<0.4.0
 # Third party dependencies

setup.cfg

@@ -56,7 +56,6 @@ install_requires =
     weasel>=0.1.0,<0.4.0
     # Third-party dependencies
     typer>=0.3.0,<0.10.0
-    pathy>=0.10.0
     smart-open>=5.2.1,<7.0.0
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0; python_version < "3.9"

spacy/__init__.py

@@ -13,6 +13,7 @@ from thinc.api import Config, prefer_gpu, require_cpu, require_gpu  # noqa: F401
 from . import pipeline  # noqa: F401
 from . import util
 from .about import __version__  # noqa: F401
+from .cli.info import info  # noqa: F401
 from .errors import Errors
 from .glossary import explain  # noqa: F401
 from .language import Language
@@ -76,9 +77,3 @@ def blank(
     # We should accept both dot notation and nested dict here for consistency
     config = util.dot_to_dict(config)
     return LangClass.from_config(config, vocab=vocab, meta=meta)
-
-
-def info(*args, **kwargs):
-    from .cli.info import info as cli_info
-
-    return cli_info(*args, **kwargs)

spacy/about.py

@@ -1,5 +1,5 @@
 # fmt: off
 __title__ = "spacy"
-__version__ = "3.7.0"
+__version__ = "3.7.2"
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

spacy/cli/__init__.py

@@ -22,8 +22,17 @@ from .init_pipeline import init_pipeline_cli  # noqa: F401
 from .package import package  # noqa: F401
 from .pretrain import pretrain  # noqa: F401
 from .profile import profile  # noqa: F401
-from .train import train_cli  # noqa: F401
+from .project.assets import project_assets  # type: ignore[attr-defined]  # noqa: F401
-from .validate import validate  # noqa: F401
+from .project.clone import project_clone  # type: ignore[attr-defined]  # noqa: F401
+from .project.document import (  # type: ignore[attr-defined]  # noqa: F401
+    project_document,
+)
+from .project.dvc import project_update_dvc  # type: ignore[attr-defined]  # noqa: F401
+from .project.pull import project_pull  # type: ignore[attr-defined]  # noqa: F401
+from .project.push import project_push  # type: ignore[attr-defined]  # noqa: F401
+from .project.run import project_run  # type: ignore[attr-defined]  # noqa: F401
+from .train import train_cli  # type: ignore[attr-defined]  # noqa: F401
+from .validate import validate  # type: ignore[attr-defined]  # noqa: F401
 
 
 @app.command("link", no_args_is_help=True, deprecated=True, hidden=True)

spacy/cli/_util.py

@@ -41,10 +41,6 @@ from ..util import (
     run_command,
 )
 
-if TYPE_CHECKING:
-    from pathy import FluidPath  # noqa: F401
-
-
 SDIST_SUFFIX = ".tar.gz"
 WHEEL_SUFFIX = "-py3-none-any.whl"
 

spacy/cli/project/assets.py

@@ -0,0 +1 @@
+from weasel.cli.assets import *

spacy/cli/project/clone.py

@@ -0,0 +1 @@
+from weasel.cli.clone import *

spacy/cli/project/document.py

@@ -0,0 +1 @@
+from weasel.cli.document import *

spacy/cli/project/dvc.py

@@ -0,0 +1 @@
+from weasel.cli.dvc import *

spacy/cli/project/pull.py

@@ -0,0 +1 @@
+from weasel.cli.pull import *

spacy/cli/project/push.py

@@ -0,0 +1 @@
+from weasel.cli.push import *

spacy/cli/project/remote_storage.py

@@ -0,0 +1 @@
+from weasel.cli.remote_storage import *

spacy/cli/project/run.py

@@ -0,0 +1 @@
+from weasel.cli.run import *

spacy/displacy/render.py

@@ -142,7 +142,25 @@ class SpanRenderer:
         spans (list): Individual entity spans and their start, end, label, kb_id and kb_url.
         title (str / None): Document title set in Doc.user_data['title'].
         """
-        per_token_info = []
+        per_token_info = self._assemble_per_token_info(tokens, spans)
+        markup = self._render_markup(per_token_info)
+        markup = TPL_SPANS.format(content=markup, dir=self.direction)
+        if title:
+            markup = TPL_TITLE.format(title=title) + markup
+        return markup
+
+    @staticmethod
+    def _assemble_per_token_info(
+        tokens: List[str], spans: List[Dict[str, Any]]
+    ) -> List[Dict[str, List[Dict[str, Any]]]]:
+        """Assembles token info used to generate markup in render_spans().
+        tokens (List[str]): Tokens in text.
+        spans (List[Dict[str, Any]]): Spans in text.
+        RETURNS (List[Dict[str, List[Dict, str, Any]]]): Per token info needed to render HTML markup for given tokens
+            and spans.
+        """
+        per_token_info: List[Dict[str, List[Dict[str, Any]]]] = []
+
         # we must sort so that we can correctly describe when spans need to "stack"
         # which is determined by their start token, then span length (longer spans on top),
         # then break any remaining ties with the span label
@@ -154,21 +172,22 @@ class SpanRenderer:
                 s["label"],
             ),
         )
+
         for s in spans:
             # this is the vertical 'slot' that the span will be rendered in
             # vertical_position = span_label_offset + (offset_step * (slot - 1))
             s["render_slot"] = 0
+
         for idx, token in enumerate(tokens):
             # Identify if a token belongs to a Span (and which) and if it's a
             # start token of said Span. We'll use this for the final HTML render
             token_markup: Dict[str, Any] = {}
             token_markup["text"] = token
-            concurrent_spans = 0
+            intersecting_spans: List[Dict[str, Any]] = []
             entities = []
             for span in spans:
                 ent = {}
                 if span["start_token"] <= idx < span["end_token"]:
-                    concurrent_spans += 1
                     span_start = idx == span["start_token"]
                     ent["label"] = span["label"]
                     ent["is_start"] = span_start
@@ -176,7 +195,12 @@ class SpanRenderer:
                         # When the span starts, we need to know how many other
                         # spans are on the 'span stack' and will be rendered.
                         # This value becomes the vertical render slot for this entire span
-                        span["render_slot"] = concurrent_spans
+                        span["render_slot"] = (
+                            intersecting_spans[-1]["render_slot"]
+                            if len(intersecting_spans)
+                            else 0
+                        ) + 1
+                    intersecting_spans.append(span)
                     ent["render_slot"] = span["render_slot"]
                     kb_id = span.get("kb_id", "")
                     kb_url = span.get("kb_url", "#")
@@ -193,11 +217,8 @@ class SpanRenderer:
                     span["render_slot"] = 0
             token_markup["entities"] = entities
             per_token_info.append(token_markup)
-        markup = self._render_markup(per_token_info)
+
-        markup = TPL_SPANS.format(content=markup, dir=self.direction)
+        return per_token_info
-        if title:
-            markup = TPL_TITLE.format(title=title) + markup
-        return markup
 
     def _render_markup(self, per_token_info: List[Dict[str, Any]]) -> str:
         """Render the markup from per-token information"""

spacy/kb/__init__.py

@@ -1,3 +1,11 @@
 from .candidate import Candidate, get_candidates, get_candidates_batch
 from .kb import KnowledgeBase
 from .kb_in_memory import InMemoryLookupKB
+
+__all__ = [
+    "Candidate",
+    "KnowledgeBase",
+    "InMemoryLookupKB",
+    "get_candidates",
+    "get_candidates_batch",
+]

spacy/matcher/__init__.py

@@ -3,4 +3,4 @@ from .levenshtein import levenshtein
 from .matcher import Matcher
 from .phrasematcher import PhraseMatcher
 
-__all__ = ["Matcher", "PhraseMatcher", "DependencyMatcher", "levenshtein"]
+__all__ = ["DependencyMatcher", "Matcher", "PhraseMatcher", "levenshtein"]

spacy/pipeline/__init__.py

@@ -22,6 +22,7 @@ from .trainable_pipe import TrainablePipe
 __all__ = [
     "AttributeRuler",
     "DependencyParser",
+    "EditTreeLemmatizer",
     "EntityLinker",
     "EntityRecognizer",
     "EntityRuler",

spacy/tests/doc/test_span.py

@@ -731,3 +731,12 @@ def test_for_no_ent_sents():
     sents = list(doc.ents[0].sents)
     assert len(sents) == 1
     assert str(sents[0]) == str(doc.ents[0].sent) == "ENTITY"
+
+
+def test_span_api_richcmp_other(en_tokenizer):
+    doc1 = en_tokenizer("a b")
+    doc2 = en_tokenizer("b c")
+    assert not doc1[1:2] == doc1[1]
+    assert not doc1[1:2] == doc2[0]
+    assert not doc1[1:2] == doc2[0:1]
+    assert not doc1[0:1] == doc2

spacy/tests/doc/test_token_api.py

@@ -294,3 +294,12 @@ def test_missing_head_dep(en_vocab):
     assert aligned_heads[0] == ref_heads[0]
     assert aligned_deps[5] == ref_deps[5]
     assert aligned_heads[5] == ref_heads[5]
+
+
+def test_token_api_richcmp_other(en_tokenizer):
+    doc1 = en_tokenizer("a b")
+    doc2 = en_tokenizer("b c")
+    assert not doc1[1] == doc1[0:1]
+    assert not doc1[1] == doc2[1:2]
+    assert not doc1[1] == doc2[0]
+    assert not doc1[0] == doc2

spacy/tests/test_cli.py

@@ -12,7 +12,6 @@ from thinc.api import Config
 
 import spacy
 from spacy import about
-from spacy import info as spacy_info
 from spacy.cli import info
 from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory
 from spacy.cli.apply import apply
@@ -193,9 +192,6 @@ def test_cli_info():
         raw_data = info(tmp_dir, exclude=[""])
         assert raw_data["lang"] == "nl"
         assert raw_data["components"] == ["textcat"]
-        raw_data = spacy_info(tmp_dir, exclude=[""])
-        assert raw_data["lang"] == "nl"
-        assert raw_data["components"] == ["textcat"]
 
 
 def test_cli_converters_conllu_to_docs():
@@ -538,7 +534,6 @@ def test_string_to_list_intify(value):
     assert string_to_list(value, intify=True) == [1, 2, 3]
 
 
-@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published")
 def test_download_compatibility():
     spec = SpecifierSet("==" + about.__version__)
     spec.prereleases = False
@@ -549,7 +544,6 @@ def test_download_compatibility():
         assert get_minor_version(about.__version__) == get_minor_version(version)
 
 
-@pytest.mark.skip(reason="Temporarily skip before 3.7 models are published")
 def test_validate_compatibility_table():
     spec = SpecifierSet("==" + about.__version__)
     spec.prereleases = False
@@ -1067,3 +1061,8 @@ def test_debug_data_trainable_lemmatizer_not_annotated():
 
     data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
     assert data["no_lemma_annotations"] == 2
+
+
+def test_project_api_imports():
+    from spacy.cli import project_run
+    from spacy.cli.project.run import project_run  # noqa: F401, F811

spacy/tests/test_displacy.py

@@ -2,7 +2,7 @@ import numpy
 import pytest
 
 from spacy import displacy
-from spacy.displacy.render import DependencyRenderer, EntityRenderer
+from spacy.displacy.render import DependencyRenderer, EntityRenderer, SpanRenderer
 from spacy.lang.en import English
 from spacy.lang.fa import Persian
 from spacy.tokens import Doc, Span
@@ -468,3 +468,23 @@ def test_issue12816(en_vocab) -> None:
     # Verify that the HTML tag is still escaped
     html = displacy.render(doc, style="span")
     assert "<TEST>" in html
+
+
+@pytest.mark.issue(13056)
+def test_displacy_span_stacking():
+    """Test whether span stacking works properly for multiple overlapping spans."""
+    spans = [
+        {"start_token": 2, "end_token": 5, "label": "SkillNC"},
+        {"start_token": 0, "end_token": 2, "label": "Skill"},
+        {"start_token": 1, "end_token": 3, "label": "Skill"},
+    ]
+    tokens = ["Welcome", "to", "the", "Bank", "of", "China", "."]
+    per_token_info = SpanRenderer._assemble_per_token_info(spans=spans, tokens=tokens)
+
+    assert len(per_token_info) == len(tokens)
+    assert all([len(per_token_info[i]["entities"]) == 1 for i in (0, 3, 4)])
+    assert all([len(per_token_info[i]["entities"]) == 2 for i in (1, 2)])
+    assert per_token_info[1]["entities"][0]["render_slot"] == 1
+    assert per_token_info[1]["entities"][1]["render_slot"] == 2
+    assert per_token_info[2]["entities"][0]["render_slot"] == 2
+    assert per_token_info[2]["entities"][1]["render_slot"] == 3

spacy/tokens/__init__.py

@@ -5,4 +5,4 @@ from .span import Span
 from .span_group import SpanGroup
 from .token import Token
 
-__all__ = ["Doc", "Token", "Span", "SpanGroup", "DocBin", "MorphAnalysis"]
+__all__ = ["Doc", "DocBin", "MorphAnalysis", "Span", "SpanGroup", "Token"]

spacy/tokens/span.pyx

@@ -127,14 +127,17 @@ cdef class Span:
         self._vector = vector
         self._vector_norm = vector_norm
 
-    def __richcmp__(self, Span other, int op):
+    def __richcmp__(self, object other, int op):
         if other is None:
             if op == 0 or op == 1 or op == 2:
                 return False
             else:
                 return True
+        if not isinstance(other, Span):
+            return False
+        cdef Span other_span = other
         self_tuple = (self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id, self.id, self.doc)
-        other_tuple = (other.c.start_char, other.c.end_char, other.c.label, other.c.kb_id, other.id, other.doc)
+        other_tuple = (other_span.c.start_char, other_span.c.end_char, other_span.c.label, other_span.c.kb_id, other_span.id, other_span.doc)
         # <
         if op == 0:
             return self_tuple < other_tuple

spacy/tokens/token.pyi

@@ -53,7 +53,12 @@ class Token:
     def __bytes__(self) -> bytes: ...
     def __str__(self) -> str: ...
     def __repr__(self) -> str: ...
-    def __richcmp__(self, other: Token, op: int) -> bool: ...
+    def __lt__(self, other: Any) -> bool: ...
+    def __le__(self, other: Any) -> bool: ...
+    def __eq__(self, other: Any) -> bool: ...
+    def __ne__(self, other: Any) -> bool: ...
+    def __gt__(self, other: Any) -> bool: ...
+    def __ge__(self, other: Any) -> bool: ...
     @property
     def _(self) -> Underscore: ...
     def nbor(self, i: int = ...) -> Token: ...

spacy/tokens/token.pyx

@@ -139,17 +139,20 @@ cdef class Token:
     def __repr__(self):
         return self.__str__()
 
-    def __richcmp__(self, Token other, int op):
+    def __richcmp__(self, object other, int op):
         # http://cython.readthedocs.io/en/latest/src/userguide/special_methods.html
         if other is None:
             if op in (0, 1, 2):
                 return False
             else:
                 return True
+        if not isinstance(other, Token):
+            return False
+        cdef Token other_token = other
         cdef Doc my_doc = self.doc
-        cdef Doc other_doc = other.doc
+        cdef Doc other_doc = other_token.doc
         my = self.idx
-        their = other.idx
+        their = other_token.idx
         if op == 0:
             return my < their
         elif op == 2:

spacy/training/__init__.py

@@ -16,3 +16,28 @@ from .iob_utils import (  # noqa: F401
     tags_to_entities,
 )
 from .loggers import console_logger  # noqa: F401
+
+__all__ = [
+    "Alignment",
+    "Corpus",
+    "Example",
+    "JsonlCorpus",
+    "PlainTextCorpus",
+    "biluo_tags_to_offsets",
+    "biluo_tags_to_spans",
+    "biluo_to_iob",
+    "create_copy_from_base_model",
+    "docs_to_json",
+    "dont_augment",
+    "iob_to_biluo",
+    "minibatch_by_padded_size",
+    "minibatch_by_words",
+    "offsets_to_biluo_tags",
+    "orth_variants_augmenter",
+    "read_json_file",
+    "remove_bilu_prefix",
+    "split_bilu_label",
+    "tags_to_entities",
+    "validate_get_examples",
+    "validate_examples",
+]

website/docs/api/cli.mdx

@@ -1544,9 +1544,9 @@ obsolete files is left up to you.
 
 Remotes can be defined in the `remotes` section of the
 [`project.yml`](/usage/projects#project-yml). Under the hood, spaCy uses
-[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the
+[`cloudpathlib`](https://cloudpathlib.drivendata.org) to communicate with the
-remote storages, so you can use any protocol that `Pathy` supports, including
+remote storages, so you can use any protocol that `cloudpathlib` supports,
-[S3](https://aws.amazon.com/s3/),
+including [S3](https://aws.amazon.com/s3/),
 [Google Cloud Storage](https://cloud.google.com/storage), and the local
 filesystem, although you may need to install extra dependencies to use certain
 protocols.

website/docs/api/large-language-models.mdx

@@ -16,14 +16,6 @@ prototyping** and **prompting**, and turning unstructured responses into
 
 ## Config and implementation {id="config"}
 
-An LLM component is implemented through the `LLMWrapper` class. It is accessible
-through a generic `llm`
-[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
-as well as through task-specific component factories: `llm_ner`, `llm_spancat`, `llm_rel`,
-`llm_textcat`, `llm_sentiment` and `llm_summarization`.
-
-### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
-
 > #### Example
 >
 > ```python
@@ -32,13 +24,26 @@ as well as through task-specific component factories: `llm_ner`, `llm_spancat`,
 > llm = nlp.add_pipe("llm", config=config)
 >
 > # Construction via add_pipe with a task-specific factory and default GPT3.5 model
-> llm = nlp.add_pipe("llm-ner")
+> llm = nlp.add_pipe("llm_ner")
+>
+> # Construction via add_pipe with a task-specific factory and custom model
+> llm = nlp.add_pipe("llm_ner", config={"model": {"@llm_models": "spacy.Dolly.v1", "name": "dolly-v2-12b"}})
 >
 > # Construction from class
 > from spacy_llm.pipeline import LLMWrapper
 > llm = LLMWrapper(vocab=nlp.vocab, task=task, model=model, cache=cache, save_io=True)
 > ```
 
+An LLM component is implemented through the `LLMWrapper` class. It is accessible
+through a generic `llm`
+[component factory](https://spacy.io/usage/processing-pipelines#custom-components-factories)
+as well as through task-specific component factories: `llm_ner`, `llm_spancat`,
+`llm_rel`, `llm_textcat`, `llm_sentiment` and `llm_summarization`. For these
+factories, the GPT-3-5 model from OpenAI is used by default, but this can be
+customized.
+
+### LLMWrapper.\_\_init\_\_ {id="init",tag="method"}
+
 Create a new pipeline instance. 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#add_pipe).
@@ -255,9 +260,11 @@ prompting.
 > ```
 
 | Argument                    | Description                                                                                                                                                                                   |
-| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `template`                  | Custom prompt template to send to LLM model. Defaults to [summarization.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/summarization.v1.jinja). ~~str~~ |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SummarizationTask]]~~                                  |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SummarizationExample`. ~~Optional[Type[FewshotExample]]~~                                                                                      |
 | `max_n_words`               | Maximum number of words to be used in summary. Note that this should not expected to work exactly. Defaults to `None`. ~~Optional[int]~~                                                      |
 | `field`                     | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `summary`. ~~str~~                                                      |
 
@@ -326,12 +333,15 @@ the v3 implementation will use a dummy example in the prompt. Technically this
 means that the task will always perform few-shot prompting under the hood.
 
 | Argument                    | Description                                                                                                                                                                                            |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `template`                  | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~                              |
+| `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~                                                     |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~                                                                                                         |
+| `scorer`                    | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                                   |
 | `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                                     |
 | `label_definitions`         | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
-| `template`                | Custom prompt template to send to LLM model. Defaults to [ner.v3.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v3.jinja). ~~str~~                              |
 | `description` (NEW)         | A description of what to recognize or not recognize as entities. ~~str~~                                                                                                                               |
-| `examples`                | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~                              |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~                         |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                                              |
@@ -416,11 +426,14 @@ v1.
 > ```
 
 | Argument                    | Description                                                                                                                                                                                            |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `labels`                  | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                                     |
-| `label_definitions` (NEW) | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
 | `template` (NEW)            | Custom prompt template to send to LLM model. Defaults to [ner.v2.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/ner.v2.jinja). ~~str~~                              |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~                                                     |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~                                                                                                         |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                                   |
+| `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                                     |
+| `label_definitions` (NEW)   | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~                              |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~                         |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                                              |
@@ -468,9 +481,12 @@ few-shot prompting.
 > ```
 
 | Argument                    | Description                                                                                                                                                                    |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `labels`                  | Comma-separated list of labels. ~~str~~                                                                                                                                        |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                 |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[NERTask]]~~                             |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `NERExample`. ~~Optional[Type[FewshotExample]]~~                                                                                 |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                           |
+| `labels`                    | Comma-separated list of labels. ~~str~~                                                                                                                                        |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                          |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                      |
@@ -540,13 +556,16 @@ support overlapping entities and store its annotations in `doc.spans`.
 > ```
 
 | Argument                    | Description                                                                                                                                                                                            |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `template`                  | Custom prompt template to send to LLM model. Defaults to [`spancat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v3.jinja). ~~str~~                    |
+| `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                                                 |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                                                     |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                                   |
 | `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                                     |
 | `label_definitions`         | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
-| `template`                | Custom prompt template to send to LLM model. Defaults to [`spancat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v3.jinja). ~~str~~                    |
 | `description` (NEW)         | A description of what to recognize or not recognize as entities. ~~str~~                                                                                                                               |
 | `spans_key`                 | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~                                                                                                                       |
-| `examples`                | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                                                  |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~                         |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                                              |
@@ -569,12 +588,15 @@ support overlapping entities and store its annotations in `doc.spans`.
 > ```
 
 | Argument                    | Description                                                                                                                                                                                            |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `template` (NEW)            | Custom prompt template to send to LLM model. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~                    |
+| `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                                                 |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                                                     |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                                   |
 | `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                                     |
 | `label_definitions` (NEW)   | Optional dict mapping a label to a description of that label. These descriptions are added to the prompt to help instruct the LLM on what to extract. Defaults to `None`. ~~Optional[Dict[str, str]]~~ |
-| `template` (NEW)          | Custom prompt template to send to LLM model. Defaults to [`spancat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/spancat.v2.jinja). ~~str~~                    |
 | `spans_key`                 | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~                                                                                                                       |
-| `examples`                | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                                         |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                                                  |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~                         |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                                              |
@@ -600,10 +622,13 @@ v1 NER task to support overlapping entities and store its annotations in
 > ```
 
 | Argument                    | Description                                                                                                                                                                    |
-| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                 |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                         |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SpanCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                             |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                           |
 | `labels`                    | Comma-separated list of labels. ~~str~~                                                                                                                                        |
 | `spans_key`                 | Key of the `Doc.spans` dict to save the spans under. Defaults to `"sc"`. ~~str~~                                                                                               |
-| `examples`                | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                 |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, defaults to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                          |
 | `alignment_mode`            | Alignment mode in case the LLM returns entities that do not align with token boundaries. Options are `"strict"`, `"contract"` or `"expand"`. Defaults to `"contract"`. ~~str~~ |
 | `case_sensitive_matching`   | Whether to search without case sensitivity. Defaults to `False`. ~~bool~~                                                                                                      |
@@ -637,11 +662,14 @@ prompt.
 > ```
 
 | Argument                    | Description                                                                                                                                                                         |
-| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `labels`                  | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                  |
-| `label_definitions` (NEW) | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~                                                                   |
 | `template`                  | Custom prompt template to send to LLM model. Defaults to [`textcat.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v3.jinja). ~~str~~ |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                      |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                              |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                                  |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                |
+| `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                  |
+| `label_definitions` (NEW)   | Dictionary of label definitions. Included in the prompt, if set. Defaults to `None`. ~~Optional[Dict[str, str]]~~                                                                   |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~         |
 | `exclusive_classes`         | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~                              |
 | `allow_none`                | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~       |
@@ -664,10 +692,13 @@ V2 includes all v1 functionality, with an improved prompt template.
 > ```
 
 | Argument                    | Description                                                                                                                                                                         |
-| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `labels`            | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                  |
 | `template` (NEW)            | Custom prompt template to send to LLM model. Defaults to [`textcat.v2.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/textcat.v2.jinja). ~~str~~ |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                      |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                              |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                                  |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                                |
+| `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                                  |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                             |
 | `exclusive_classes`         | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~                              |
 | `allow_none`                | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~       |
@@ -691,13 +722,16 @@ prompting.
 > ```
 
 | Argument                    | Description                                                                                                                                                                   |
-| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `labels`            | Comma-separated list of labels. ~~str~~                                                                                                                                       |
 | `examples`                  | Optional function that generates examples for few-shot learning. Deafults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SpanCatTask]]~~                        |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `TextCatExample`. ~~Optional[Type[FewshotExample]]~~                                                                            |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                          |
+| `labels`                    | Comma-separated list of labels. ~~str~~                                                                                                                                       |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. ~~Optional[Callable[[str], str]]~~                       |
-| `exclusive_classes` | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Deafults to `False`. ~~bool~~                        |
+| `exclusive_classes`         | If set to `True`, only one label per document should be valid. If set to `False`, one document can have multiple labels. Defaults to `False`. ~~bool~~                        |
-| `allow_none`        | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Deafults to `True`. ~~bool~~ |
+| `allow_none`                | When set to `True`, allows the LLM to not return any of the given label. The resulting dict in `doc.cats` will have `0.0` scores for all labels. Defaults to `True`. ~~bool~~ |
-| `verbose`           | If set to `True`, warnings will be generated when the LLM returns invalid responses. Deafults to `False`. ~~bool~~                                                            |
+| `verbose`                   | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~                                                            |
 
 To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
 you can write down a few examples in a separate file, and provide these to be
@@ -723,6 +757,25 @@ supports `.yml`, `.yaml`, `.json` and `.jsonl`.
 path = "textcat_examples.json"
 ```
 
+If you want to perform few-shot learning with a binary classifier (i. e. a text
+either should or should not be assigned to a given class), you can provide
+positive and negative examples with answers of "POS" or "NEG". "POS" means that
+this example should be assigned the class label defined in the configuration,
+"NEG" means it shouldn't. E. g. for spam classification:
+
+```json
+[
+  {
+    "text": "You won the lottery! Wire a fee of 200$ to be able to withdraw your winnings.",
+    "answer": "POS"
+  },
+  {
+    "text": "Your order #123456789 has arrived",
+    "answer": "NEG"
+  }
+]
+```
+
 ### REL {id="rel"}
 
 The REL task extracts relations between named entities.
@@ -741,11 +794,14 @@ on an upstream NER component for entities extraction.
 > ```
 
 | Argument                    | Description                                                                                                                                                                 |
-| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `labels`            | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                          |
 | `template`                  | Custom prompt template to send to LLM model. Defaults to [`rel.v3.jinja`](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/rel.v1.jinja). ~~str~~ |
-| `label_definitions` | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~                                                                |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                              |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[RELTask]]~~                          |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `RELExample`. ~~Optional[Type[FewshotExample]]~~                                                                              |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                        |
+| `labels`                    | List of labels or str of comma-separated list of labels. ~~Union[List[str], str]~~                                                                                          |
+| `label_definitions`         | Dictionary providing a description for each relation label. Defaults to `None`. ~~Optional[Dict[str, str]]~~                                                                |
 | `normalizer`                | Function that normalizes the labels as returned by the LLM. If `None`, falls back to `spacy.LowercaseNormalizer.v1`. Defaults to `None`. ~~Optional[Callable[[str], str]]~~ |
 | `verbose`                   | If set to `True`, warnings will be generated when the LLM returns invalid responses. Defaults to `False`. ~~bool~~                                                          |
 
@@ -794,9 +850,12 @@ This task supports both zero-shot and few-shot prompting.
 > ```
 
 | Argument                    | Description                                                                                                                                                                   |
-| ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| --------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `template`                  | Custom prompt template to send to LLM model. Defaults to [lemma.v1.jinja](https://github.com/explosion/spacy-llm/blob/main/spacy_llm/tasks/templates/lemma.v1.jinja). ~~str~~ |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                                                |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[LemmaTask]]~~                          |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `LemmaExample`. ~~Optional[Type[FewshotExample]]~~                                                                              |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                                          |
 
 The task prompts the LLM to lemmatize the passed text and return the lemmatized
 version as a list of tokens and their corresponding lemma. E. g. the text
@@ -871,9 +930,12 @@ This task supports both zero-shot and few-shot prompting.
 > ```
 
 | Argument                    | Description                                                                                                                                              |
-| ---------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
+| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `template`                  | Custom prompt template to send to LLM model. Defaults to [sentiment.v1.jinja](./spacy_llm/tasks/templates/sentiment.v1.jinja). ~~str~~                   |
 | `examples`                  | Optional function that generates examples for few-shot learning. Defaults to `None`. ~~Optional[Callable[[], Iterable[Any]]]~~                           |
+| `parse_responses` (NEW)     | Callable for parsing LLM responses for this task. Defaults to the internal parsing method for this task. ~~Optional[TaskResponseParser[SentimentTask]]~~ |
+| `prompt_example_type` (NEW) | Type to use for fewshot examples. Defaults to `SentimentExample`. ~~Optional[Type[FewshotExample]]~~                                                     |
+| `scorer` (NEW)              | Scorer function that evaluates the task performance on provided examples. Defaults to the metric used by spaCy. ~~Optional[Scorer]~~                     |
 | `field`                     | Name of extension attribute to store summary in (i. e. the summary will be available in `doc._.{field}`). Defaults to `sentiment`. ~~str~~               |
 
 To perform [few-shot learning](/usage/large-language-models#few-shot-prompts),
@@ -953,11 +1015,11 @@ provider's API.
 Currently, these models are provided as part of the core library:
 
 | Model                         | Provider          | Supported names                                                                                                    | Default name           | Default config                       |
-| ----------------------------- | --------- | ---------------------------------------------------------------------------------------- | ---------------------- | ------------------------------------ |
+| ----------------------------- | ----------------- | ------------------------------------------------------------------------------------------------------------------ | ---------------------- | ------------------------------------ |
 | `spacy.GPT-4.v1`              | OpenAI            | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]`                                                           | `"gpt-4"`              | `{}`                                 |
 | `spacy.GPT-4.v2`              | OpenAI            | `["gpt-4", "gpt-4-0314", "gpt-4-32k", "gpt-4-32k-0314"]`                                                           | `"gpt-4"`              | `{temperature=0.0}`                  |
-| `spacy.GPT-3-5.v1`            | OpenAI    | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"`      | `{}`                                 |
+| `spacy.GPT-3-5.v1`            | OpenAI            | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"`      | `{}`                                 |
-| `spacy.GPT-3-5.v2`            | OpenAI    | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k"]` | `"gpt-3.5-turbo"`      | `{temperature=0.0}`                  |
+| `spacy.GPT-3-5.v2`            | OpenAI            | `["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-0613-16k", "gpt-3.5-turbo-instruct"]` | `"gpt-3.5-turbo"`      | `{temperature=0.0}`                  |
 | `spacy.Davinci.v1`            | OpenAI            | `["davinci"]`                                                                                                      | `"davinci"`            | `{}`                                 |
 | `spacy.Davinci.v2`            | OpenAI            | `["davinci"]`                                                                                                      | `"davinci"`            | `{temperature=0.0, max_tokens=500}`  |
 | `spacy.Text-Davinci.v1`       | OpenAI            | `["text-davinci-003", "text-davinci-002"]`                                                                         | `"text-davinci-003"`   | `{}`                                 |
@@ -976,6 +1038,7 @@ Currently, these models are provided as part of the core library:
 | `spacy.Ada.v2`                | OpenAI            | `["ada"]`                                                                                                          | `"ada"`                | `{temperature=0.0, max_tokens=500}`  |
 | `spacy.Text-Ada.v1`           | OpenAI            | `["text-ada-001"]`                                                                                                 | `"text-ada-001"`       | `{}`                                 |
 | `spacy.Text-Ada.v2`           | OpenAI            | `["text-ada-001"]`                                                                                                 | `"text-ada-001"`       | `{temperature=0.0, max_tokens=500}`  |
+| `spacy.Azure.v1`              | Microsoft, OpenAI | Arbitrary values                                                                                                   | No default             | `{temperature=0.0}`                  |
 | `spacy.Command.v1`            | Cohere            | `["command", "command-light", "command-light-nightly", "command-nightly"]`                                         | `"command"`            | `{}`                                 |
 | `spacy.Claude-2.v1`           | Anthropic         | `["claude-2", "claude-2-100k"]`                                                                                    | `"claude-2"`           | `{}`                                 |
 | `spacy.Claude-1.v1`           | Anthropic         | `["claude-1", "claude-1-100k"]`                                                                                    | `"claude-1"`           | `{}`                                 |
@@ -984,10 +1047,29 @@ Currently, these models are provided as part of the core library:
 | `spacy.Claude-1-3.v1`         | Anthropic         | `["claude-1.3", "claude-1.3-100k"]`                                                                                | `"claude-1.3"`         | `{}`                                 |
 | `spacy.Claude-instant-1.v1`   | Anthropic         | `["claude-instant-1", "claude-instant-1-100k"]`                                                                    | `"claude-instant-1"`   | `{}`                                 |
 | `spacy.Claude-instant-1-1.v1` | Anthropic         | `["claude-instant-1.1", "claude-instant-1.1-100k"]`                                                                | `"claude-instant-1.1"` | `{}`                                 |
+| `spacy.PaLM.v1`               | Google            | `["chat-bison-001", "text-bison-001"]`                                                                             | `"text-bison-001"`     | `{temperature=0.0}`                  |
 
 To use these models, make sure that you've [set the relevant API](#api-keys)
 keys as environment variables.
 
+**⚠️ A note on `spacy.Azure.v1`.** Working with Azure OpenAI is slightly
+different than working with models from other providers:
+
+- In Azure LLMs have to be made available by creating a _deployment_ of a given
+  model (e. g. GPT-3.5). This deployment can have an arbitrary name. The `name`
+  argument, which everywhere else denotes the model name (e. g. `claude-1.0`,
+  `gpt-3.5`), here refers to the _deployment name_.
+- Deployed Azure OpenAI models are reachable via a resource-specific base URL,
+  usually of the form `https://{resource}.openai.azure.com`. Hence the URL has
+  to be specified via the `base_url` argument.
+- Azure further expects the _API version_ to be specified. The default value for
+  this, via the `api_version` argument, is currently `2023-05-15` but may be
+  updated in the future.
+- Finally, since we can't infer information about the model from the deployment
+  name, `spacy-llm` requires the `model_type` to be set to either
+  `"completions"` or `"chat"`, depending on whether the deployed model is a
+  completion or chat model.
+
 #### API Keys {id="api-keys"}
 
 Note that when using hosted services, you have to ensure that the proper API
@@ -1014,6 +1096,12 @@ For Anthropic:
 export ANTHROPIC_API_KEY="..."
 ```
 
+For PaLM:
+
+```shell
+export PALM_API_KEY="..."
+```
+
 ### Models via HuggingFace {id="models-hf"}
 
 These models all take the same parameters:
@@ -1037,11 +1125,27 @@ Currently, these models are provided as part of the core library:
 | Model                | Provider        | Supported names                                                                                              | HF directory                           |
 | -------------------- | --------------- | ------------------------------------------------------------------------------------------------------------ | -------------------------------------- |
 | `spacy.Dolly.v1`     | Databricks      | `["dolly-v2-3b", "dolly-v2-7b", "dolly-v2-12b"]`                                                             | https://huggingface.co/databricks      |
-| `spacy.Llama2.v1`    | Meta AI         | `["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]`                                                      | https://huggingface.co/meta-llama      |
 | `spacy.Falcon.v1`    | TII             | `["falcon-rw-1b", "falcon-7b", "falcon-7b-instruct", "falcon-40b-instruct"]`                                 | https://huggingface.co/tiiuae          |
+| `spacy.Llama2.v1`    | Meta AI         | `["Llama-2-7b-hf", "Llama-2-13b-hf", "Llama-2-70b-hf"]`                                                      | https://huggingface.co/meta-llama      |
+| `spacy.Mistral.v1`   | Mistral AI      | `["Mistral-7B-v0.1", "Mistral-7B-Instruct-v0.1"]`                                                            | https://huggingface.co/mistralai       |
 | `spacy.StableLM.v1`  | Stability AI    | `["stablelm-base-alpha-3b", "stablelm-base-alpha-7b", "stablelm-tuned-alpha-3b", "stablelm-tuned-alpha-7b"]` | https://huggingface.co/stabilityai     |
 | `spacy.OpenLLaMA.v1` | OpenLM Research | `["open_llama_3b", "open_llama_7b", "open_llama_7b_v2", "open_llama_13b"]`                                   | https://huggingface.co/openlm-research |
 
+<Infobox variant="warning" title="Gated models on Hugging Face" id="hf_licensing">
+
+Some models available on Hugging Face (HF), such as Llama 2, are _gated models_.
+That means that users have to fulfill certain requirements to be allowed access
+to these models. In the case of Llama 2 you'll need to request agree to Meta's
+Terms of Service while logged in with your HF account. After Meta grants you
+permission to use Llama 2, you'll be able to download and use the model.
+
+This requires that you are logged in with your HF account on your local
+machine - check out the HF quick start documentation. In a nutshell, you'll need
+to create an access token on HF and log in to HF using your access token, e. g.
+with `huggingface-cli login`.
+
+</Infobox>
+
 Note that Hugging Face will download the model the first time you use it - you
 can
 [define the cached directory](https://huggingface.co/docs/huggingface_hub/main/en/guides/manage-cache)

website/docs/api/spancategorizer.mdx

@@ -89,6 +89,21 @@ architectures and their arguments and hyperparameters.
 | `negative_weight` <Tag variant="new">3.5.1</Tag>    | Multiplier for the loss terms. It can be used to downweight the negative samples if there are too many. It is only used when `add_negative_label` is `True`. Defaults to `1.0`. ~~float~~                                                                                                               |
 | `allow_overlap` <Tag variant="new">3.5.1</Tag>      | If `True`, the data is assumed to contain overlapping spans. It is only available when `max_positive` is exactly 1. Defaults to `True`. ~~bool~~                                                                                                                                                        |
 
+<Infobox variant="warning">
+
+If you set a non-default value for `spans_key`, you'll have to update
+`[training.score_weights]` as well so that weights are computed properly. E. g.
+for `spans_key == "myspankey"`, include this in your config:
+
+```ini
+[training.score_weights]
+spans_myspankey_f = 1.0
+spans_myspankey_p = 0.0
+spans_myspankey_r = 0.0
+```
+
+</Infobox>
+
 ```python
 %%GITHUB_SPACY/spacy/pipeline/spancat.py
 ```

website/docs/usage/101/_named-entities.mdx

@@ -31,8 +31,6 @@ for ent in doc.ents:
 Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
 our example sentence and its named entities look like:
 
-<Iframe
+<Standalone height={120}>
-  title="displaCy visualization of entities"
+<div style={{lineHeight: 2.5, fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18}}><mark style={{ background: '#7aecec', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Apple <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark> is looking at buying <mark style={{ background: '#feca74', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>U.K. <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>GPE</span></mark> startup for <mark style={{ background: '#e4e7d2', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>$1 billion <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>MONEY</span></mark></div>
-  src="/images/displacy-ent1.html"
+</Standalone>
-  height={100}
-/>

website/docs/usage/101/_pos-deps.mdx

@@ -56,8 +56,7 @@ for token in doc:
 Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what
 our example sentence and its dependencies look like:
 
-<Iframe
+<ImageScrollable
-  title="displaCy visualization of dependencies and entities"
+  src="/images/displacy-long.svg"
-  src="/images/displacy-long.html"
+  width={1975}
-  height={450}
 />

website/docs/usage/large-language-models.mdx

@@ -170,8 +170,8 @@ to be `"databricks/dolly-v2-12b"` for better performance.
 ### Example 3: Create the component directly in Python {id="example-3"}
 
 The `llm` component behaves as any other component does, and there are
-[task-specific components](/api/large-language-models#config) defined to
+[task-specific components](/api/large-language-models#config) defined to help
-help you hit the ground running with a reasonable built-in task implementation.
+you hit the ground running with a reasonable built-in task implementation.
 
 ```python
 import spacy
@@ -436,7 +436,7 @@ respectively. Alternatively you can use LangChain to access hosted or local
 models by specifying one of the models registered with the `langchain.` prefix.
 
 <Infobox>
-_Why LangChain if there are also are a native REST and a HuggingFace interface? When should I use what?_
+_Why LangChain if there are also are native REST and HuggingFace interfaces? When should I use what?_
 
 Third-party libraries like `langchain` focus on prompt management, integration
 of many different LLM APIs, and other related features such as conversational
@@ -476,6 +476,7 @@ provider's documentation.
 | [`spacy.Curie.v2`](/api/large-language-models#models-rest)              | OpenAI’s `curie` model family.                 |
 | [`spacy.Babbage.v2`](/api/large-language-models#models-rest)            | OpenAI’s `babbage` model family.               |
 | [`spacy.Ada.v2`](/api/large-language-models#models-rest)                | OpenAI’s `ada` model family.                   |
+| [`spacy.Azure.v1`](/api/large-language-models#models-rest)              | Azure's OpenAI models.                         |
 | [`spacy.Command.v1`](/api/large-language-models#models-rest)            | Cohere’s `command` model family.               |
 | [`spacy.Claude-2.v1`](/api/large-language-models#models-rest)           | Anthropic’s `claude-2` model family.           |
 | [`spacy.Claude-1.v1`](/api/large-language-models#models-rest)           | Anthropic’s `claude-1` model family.           |
@@ -484,8 +485,10 @@ provider's documentation.
 | [`spacy.Claude-1-0.v1`](/api/large-language-models#models-rest)         | Anthropic’s `claude-1.0` model family.         |
 | [`spacy.Claude-1-2.v1`](/api/large-language-models#models-rest)         | Anthropic’s `claude-1.2` model family.         |
 | [`spacy.Claude-1-3.v1`](/api/large-language-models#models-rest)         | Anthropic’s `claude-1.3` model family.         |
+| [`spacy.PaLM.v1`](/api/large-language-models#models-rest)               | Google’s `PaLM` model family.                  |
 | [`spacy.Dolly.v1`](/api/large-language-models#models-hf)                | Dolly models through HuggingFace.              |
 | [`spacy.Falcon.v1`](/api/large-language-models#models-hf)               | Falcon models through HuggingFace.             |
+| [`spacy.Mistral.v1`](/api/large-language-models#models-hf)              | Mistral models through HuggingFace.            |
 | [`spacy.Llama2.v1`](/api/large-language-models#models-hf)               | Llama2 models through HuggingFace.             |
 | [`spacy.StableLM.v1`](/api/large-language-models#models-hf)             | StableLM models through HuggingFace.           |
 | [`spacy.OpenLLaMA.v1`](/api/large-language-models#models-hf)            | OpenLLaMA models through HuggingFace.          |

website/docs/usage/linguistic-features.mdx

@@ -290,11 +290,7 @@ for token in doc:
 | toward        | `prep`     | shift     | `NOUN`   | manufacturers           |
 | manufacturers | `pobj`     | toward    | `ADP`    |                         |
 
-<Iframe
+<ImageScrollable src="/images/displacy-long2.svg" width={1275} />
-  title="displaCy visualization of dependencies and entities 2"
-  src="/images/displacy-long2.html"
-  height={450}
-/>
 
 Because the syntactic relations form a tree, every word has **exactly one
 head**. You can therefore iterate over the arcs in the tree by iterating over
@@ -709,11 +705,9 @@ doc = nlp(text)
 displacy.serve(doc, style="ent")
 ```
 
-<Iframe
+<Standalone height={180}>
-  title="displaCy visualizer for entities"
+<div style={{lineHeight: 2.5, fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18}}>When <mark style={{ background: '#aa9cfc', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Sebastian Thrun <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>PERSON</span></mark> started working on self-driving cars at <mark style={{ background: '#7aecec', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Google <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark> in <mark style={{ background: '#bfe1d9', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>2007 <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>DATE</span></mark>, few people outside of the company took him seriously.</div>
-  src="/images/displacy-ent2.html"
+</Standalone>
-  height={180}
-/>
 
 ## Entity Linking {id="entity-linking"}
 
@@ -723,6 +717,10 @@ identifier from a knowledge base (KB). You can create your own
 [`KnowledgeBase`](/api/kb) and [train](/usage/training) a new
 [`EntityLinker`](/api/entitylinker) using that custom knowledge base.
 
+As an example on how to define a KnowledgeBase and train an entity linker model,
+see [`this tutorial`](https://github.com/explosion/projects/blob/v3/tutorials/nel_emerson)
+using [spaCy projects](/usage/projects).
+
 ### Accessing entity identifiers {id="entity-linking-accessing",model="entity linking"}
 
 The annotated KB identifier is accessible as either a hash value or as a string,
@@ -733,6 +731,7 @@ object, or the `ent_kb_id` and `ent_kb_id_` attributes of a
 ```python
 import spacy
 
+# "my_custom_el_pipeline" is assumed to be a custom NLP pipeline that was trained and serialized to disk
 nlp = spacy.load("my_custom_el_pipeline")
 doc = nlp("Ada Lovelace was born in London")
 

website/docs/usage/projects.mdx

@@ -656,9 +656,9 @@ locally.
 You can list one or more remotes in the `remotes` section of your
 [`project.yml`](#project-yml) by mapping a string name to the URL of the
 storage. Under the hood, spaCy uses
-[`Pathy`](https://github.com/justindujardin/pathy) to communicate with the
+[`cloudpathlib`](https://cloudpathlib.drivendata.org) to communicate with the
-remote storages, so you can use any protocol that `Pathy` supports, including
+remote storages, so you can use any protocol that `cloudpathlib` supports,
-[S3](https://aws.amazon.com/s3/),
+including [S3](https://aws.amazon.com/s3/),
 [Google Cloud Storage](https://cloud.google.com/storage), and the local
 filesystem, although you may need to install extra dependencies to use certain
 protocols.

website/docs/usage/rule-based-matching.mdx

@@ -1144,10 +1144,9 @@ relations and tokens we want to match:
 > displacy.serve(doc)
 > ```
 
-<Iframe
+<ImageScrollable
-  title="displaCy visualization of dependencies"
+  src="/images/displacy-dep-founded.svg"
-  src="/images/displacy-dep-founded.html"
+  width={925}
-  height={450}
 />
 
 The relations we're interested in are:

website/docs/usage/saving-loading.mdx

@@ -586,11 +586,9 @@ After installing the package, the custom colors will be used when visualizing
 text with `displacy`. Whenever the label `SNEK` is assigned, it will be
 displayed in `#3dff74`.
 
-<Iframe
+<Standalone height={100}>
-  title="displaCy visualization of entities"
+<div style={{lineHeight: 2.5, fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18}}>🌱🌿 <mark style={{ background: '#3dff74', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>🐍 <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>SNEK</span></mark> ____ 🌳🌲 ____ <mark style={{ background: '#cfc5ff', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>👨‍🌾 <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>HUMAN</span></mark> 🏘️</div>
-  src="/images/displacy-ent-snek.html"
+</Standalone>
-  height={100}
-/>
 
 ## Saving, loading and distributing trained pipelines {id="models"}
 

website/docs/usage/v3-3.mdx

@@ -77,11 +77,9 @@ doc.spans["custom"] = [Span(doc, 3, 6, "ORG"), Span(doc, 5, 6, "GPE")]
 displacy.serve(doc, style="span", options={"spans_key": "custom"})
 ```
 
-<Iframe
+<Standalone height={100}>
-  title="displaCy visualizer for overlapping spans"
+<div style={{ lineHeight: 2.5, direction: 'ltr', fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18 }}>Welcome to the <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>Bank<span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#7aecec', top: 40, height: 4, borderTopLeftRadius: 3, borderBottomLeftRadius: 3, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}><span style={{ background: '#7aecec', color: '#000', top: '-0.5em', padding: '2px 3px', position: 'absolute', fontSize: '0.6em', fontWeight: 'bold', lineHeight: 1, borderRadius: 3 }}>ORG</span></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>of <span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>China<span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#feca74', top: 57, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#feca74', top: 57, height: 4, borderTopLeftRadius: 3, borderBottomLeftRadius: 3, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}><span style={{ background: '#feca74', color: '#000', top: '-0.5em', padding: '2px 3px', position: 'absolute', fontSize: '0.6em', fontWeight: 'bold', lineHeight: 1, borderRadius: 3 }}>GPE</span></span></span>.</div>
-  src="/images/displacy-span.html"
+</Standalone>
-  height={180}
-/>
 
 ## Additional features and improvements
 

website/docs/usage/v3-7.mdx

@@ -0,0 +1,140 @@
+---
+title: What's New in v3.7
+teaser: New features and how to upgrade
+menu:
+  - ['New Features', 'features']
+  - ['Upgrading Notes', 'upgrading']
+---
+
+## New features {id="features",hidden="true"}
+
+spaCy v3.7 adds support for Python 3.12, introduces the new standalone library
+[Weasel](https://github.com/explosion/weasel) for project workflows, and updates
+the transformer-based trained pipelines to use our new
+[Curated Transformers](https://github.com/explosion/curated-transformers)
+library.
+
+This release drops support for Python 3.6.
+
+### Weasel {id="weasel"}
+
+The [spaCy projects](/usage/projects) functionality has been moved into a new
+standalone library [Weasel](https://github.com/explosion/weasel). This brings
+minor changes to spaCy-specific settings in spaCy projects (see
+[upgrading](#upgrading) below), but also makes it possible to use the same
+workflow functionality outside of spaCy.
+
+All `spacy project` commands should run as before, just now they're using Weasel
+under the hood.
+
+<Infobox title="Remote storage for Python 3.12" variant="warning">
+
+Remote storage for spaCy projects is not yet supported for Python 3.12. Use
+Python 3.11 or earlier for remote storage.
+
+</Infobox>
+
+### Registered vectors {id="custom-vectors"}
+
+You can specify a custom registered vectors class under `[nlp.vectors]` in order
+to use static vectors in formats other than the ones supported by
+[`Vectors`](/api/vectors). To implement your custom vectors, extend the abstract
+class [`BaseVectors`](/api/basevectors). See an example using
+[BPEmb subword embeddings](/usage/embeddings-transformers#custom-vectors).
+
+### Additional features and improvements {id="additional-features-and-improvements"}
+
+- Add support for Python 3.12.
+- Extend to Thinc v8.2.
+- Extend `transformers` extra to `spacy-transformers` v1.3.
+- Add `--spans-key` option for CLI evaluation with `spacy benchmark accuracy`.
+- Load the CLI module lazily for `spacy.info`.
+- Add type stubs for for `spacy.training.example`.
+- Warn for unsupported pattern keys in dependency matcher.
+- `Language.replace_listeners`: Pass the replaced listener and the `tok2vec`
+  pipe to the callback in order to support `spacy-curated-transformers`.
+- Always use `tqdm` with `disable=None` in order to disable output in
+  non-interactive environments.
+- Language updates:
+  - Add left and right pointing angle brackets as punctuation to ancient Greek.
+  - Update example sentences for Turkish.
+- Package setup updates:
+  - Update NumPy build constraints for NumPy 1.25+. For Python 3.9+, it is no
+    longer necessary to set build constraints while building binary wheels.
+  - Refactor Cython profiling in order to disable profiling for Python 3.12 in
+    the package setup, since Cython does not currently support profiling for
+    Python 3.12.
+
+## Trained pipelines {id="pipelines"}
+
+### Pipeline updates {id="pipeline-updates"}
+
+The transformer-based `trf` pipelines have been updated to use our new
+[Curated Transformers](https://github.com/explosion/curated-transformers)
+library using the Thinc model wrappers and pipeline component from
+[spaCy Curated Transformers](https://github.com/explosion/spacy-curated-transformers).
+
+## Notes about upgrading from v3.6 {id="upgrading"}
+
+This release drops support for Python 3.6, drops mypy checks for Python 3.7 and
+removes the `ray` extra. In addition there are several minor changes for spaCy
+projects described in the following section.
+
+### Backwards incompatibilities for spaCy Projects {id="upgrading-projects"}
+
+`spacy project` has a few backwards incompatibilities due to the transition to
+the standalone library [Weasel](https://github.com/explosion/weasel), which is
+not as tightly coupled to spaCy. Weasel produces warnings when it detects older
+spaCy-specific settings in your environment or project config.
+
+- Support for the `spacy_version` configuration key has been dropped.
+- Support for the `check_requirements` configuration key has been dropped due to
+  the deprecation of `pkg_resources`.
+- The `SPACY_CONFIG_OVERRIDES` environment variable is no longer checked. You
+  can set configuration overrides using `WEASEL_CONFIG_OVERRIDES`.
+- Support for `SPACY_PROJECT_USE_GIT_VERSION` environment variable has been
+  dropped.
+- Error codes are now Weasel-specific and do not follow spaCy error codes.
+
+### Pipeline package version compatibility {id="version-compat"}
+
+> #### Using legacy implementations
+>
+> In spaCy v3, you'll still be able to load and reference legacy implementations
+> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the
+> components or architectures change and newer versions are available in the
+> core library.
+
+When you're loading a pipeline package trained with an earlier version of spaCy
+v3, you will see a warning telling you that the pipeline may be incompatible.
+This doesn't necessarily have to be true, but we recommend running your
+pipelines against your test suite or evaluation data to make sure there are no
+unexpected results.
+
+If you're using one of the [trained pipelines](/models) we provide, you should
+run [`spacy download`](/api/cli#download) to update to the latest version. To
+see an overview of all installed packages and their compatibility, you can run
+[`spacy validate`](/api/cli#validate).
+
+If you've trained your own custom pipeline and you've confirmed that it's still
+working as expected, you can update the spaCy version requirements in the
+[`meta.json`](/api/data-formats#meta):
+
+```diff
+- "spacy_version": ">=3.6.0,<3.7.0",
++ "spacy_version": ">=3.6.0,<3.8.0",
+```
+
+### Updating v3.6 configs
+
+To update a config from spaCy v3.6 with the new v3.7 settings, run
+[`init fill-config`](/api/cli#init-fill-config):
+
+```cli
+$ python -m spacy init fill-config config-v3.6.cfg config-v3.7.cfg
+```
+
+In many cases ([`spacy train`](/api/cli#train),
+[`spacy.load`](/api/top-level#spacy.load)), the new defaults will be filled in
+automatically, but you'll need to fill in the new settings to run
+[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data).

website/docs/usage/visualizers.mdx

@@ -119,11 +119,9 @@ doc = nlp(text)
 displacy.serve(doc, style="ent")
 ```
 
-<Iframe
+<Standalone height={180}>
-  title="displaCy visualizer for entities"
+<div style={{lineHeight: 2.5, fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18}}>When <mark style={{ background: '#aa9cfc', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Sebastian Thrun <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>PERSON</span></mark> started working on self-driving cars at <mark style={{ background: '#7aecec', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Google <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark> in <mark style={{ background: '#bfe1d9', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>2007 <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>DATE</span></mark>, few people outside of the company took him seriously.</div>
-  src="/images/displacy-ent2.html"
+</Standalone>
-  height={180}
-/>
 
 The entity visualizer lets you customize the following `options`:
 
@@ -148,11 +146,9 @@ use the `colors` setting to add your own colors for them.
 > displacy.serve(doc, style="ent", options=options)
 > ```
 
-<Iframe
+<Standalone height={225}>
-  title="displaCy visualizer for entities (custom styling)"
+<div style={{lineHeight: 2.5, fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18}}>But <mark style={{ background: 'linear-gradient(90deg, #aa9cfc, #fc9ce7)', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Google <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark> is starting from behind. The company made a late push into hardware, and <mark style={{ background: 'linear-gradient(90deg, #aa9cfc, #fc9ce7)', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Apple <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark>’s Siri, available on iPhones, and <mark style={{ background: 'linear-gradient(90deg, #aa9cfc, #fc9ce7)', padding: '0.45em 0.6em', margin: '0 0.25em', lineHeight: 1, borderRadius: '0.35em'}}>Amazon <span style={{ fontSize: '0.8em', fontWeight: 'bold', lineHeight: 1, borderRadius: '0.35em', marginLeft: '0.5rem'}}>ORG</span></mark>’s Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer adoption.</div>
-  src="/images/displacy-ent-custom.html"
+</Standalone>
-  height={225}
-/>
 
 The above example uses a little trick: Since the background color values are
 added as the `background` style attribute, you can use any
@@ -197,11 +193,9 @@ doc.spans["sc"] = [
 displacy.serve(doc, style="span")
 ```
 
-<Iframe
+<Standalone height={100}>
-  title="displaCy visualizer for overlapping spans"
+<div style={{ lineHeight: 2.5, direction: 'ltr', fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18 }}>Welcome to the <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>Bank<span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#7aecec', top: 40, height: 4, borderTopLeftRadius: 3, borderBottomLeftRadius: 3, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}><span style={{ background: '#7aecec', color: '#000', top: '-0.5em', padding: '2px 3px', position: 'absolute', fontSize: '0.6em', fontWeight: 'bold', lineHeight: 1, borderRadius: 3 }}>ORG</span></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>of <span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>China<span style={{ background: '#7aecec', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#feca74', top: 57, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#feca74', top: 57, height: 4, borderTopLeftRadius: 3, borderBottomLeftRadius: 3, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}><span style={{ background: '#feca74', color: '#000', top: '-0.5em', padding: '2px 3px', position: 'absolute', fontSize: '0.6em', fontWeight: 'bold', lineHeight: 1, borderRadius: 3 }}>GPE</span></span></span>.</div>
-  src="/images/displacy-span.html"
+</Standalone>
-  height={180}
-/>
 
 The span visualizer lets you customize the following `options`:
 
@@ -223,11 +217,9 @@ specify which one displaCy should use with `spans_key` (`sc` is the default).
 > displacy.serve(doc, style="span", options=options)
 > ```
 
-<Iframe
+<Standalone height={100}>
-  title="displaCy visualizer for spans (custom spans_key)"
+<div style={{ lineHeight: 2.5, direction: 'ltr', fontFamily: "-apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'", fontSize: 18 }}>Welcome to the <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>Bank<span style={{ background: '#ddd', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span><span style={{ background: '#ddd', top: 40, height: 4, borderTopLeftRadius: 3, borderBottomLeftRadius: 3, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}><span style={{ background: '#ddd', color: '#000', top: '-0.5em', padding: '2px 3px', position: 'absolute', fontSize: '0.6em', fontWeight: 'bold', lineHeight: 1, borderRadius: 3 }}>BANK</span></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>of <span style={{ background: '#ddd', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span></span> <span style={{ fontWeight: 'bold', display: 'inline-block', position: 'relative'}}>China<span style={{ background: '#ddd', top: 40, height: 4, left: -1, width: 'calc(100% + 2px)', position: 'absolute' }}></span></span>.</div>
-  src="/images/displacy-span-custom.html"
+</Standalone>
-  height={225}
-/>
 
 ## Using displaCy in Jupyter notebooks {id="jupyter"}
 

website/meta/sidebars.json

@@ -9,13 +9,9 @@
                     { "text": "Models & Languages", "url": "/usage/models" },
                     { "text": "Facts & Figures", "url": "/usage/facts-figures" },
                     { "text": "spaCy 101", "url": "/usage/spacy-101" },
-                    { "text": "New in v3.0", "url": "/usage/v3" },
+                    { "text": "New in v3.7", "url": "/usage/v3-7" },
-                    { "text": "New in v3.1", "url": "/usage/v3-1" },
+                    { "text": "New in v3.6", "url": "/usage/v3-6" },
-                    { "text": "New in v3.2", "url": "/usage/v3-2" },
+                    { "text": "New in v3.5", "url": "/usage/v3-5" }
-                    { "text": "New in v3.3", "url": "/usage/v3-3" },
-                    { "text": "New in v3.4", "url": "/usage/v3-4" },
-                    { "text": "New in v3.5", "url": "/usage/v3-5" },
-                    { "text": "New in v3.6", "url": "/usage/v3-6" }
                 ]
             },
             {

website/meta/site.json

@@ -27,7 +27,7 @@
         "indexName": "spacy"
     },
     "binderUrl": "explosion/spacy-io-binder",
-    "binderVersion": "3.6",
+    "binderVersion": "3.7",
     "sections": [
         { "id": "usage", "title": "Usage Documentation", "theme": "blue" },
         { "id": "models", "title": "Models Documentation", "theme": "blue" },

website/public/images/displacy-ent-custom.html

@@ -1,80 +0,0 @@
-<div
-    class="entities"
-    style="
-        line-height: 2.5;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 18px;
-    "
-    >But
-    <mark
-        class="entity"
-        style="
-            background: linear-gradient(90deg, #aa9cfc, #fc9ce7);
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-        >Google
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >ORG</span
-        ></mark
-    >is starting from behind. The company made a late push into hardware, and
-    <mark
-        class="entity"
-        style="
-            background: linear-gradient(90deg, #aa9cfc, #fc9ce7);
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-        >Apple
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >ORG</span
-        ></mark
-    >’s Siri, available on iPhones, and
-    <mark
-        class="entity"
-        style="
-            background: linear-gradient(90deg, #aa9cfc, #fc9ce7);
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-        >Amazon
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >ORG</span
-        ></mark
-    >’s Alexa software, which runs on its Echo and Dot devices, have clear leads in consumer
-    adoption.</div
->

website/public/images/displacy-ent-snek.html

@@ -1,59 +0,0 @@
-<div
-    class="entities"
-    style="
-        line-height: 2.5;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 16px;
-    "
->
-    🌱🌿
-    <mark
-        class="entity"
-        style="
-            background: #3dff74;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-        >🐍
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >SNEK</span
-        ></mark
-    >
-    ____ 🌳🌲 ____
-    <mark
-        class="entity"
-        style="
-            background: #cfc5ff;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-        >👨‍🌾
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >HUMAN</span
-        ></mark
-    >
-    🏘️
-</div>

website/public/images/displacy-ent1.html

@@ -1,84 +0,0 @@
-<div
-    class="entities"
-    style="
-        line-height: 2.5;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 16px;
-    "
->
-    <mark
-        class="entity"
-        style="
-            background: #7aecec;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        Apple
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >ORG</span
-        >
-    </mark>
-    is looking at buying
-    <mark
-        class="entity"
-        style="
-            background: #feca74;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        U.K.
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >GPE</span
-        >
-    </mark>
-    startup for
-    <mark
-        class="entity"
-        style="
-            background: #e4e7d2;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        $1 billion
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >MONEY</span
-        >
-    </mark>
-</div>

website/public/images/displacy-ent2.html

@@ -1,86 +0,0 @@
-<div
-    class="entities"
-    style="
-        line-height: 2.5;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 18px;
-    "
->
-    When
-    <mark
-        class="entity"
-        style="
-            background: #aa9cfc;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        Sebastian Thrun
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >PERSON</span
-        >
-    </mark>
-    started working on self-driving cars at
-    <mark
-        class="entity"
-        style="
-            background: #7aecec;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        Google
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >ORG</span
-        >
-    </mark>
-    in
-    <mark
-        class="entity"
-        style="
-            background: #bfe1d9;
-            padding: 0.45em 0.6em;
-            margin: 0 0.25em;
-            line-height: 1;
-            border-radius: 0.35em;
-        "
-    >
-        2007
-        <span
-            style="
-                font-size: 0.8em;
-                font-weight: bold;
-                line-height: 1;
-                border-radius: 0.35em;
-                text-transform: uppercase;
-                vertical-align: middle;
-                margin-left: 0.5rem;
-            "
-            >DATE</span
-        >
-    </mark>
-    , few people outside of the company took him seriously.
-</div>

website/public/images/displacy-long2.svg

@@ -0,0 +1,212 @@
+<svg
+    xmlns="http://www.w3.org/2000/svg"
+    xmlns:xlink="http://www.w3.org/1999/xlink"
+    id="0"
+    class="displacy"
+    width="1275"
+    height="399.5"
+    style="
+        max-width: none;
+        height: 399.5px;
+        color: #000000;
+        background: #ffffff;
+        font-family: Arial;
+    "
+>
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="50">Autonomous</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="50">ADJ</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="225">cars</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="225">NOUN</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="400">shift</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="400">VERB</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="575">insurance</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="575">NOUN</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="750">liability</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="750">NOUN</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="925">toward</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="925">ADP</tspan>
+    </text>
+
+    <text class="displacy-token" fill="currentColor" text-anchor="middle" y="309.5">
+        <tspan class="displacy-word" fill="currentColor" x="1100">manufacturers</tspan>
+        <tspan class="displacy-tag" dy="2em" fill="currentColor" x="1100">NOUN</tspan>
+    </text>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-0"
+            stroke-width="2px"
+            d="M70,264.5 C70,177.0 215.0,177.0 215.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-0"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                amod
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M70,266.5 L62,254.5 78,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-1"
+            stroke-width="2px"
+            d="M245,264.5 C245,177.0 390.0,177.0 390.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-1"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                nsubj
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M245,266.5 L237,254.5 253,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-2"
+            stroke-width="2px"
+            d="M595,264.5 C595,177.0 740.0,177.0 740.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-2"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                compound
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M595,266.5 L587,254.5 603,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-3"
+            stroke-width="2px"
+            d="M420,264.5 C420,89.5 745.0,89.5 745.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-3"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                dobj
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M745.0,266.5 L753.0,254.5 737.0,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-4"
+            stroke-width="2px"
+            d="M420,264.5 C420,2.0 925.0,2.0 925.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-4"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                prep
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M925.0,266.5 L933.0,254.5 917.0,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+
+    <g class="displacy-arrow">
+        <path
+            class="displacy-arc"
+            id="arrow-0-5"
+            stroke-width="2px"
+            d="M945,264.5 C945,177.0 1090.0,177.0 1090.0,264.5"
+            fill="none"
+            stroke="currentColor"
+        ></path>
+        <text dy="1.25em" style="font-size: 0.8em; letter-spacing: 1px">
+            <textpath
+                xlink:href="#arrow-0-5"
+                class="displacy-label"
+                startOffset="50%"
+                fill="currentColor"
+                text-anchor="middle"
+            >
+                pobj
+            </textpath>
+        </text>
+        <path
+            class="displacy-arrowhead"
+            d="M1090.0,266.5 L1098.0,254.5 1082.0,254.5"
+            fill="currentColor"
+        ></path>
+    </g>
+</svg>

website/public/images/displacy-span-custom.html

@@ -1,84 +0,0 @@
-<div
-    class="spans"
-    style="
-        line-height: 2.5;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 18px;
-        direction: ltr;
-    "
->
-    Welcome to the
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        Bank
-        <span
-            style="
-                background: #ddd;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-        <span
-            style="
-                background: #ddd;
-                top: 40px;
-                height: 4px;
-                border-top-left-radius: 3px;
-                border-bottom-left-radius: 3px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-            <span
-                style="
-                    background: #ddd;
-                    color: #000;
-                    top: -0.5em;
-                    padding: 2px 3px;
-                    position: absolute;
-                    font-size: 0.6em;
-                    font-weight: bold;
-                    line-height: 1;
-                    border-radius: 3px;
-                "
-            >
-                BANK
-            </span>
-        </span>
-    </span>
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        of
-        <span
-            style="
-                background: #ddd;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-    </span>
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        China
-
-        <span
-            style="
-                background: #ddd;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-    </span>
-    .
-</div>

website/public/images/displacy-span.html

@@ -1,123 +0,0 @@
-<div
-    class="spans"
-    style="
-        line-height: 2.5;
-        direction: ltr;
-        font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, Arial, sans-serif,
-            'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';
-        font-size: 18px;
-    "
->
-    Welcome to the
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        Bank
-        <span
-            style="
-                background: #7aecec;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-        <span
-            style="
-                background: #7aecec;
-                top: 40px;
-                height: 4px;
-                border-top-left-radius: 3px;
-                border-bottom-left-radius: 3px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-            <span
-                style="
-                    background: #7aecec;
-                    color: #000;
-                    top: -0.5em;
-                    padding: 2px 3px;
-                    position: absolute;
-                    font-size: 0.6em;
-                    font-weight: bold;
-                    line-height: 1;
-                    border-radius: 3px;
-                "
-            >
-                ORG
-            </span>
-        </span>
-    </span>
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        of
-
-        <span
-            style="
-                background: #7aecec;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-    </span>
-    <span style="font-weight: bold; display: inline-block; position: relative">
-        China
-        <span
-            style="
-                background: #7aecec;
-                top: 40px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-        <span
-            style="
-                background: #feca74;
-                top: 57px;
-                height: 4px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-        </span>
-        <span
-            style="
-                background: #feca74;
-                top: 57px;
-                height: 4px;
-                border-top-left-radius: 3px;
-                border-bottom-left-radius: 3px;
-                left: -1px;
-                width: calc(100% + 2px);
-                position: absolute;
-            "
-        >
-            <span
-                style="
-                    background: #feca74;
-                    color: #000;
-                    top: -0.5em;
-                    padding: 2px 3px;
-                    position: absolute;
-                    font-size: 0.6em;
-                    font-weight: bold;
-                    line-height: 1;
-                    border-radius: 3px;
-                "
-            >
-                GPE
-            </span>
-        </span>
-    </span>
-    .
-</div>

website/src/components/embed.js

@@ -107,6 +107,22 @@ const Image = ({ src, alt, title, href, ...props }) => {
     )
 }
 
+const ImageScrollable = ({ src, alt, width, ...props }) => {
+    return (
+        <figure className={classNames(classes.standalone, classes.scrollable)}>
+            <img className={classes['image-scrollable']} src={src} alt={alt} width={width} height="auto" />
+        </figure>
+    )
+}
+
+const Standalone = ({ height, children, ...props }) => {
+    return (
+        <figure className={classes.standalone} style={{ height }}>
+            {children}
+        </figure>
+    )
+}
+
 const ImageFill = ({ image, ...props }) => {
     return (
         <span
@@ -137,4 +153,4 @@ const GoogleSheet = ({ id, link, height, button = 'View full table' }) => {
     )
 }
 
-export { YouTube, SoundCloud, Iframe, Image, ImageFill, GoogleSheet }
+export { YouTube, SoundCloud, Iframe, Image, ImageFill, ImageScrollable, GoogleSheet, Standalone }

website/src/remark.js

@@ -13,7 +13,7 @@ import Aside from './components/aside'
 import Button from './components/button'
 import Tag from './components/tag'
 import Grid from './components/grid'
-import { YouTube, SoundCloud, Iframe, Image, GoogleSheet } from './components/embed'
+import { YouTube, SoundCloud, Iframe, Image, ImageScrollable, GoogleSheet, Standalone } from './components/embed'
 import Project from './widgets/project'
 import { Integration, IntegrationLogo } from './widgets/integration.js'
 import { Logos, Colors, Patterns } from './widgets/styleguide'
@@ -90,6 +90,8 @@ export const remarkComponents = {
      * For regular img elements it is not possible to pass properties
      */
     Image,
+    ImageScrollable,
+    Standalone,
 
     Label,
     Logos,

website/src/styles/embed.module.sass

@@ -26,12 +26,20 @@
     padding: var(--spacing-xs)
     margin-bottom: var(--spacing-md)
 
+.scrollable
+    max-width: 100%
+    overflow: auto
+
 .image
     position: relative
     display: block
     max-width: 100%
     margin: 0 auto
 
+.image-scrollable
+    display: block
+    max-width: fit-content
+
 .figure-fill
     display: block
     position: relative

website/src/templates/index.js

@@ -58,8 +58,8 @@ const AlertSpace = ({ nightly, legacy }) => {
 }
 
 const navAlert = (
-    <Link to="/usage/v3-6" noLinkLayout>
+    <Link to="https://form.typeform.com/to/WlflqP1b" noLinkLayout>
-        <strong>💥 Out now:</strong> spaCy v3.6
+        💥 Interested in <strong>Premium spaCy Models</strong>?
     </Link>
 )