diff --git a/.gitignore b/.gitignore
index ac333f958..af75a4d47 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,16 +10,6 @@ spacy/tests/package/setup.cfg
spacy/tests/package/pyproject.toml
spacy/tests/package/requirements.txt
-# Website
-website/.cache/
-website/public/
-website/node_modules
-website/.npm
-website/logs
-*.log
-npm-debug.log*
-quickstart-training-generator.js
-
# Cython / C extensions
cythonize.json
spacy/*.html
diff --git a/README.md b/README.md
index 842a5d839..bf8083e0e 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ production-ready [**training system**](https://spacy.io/usage/training) and easy
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).
-π« **Version 3.4 out now!**
+π« **Version 3.5 out now!**
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
[](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 99f1b8aff..a6a575315 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -11,18 +11,28 @@ trigger:
exclude:
- "website/*"
- "*.md"
+ - "*.mdx"
- ".github/workflows/*"
pr:
paths:
exclude:
- "*.md"
+ - "*.mdx"
- "website/docs/*"
- "website/src/*"
+ - "website/meta/*.tsx"
+ - "website/meta/*.mjs"
+ - "website/meta/languages.json"
+ - "website/meta/site.json"
+ - "website/meta/sidebars.json"
+ - "website/meta/type-annotations.json"
+ - "website/pages/*"
- ".github/workflows/*"
jobs:
- # Perform basic checks for most important errors (syntax etc.) Uses the config
- # defined in .flake8 and overwrites the selected codes.
+ # Check formatting and linting. Perform basic checks for most important errors
+ # (syntax etc.) Uses the config defined in setup.cfg and overwrites the
+ # selected codes.
- job: "Validate"
pool:
vmImage: "ubuntu-latest"
@@ -30,6 +40,10 @@ jobs:
- task: UsePythonVersion@0
inputs:
versionSpec: "3.8"
+ - script: |
+ pip install black==22.3.0
+ python -m black spacy --check
+ displayName: "black"
- script: |
pip install flake8==5.0.4
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
diff --git a/spacy/cli/__init__.py b/spacy/cli/__init__.py
index aabd1cfef..868526b42 100644
--- a/spacy/cli/__init__.py
+++ b/spacy/cli/__init__.py
@@ -4,6 +4,7 @@ from ._util import app, setup_cli # noqa: F401
# These are the actual functions, NOT the wrapped CLI commands. The CLI commands
# are registered automatically and won't have to be imported here.
+from .benchmark_speed import benchmark_speed_cli # noqa: F401
from .download import download # noqa: F401
from .info import info # noqa: F401
from .package import package # noqa: F401
diff --git a/spacy/cli/_util.py b/spacy/cli/_util.py
index 6dd3eadfc..eb4869666 100644
--- a/spacy/cli/_util.py
+++ b/spacy/cli/_util.py
@@ -45,6 +45,7 @@ DEBUG_HELP = """Suite of helpful commands for debugging and profiling. Includes
commands to check and validate your config files, training and evaluation data,
and custom model implementations.
"""
+BENCHMARK_HELP = """Commands for benchmarking pipelines."""
INIT_HELP = """Commands for initializing configs and pipeline packages."""
# Wrappers for Typer's annotations. Initially created to set defaults and to
@@ -53,12 +54,14 @@ Arg = typer.Argument
Opt = typer.Option
app = typer.Typer(name=NAME, help=HELP)
+benchmark_cli = typer.Typer(name="benchmark", help=BENCHMARK_HELP, no_args_is_help=True)
project_cli = typer.Typer(name="project", help=PROJECT_HELP, no_args_is_help=True)
debug_cli = typer.Typer(name="debug", help=DEBUG_HELP, no_args_is_help=True)
init_cli = typer.Typer(name="init", help=INIT_HELP, no_args_is_help=True)
app.add_typer(project_cli)
app.add_typer(debug_cli)
+app.add_typer(benchmark_cli)
app.add_typer(init_cli)
diff --git a/spacy/cli/benchmark_speed.py b/spacy/cli/benchmark_speed.py
new file mode 100644
index 000000000..4eb20a5fa
--- /dev/null
+++ b/spacy/cli/benchmark_speed.py
@@ -0,0 +1,174 @@
+from typing import Iterable, List, Optional
+import random
+from itertools import islice
+import numpy
+from pathlib import Path
+import time
+from tqdm import tqdm
+import typer
+from wasabi import msg
+
+from .. import util
+from ..language import Language
+from ..tokens import Doc
+from ..training import Corpus
+from ._util import Arg, Opt, benchmark_cli, setup_gpu
+
+
+@benchmark_cli.command(
+ "speed",
+ context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
+)
+def benchmark_speed_cli(
+ # fmt: off
+ ctx: typer.Context,
+ model: str = Arg(..., help="Model name or path"),
+ data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
+ batch_size: Optional[int] = Opt(None, "--batch-size", "-b", min=1, help="Override the pipeline batch size"),
+ no_shuffle: bool = Opt(False, "--no-shuffle", help="Do not shuffle benchmark data"),
+ use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
+ n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
+ warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
+ # fmt: on
+):
+ """
+ Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
+ data in the binary .spacy format.
+ """
+ setup_gpu(use_gpu=use_gpu, silent=False)
+
+ nlp = util.load_model(model)
+ batch_size = batch_size if batch_size is not None else nlp.batch_size
+ corpus = Corpus(data_path)
+ docs = [eg.predicted for eg in corpus(nlp)]
+
+ if len(docs) == 0:
+ msg.fail("Cannot benchmark speed using an empty corpus.", exits=1)
+
+ print(f"Warming up for {warmup_epochs} epochs...")
+ warmup(nlp, docs, warmup_epochs, batch_size)
+
+ print()
+ print(f"Benchmarking {n_batches} batches...")
+ wps = benchmark(nlp, docs, n_batches, batch_size, not no_shuffle)
+
+ print()
+ print_outliers(wps)
+ print_mean_with_ci(wps)
+
+
+# Lowercased, behaves as a context manager function.
+class time_context:
+ """Register the running time of a context."""
+
+ def __enter__(self):
+ self.start = time.perf_counter()
+ return self
+
+ def __exit__(self, type, value, traceback):
+ self.elapsed = time.perf_counter() - self.start
+
+
+class Quartiles:
+ """Calculate the q1, q2, q3 quartiles and the inter-quartile range (iqr)
+ of a sample."""
+
+ q1: float
+ q2: float
+ q3: float
+ iqr: float
+
+ def __init__(self, sample: numpy.ndarray) -> None:
+ self.q1 = numpy.quantile(sample, 0.25)
+ self.q2 = numpy.quantile(sample, 0.5)
+ self.q3 = numpy.quantile(sample, 0.75)
+ self.iqr = self.q3 - self.q1
+
+
+def annotate(
+ nlp: Language, docs: List[Doc], batch_size: Optional[int]
+) -> numpy.ndarray:
+ docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size)
+ wps = []
+ while True:
+ with time_context() as elapsed:
+ batch_docs = list(
+ islice(docs, batch_size if batch_size else nlp.batch_size)
+ )
+ if len(batch_docs) == 0:
+ break
+ n_tokens = count_tokens(batch_docs)
+ wps.append(n_tokens / elapsed.elapsed)
+
+ return numpy.array(wps)
+
+
+def benchmark(
+ nlp: Language,
+ docs: List[Doc],
+ n_batches: int,
+ batch_size: int,
+ shuffle: bool,
+) -> numpy.ndarray:
+ if shuffle:
+ bench_docs = [
+ nlp.make_doc(random.choice(docs).text)
+ for _ in range(n_batches * batch_size)
+ ]
+ else:
+ bench_docs = [
+ nlp.make_doc(docs[i % len(docs)].text)
+ for i in range(n_batches * batch_size)
+ ]
+
+ return annotate(nlp, bench_docs, batch_size)
+
+
+def bootstrap(x, statistic=numpy.mean, iterations=10000) -> numpy.ndarray:
+ """Apply a statistic to repeated random samples of an array."""
+ return numpy.fromiter(
+ (
+ statistic(numpy.random.choice(x, len(x), replace=True))
+ for _ in range(iterations)
+ ),
+ numpy.float64,
+ )
+
+
+def count_tokens(docs: Iterable[Doc]) -> int:
+ return sum(len(doc) for doc in docs)
+
+
+def print_mean_with_ci(sample: numpy.ndarray):
+ mean = numpy.mean(sample)
+ bootstrap_means = bootstrap(sample)
+ bootstrap_means.sort()
+
+ # 95% confidence interval
+ low = bootstrap_means[int(len(bootstrap_means) * 0.025)]
+ high = bootstrap_means[int(len(bootstrap_means) * 0.975)]
+
+ print(f"Mean: {mean:.1f} words/s (95% CI: {low-mean:.1f} +{high-mean:.1f})")
+
+
+def print_outliers(sample: numpy.ndarray):
+ quartiles = Quartiles(sample)
+
+ n_outliers = numpy.sum(
+ (sample < (quartiles.q1 - 1.5 * quartiles.iqr))
+ | (sample > (quartiles.q3 + 1.5 * quartiles.iqr))
+ )
+ n_extreme_outliers = numpy.sum(
+ (sample < (quartiles.q1 - 3.0 * quartiles.iqr))
+ | (sample > (quartiles.q3 + 3.0 * quartiles.iqr))
+ )
+ print(
+ f"Outliers: {(100 * n_outliers) / len(sample):.1f}%, extreme outliers: {(100 * n_extreme_outliers) / len(sample)}%"
+ )
+
+
+def warmup(
+ nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
+) -> numpy.ndarray:
+ docs = warmup_epochs * docs
+ return annotate(nlp, docs, batch_size)
diff --git a/spacy/cli/debug_data.py b/spacy/cli/debug_data.py
index 0df1049e5..1c242cec8 100644
--- a/spacy/cli/debug_data.py
+++ b/spacy/cli/debug_data.py
@@ -17,6 +17,7 @@ from ..pipeline import TrainablePipe
from ..pipeline._parser_internals import nonproj
from ..pipeline._parser_internals.nonproj import DELIMITER
from ..pipeline import Morphologizer, SpanCategorizer
+from ..pipeline._edit_tree_internals.edit_trees import EditTrees
from ..morphology import Morphology
from ..language import Language
from ..util import registry, resolve_dot_names
@@ -670,6 +671,59 @@ def debug_data(
f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
)
+ if "trainable_lemmatizer" in factory_names:
+ msg.divider("Trainable Lemmatizer")
+ trees_train: Set[str] = gold_train_data["lemmatizer_trees"]
+ trees_dev: Set[str] = gold_dev_data["lemmatizer_trees"]
+ # This is necessary context when someone is attempting to interpret whether the
+ # number of trees exclusively in the dev set is meaningful.
+ msg.info(f"{len(trees_train)} lemmatizer trees generated from training data")
+ msg.info(f"{len(trees_dev)} lemmatizer trees generated from dev data")
+ dev_not_train = trees_dev - trees_train
+
+ if len(dev_not_train) != 0:
+ pct = len(dev_not_train) / len(trees_dev)
+ msg.info(
+ f"{len(dev_not_train)} lemmatizer trees ({pct*100:.1f}% of dev trees)"
+ " were found exclusively in the dev data."
+ )
+ else:
+ # Would we ever expect this case? It seems like it would be pretty rare,
+ # and we might actually want a warning?
+ msg.info("All trees in dev data present in training data.")
+
+ if gold_train_data["n_low_cardinality_lemmas"] > 0:
+ n = gold_train_data["n_low_cardinality_lemmas"]
+ msg.warn(f"{n} training docs with 0 or 1 unique lemmas.")
+
+ if gold_dev_data["n_low_cardinality_lemmas"] > 0:
+ n = gold_dev_data["n_low_cardinality_lemmas"]
+ msg.warn(f"{n} dev docs with 0 or 1 unique lemmas.")
+
+ if gold_train_data["no_lemma_annotations"] > 0:
+ n = gold_train_data["no_lemma_annotations"]
+ msg.warn(f"{n} training docs with no lemma annotations.")
+ else:
+ msg.good("All training docs have lemma annotations.")
+
+ if gold_dev_data["no_lemma_annotations"] > 0:
+ n = gold_dev_data["no_lemma_annotations"]
+ msg.warn(f"{n} dev docs with no lemma annotations.")
+ else:
+ msg.good("All dev docs have lemma annotations.")
+
+ if gold_train_data["partial_lemma_annotations"] > 0:
+ n = gold_train_data["partial_lemma_annotations"]
+ msg.info(f"{n} training docs with partial lemma annotations.")
+ else:
+ msg.good("All training docs have complete lemma annotations.")
+
+ if gold_dev_data["partial_lemma_annotations"] > 0:
+ n = gold_dev_data["partial_lemma_annotations"]
+ msg.info(f"{n} dev docs with partial lemma annotations.")
+ else:
+ msg.good("All dev docs have complete lemma annotations.")
+
msg.divider("Summary")
good_counts = msg.counts[MESSAGES.GOOD]
warn_counts = msg.counts[MESSAGES.WARN]
@@ -731,7 +785,13 @@ def _compile_gold(
"n_cats_multilabel": 0,
"n_cats_bad_values": 0,
"texts": set(),
+ "lemmatizer_trees": set(),
+ "no_lemma_annotations": 0,
+ "partial_lemma_annotations": 0,
+ "n_low_cardinality_lemmas": 0,
}
+ if "trainable_lemmatizer" in factory_names:
+ trees = EditTrees(nlp.vocab.strings)
for eg in examples:
gold = eg.reference
doc = eg.predicted
@@ -861,6 +921,25 @@ def _compile_gold(
data["n_nonproj"] += 1
if nonproj.contains_cycle(aligned_heads):
data["n_cycles"] += 1
+ if "trainable_lemmatizer" in factory_names:
+ # from EditTreeLemmatizer._labels_from_data
+ if all(token.lemma == 0 for token in gold):
+ data["no_lemma_annotations"] += 1
+ continue
+ if any(token.lemma == 0 for token in gold):
+ data["partial_lemma_annotations"] += 1
+ lemma_set = set()
+ for token in gold:
+ if token.lemma != 0:
+ lemma_set.add(token.lemma)
+ tree_id = trees.add(token.text, token.lemma_)
+ tree_str = trees.tree_to_str(tree_id)
+ data["lemmatizer_trees"].add(tree_str)
+ # We want to identify cases where lemmas aren't assigned
+ # or are all assigned the same value, as this would indicate
+ # an issue since we're expecting a large set of lemmas
+ if len(lemma_set) < 2 and len(gold) > 1:
+ data["n_low_cardinality_lemmas"] += 1
return data
diff --git a/spacy/cli/evaluate.py b/spacy/cli/evaluate.py
index 0d08d2c5e..8f3d6b859 100644
--- a/spacy/cli/evaluate.py
+++ b/spacy/cli/evaluate.py
@@ -7,12 +7,15 @@ from thinc.api import fix_random_seed
from ..training import Corpus
from ..tokens import Doc
-from ._util import app, Arg, Opt, setup_gpu, import_code
+from ._util import app, Arg, Opt, setup_gpu, import_code, benchmark_cli
from ..scorer import Scorer
from .. import util
from .. import displacy
+@benchmark_cli.command(
+ "accuracy",
+)
@app.command("evaluate")
def evaluate_cli(
# fmt: off
@@ -36,7 +39,7 @@ def evaluate_cli(
dependency parses in a HTML file, set as output directory as the
displacy_path argument.
- DOCS: https://spacy.io/api/cli#evaluate
+ DOCS: https://spacy.io/api/cli#benchmark-accuracy
"""
import_code(code_path)
evaluate(
diff --git a/spacy/displacy/__init__.py b/spacy/displacy/__init__.py
index a3cfd96dd..ea6bba2c9 100644
--- a/spacy/displacy/__init__.py
+++ b/spacy/displacy/__init__.py
@@ -106,9 +106,7 @@ def serve(
if is_in_jupyter():
warnings.warn(Warnings.W011)
- render(
- docs, style=style, page=page, minify=minify, options=options, manual=manual
- )
+ render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
httpd = simple_server.make_server(host, port, app)
print(f"\nUsing the '{style}' visualizer")
print(f"Serving on http://{host}:{port} ...\n")
diff --git a/spacy/errors.py b/spacy/errors.py
index 18811b725..5f480c16c 100644
--- a/spacy/errors.py
+++ b/spacy/errors.py
@@ -949,8 +949,8 @@ class Errors(metaclass=ErrorsWithCodes):
E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.")
E1048 = ("Got '{unexpected}' as console progress bar type, but expected one of the following: {expected}")
E1049 = ("No available port found for displaCy on host {host}. Please specify an available port "
- "with `displacy.serve(doc, port)`")
- E1050 = ("Port {port} is already in use. Please specify an available port with `displacy.serve(doc, port)` "
+ "with `displacy.serve(doc, port=port)`")
+ E1050 = ("Port {port} is already in use. Please specify an available port with `displacy.serve(doc, port=port)` "
"or use `auto_switch_port=True` to pick an available port automatically.")
# v4 error strings
diff --git a/spacy/kb/kb_in_memory.pyx b/spacy/kb/kb_in_memory.pyx
index 485e52c2f..edba523cf 100644
--- a/spacy/kb/kb_in_memory.pyx
+++ b/spacy/kb/kb_in_memory.pyx
@@ -25,7 +25,7 @@ cdef class InMemoryLookupKB(KnowledgeBase):
"""An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
- DOCS: https://spacy.io/api/kb_in_memory
+ DOCS: https://spacy.io/api/inmemorylookupkb
"""
def __init__(self, Vocab vocab, entity_vector_length):
diff --git a/spacy/matcher/levenshtein.pyx b/spacy/matcher/levenshtein.pyx
index 0e8cd26da..e823ce99d 100644
--- a/spacy/matcher/levenshtein.pyx
+++ b/spacy/matcher/levenshtein.pyx
@@ -22,7 +22,7 @@ cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int =
max_edits = fuzzy
else:
# allow at least two edits (to allow at least one transposition) and up
- # to 20% of the pattern string length
+ # to 30% of the pattern string length
max_edits = max(2, round(0.3 * len(pattern_text)))
return levenshtein(input_text, pattern_text, max_edits) <= max_edits
diff --git a/spacy/matcher/matcher.pyi b/spacy/matcher/matcher.pyi
index ad214c120..9797463aa 100644
--- a/spacy/matcher/matcher.pyi
+++ b/spacy/matcher/matcher.pyi
@@ -4,8 +4,12 @@ from ..vocab import Vocab
from ..tokens import Doc, Span
class Matcher:
- def __init__(self, vocab: Vocab, validate: bool = ...,
- fuzzy_compare: Callable[[str, str, int], bool] = ...) -> None: ...
+ def __init__(
+ self,
+ vocab: Vocab,
+ validate: bool = ...,
+ fuzzy_compare: Callable[[str, str, int], bool] = ...,
+ ) -> None: ...
def __reduce__(self) -> Any: ...
def __len__(self) -> int: ...
def __contains__(self, key: str) -> bool: ...
diff --git a/spacy/pipeline/edit_tree_lemmatizer.py b/spacy/pipeline/edit_tree_lemmatizer.py
index 20f83fffc..3198b7509 100644
--- a/spacy/pipeline/edit_tree_lemmatizer.py
+++ b/spacy/pipeline/edit_tree_lemmatizer.py
@@ -5,7 +5,7 @@ from itertools import islice
import numpy as np
import srsly
-from thinc.api import Config, Model
+from thinc.api import Config, Model, SequenceCategoricalCrossentropy, NumpyOps
from thinc.types import ArrayXd, Floats2d, Ints1d
from thinc.legacy import LegacySequenceCategoricalCrossentropy
@@ -22,6 +22,8 @@ from .. import util
ActivationsT = Dict[str, Union[List[Floats2d], List[Ints1d]]]
+# The cutoff value of *top_k* above which an alternative method is used to process guesses.
+TOP_K_GUARDRAIL = 20
default_model_config = """
@@ -125,6 +127,7 @@ class EditTreeLemmatizer(TrainablePipe):
self.cfg: Dict[str, Any] = {"labels": []}
self.scorer = scorer
self.save_activations = save_activations
+ self.numpy_ops = NumpyOps()
def get_loss(
self, examples: Iterable[Example], scores: List[Floats2d]
@@ -140,7 +143,7 @@ class EditTreeLemmatizer(TrainablePipe):
for (predicted, gold_lemma) in zip(
eg.predicted, eg.get_aligned("LEMMA", as_string=True)
):
- if gold_lemma is None:
+ if gold_lemma is None or gold_lemma == "":
label = -1
else:
tree_id = self.trees.add(predicted.text, gold_lemma)
@@ -165,7 +168,7 @@ class EditTreeLemmatizer(TrainablePipe):
student_scores: Scores representing the student model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
-
+
DOCS: https://spacy.io/api/edittreelemmatizer#get_teacher_student_loss
"""
loss_func = LegacySequenceCategoricalCrossentropy(normalize=False)
@@ -175,6 +178,18 @@ class EditTreeLemmatizer(TrainablePipe):
return float(loss), d_scores
def predict(self, docs: Iterable[Doc]) -> ActivationsT:
+ if self.top_k == 1:
+ scores2guesses = self._scores2guesses_top_k_equals_1
+ elif self.top_k <= TOP_K_GUARDRAIL:
+ scores2guesses = self._scores2guesses_top_k_greater_1
+ else:
+ scores2guesses = self._scores2guesses_top_k_guardrail
+ # The behaviour of *_scores2guesses_top_k_greater_1()* is efficient for values
+ # of *top_k>1* that are likely to be useful when the edit tree lemmatizer is used
+ # for its principal purpose of lemmatizing tokens. However, the code could also
+ # be used for other purposes, and with very large values of *top_k* the method
+ # becomes inefficient. In such cases, *_scores2guesses_top_k_guardrail()* is used
+ # instead.
n_docs = len(list(docs))
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
@@ -189,20 +204,52 @@ class EditTreeLemmatizer(TrainablePipe):
return {"probabilities": scores, "tree_ids": guesses}
scores = self.model.predict(docs)
assert len(scores) == n_docs
- guesses = self._scores2guesses(docs, scores)
+ guesses = scores2guesses(docs, scores)
assert len(guesses) == n_docs
return {"probabilities": scores, "tree_ids": guesses}
- def _scores2guesses(self, docs, scores):
+ def _scores2guesses_top_k_equals_1(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
- if self.top_k == 1:
- doc_guesses = doc_scores.argmax(axis=1).reshape(-1, 1)
- else:
- doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
+ doc_guesses = doc_scores.argmax(axis=1)
+ doc_guesses = self.numpy_ops.asarray(doc_guesses)
- if not isinstance(doc_guesses, np.ndarray):
- doc_guesses = doc_guesses.get()
+ doc_compat_guesses = []
+ for i, token in enumerate(doc):
+ tree_id = self.cfg["labels"][doc_guesses[i]]
+ if self.trees.apply(tree_id, token.text) is not None:
+ doc_compat_guesses.append(tree_id)
+ else:
+ doc_compat_guesses.append(-1)
+ guesses.append(np.array(doc_compat_guesses))
+
+ return guesses
+
+ def _scores2guesses_top_k_greater_1(self, docs, scores):
+ guesses = []
+ top_k = min(self.top_k, len(self.labels))
+ for doc, doc_scores in zip(docs, scores):
+ doc_scores = self.numpy_ops.asarray(doc_scores)
+ doc_compat_guesses = []
+ for i, token in enumerate(doc):
+ for _ in range(top_k):
+ candidate = int(doc_scores[i].argmax())
+ candidate_tree_id = self.cfg["labels"][candidate]
+ if self.trees.apply(candidate_tree_id, token.text) is not None:
+ doc_compat_guesses.append(candidate_tree_id)
+ break
+ doc_scores[i, candidate] = np.finfo(np.float32).min
+ else:
+ doc_compat_guesses.append(-1)
+ guesses.append(np.array(doc_compat_guesses))
+
+ return guesses
+
+ def _scores2guesses_top_k_guardrail(self, docs, scores):
+ guesses = []
+ for doc, doc_scores in zip(docs, scores):
+ doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
+ doc_guesses = self.numpy_ops.asarray(doc_guesses)
doc_compat_guesses = []
for token, candidates in zip(doc, doc_guesses):
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index 19c355238..fa4dea75a 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -453,7 +453,11 @@ class EntityLinker(TrainablePipe):
docs_ents: List[Ragged] = []
docs_scores: List[Ragged] = []
if not docs:
- return {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores}
+ return {
+ KNOWLEDGE_BASE_IDS: final_kb_ids,
+ "ents": docs_ents,
+ "scores": docs_scores,
+ }
if isinstance(docs, Doc):
docs = [docs]
for doc in docs:
@@ -585,7 +589,11 @@ class EntityLinker(TrainablePipe):
method="predict", msg="result variables not of equal length"
)
raise RuntimeError(err)
- return {KNOWLEDGE_BASE_IDS: final_kb_ids, "ents": docs_ents, "scores": docs_scores}
+ return {
+ KNOWLEDGE_BASE_IDS: final_kb_ids,
+ "ents": docs_ents,
+ "scores": docs_scores,
+ }
def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
"""Modify a batch of documents, using pre-computed scores.
diff --git a/spacy/pipeline/ner.py b/spacy/pipeline/ner.py
index 651a0b3e3..7e44b2835 100644
--- a/spacy/pipeline/ner.py
+++ b/spacy/pipeline/ner.py
@@ -252,8 +252,11 @@ class EntityRecognizer(Parser):
def labels(self):
# Get the labels from the model by looking at the available moves, e.g.
# B-PERSON, I-PERSON, L-PERSON, U-PERSON
- labels = set(remove_bilu_prefix(move) for move in self.move_names
- if move[0] in ("B", "I", "L", "U"))
+ labels = set(
+ remove_bilu_prefix(move)
+ for move in self.move_names
+ if move[0] in ("B", "I", "L", "U")
+ )
return tuple(sorted(labels))
def scored_ents(self, beams):
diff --git a/spacy/schemas.py b/spacy/schemas.py
index aea3cc4f7..c8467fea8 100644
--- a/spacy/schemas.py
+++ b/spacy/schemas.py
@@ -162,15 +162,33 @@ class TokenPatternString(BaseModel):
IS_SUPERSET: Optional[List[StrictStr]] = Field(None, alias="is_superset")
INTERSECTS: Optional[List[StrictStr]] = Field(None, alias="intersects")
FUZZY: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy")
- FUZZY1: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy1")
- FUZZY2: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy2")
- FUZZY3: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy3")
- FUZZY4: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy4")
- FUZZY5: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy5")
- FUZZY6: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy6")
- FUZZY7: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy7")
- FUZZY8: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy8")
- FUZZY9: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy9")
+ FUZZY1: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy1"
+ )
+ FUZZY2: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy2"
+ )
+ FUZZY3: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy3"
+ )
+ FUZZY4: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy4"
+ )
+ FUZZY5: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy5"
+ )
+ FUZZY6: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy6"
+ )
+ FUZZY7: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy7"
+ )
+ FUZZY8: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy8"
+ )
+ FUZZY9: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
+ None, alias="fuzzy9"
+ )
class Config:
extra = "forbid"
diff --git a/spacy/tests/pipeline/test_edit_tree_lemmatizer.py b/spacy/tests/pipeline/test_edit_tree_lemmatizer.py
index b855c7a26..c5c50c77f 100644
--- a/spacy/tests/pipeline/test_edit_tree_lemmatizer.py
+++ b/spacy/tests/pipeline/test_edit_tree_lemmatizer.py
@@ -103,14 +103,15 @@ def test_initialize_from_labels():
}
-def test_no_data():
+@pytest.mark.parametrize("top_k", (1, 5, 30))
+def test_no_data(top_k):
# Test that the lemmatizer provides a nice error when there's no tagging data / labels
TEXTCAT_DATA = [
("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
]
nlp = English()
- nlp.add_pipe("trainable_lemmatizer")
+ nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp.add_pipe("textcat")
train_examples = []
@@ -121,10 +122,11 @@ def test_no_data():
nlp.initialize(get_examples=lambda: train_examples)
-def test_incomplete_data():
+@pytest.mark.parametrize("top_k", (1, 5, 30))
+def test_incomplete_data(top_k):
# Test that the lemmatizer works with incomplete information
nlp = English()
- lemmatizer = nlp.add_pipe("trainable_lemmatizer")
+ lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in PARTIAL_DATA:
@@ -141,10 +143,25 @@ def test_incomplete_data():
assert doc[1].lemma_ == "like"
assert doc[2].lemma_ == "blue"
+ # Check that incomplete annotations are ignored.
+ scores, _ = lemmatizer.model([eg.predicted for eg in train_examples], is_train=True)
+ _, dX = lemmatizer.get_loss(train_examples, scores)
+ xp = lemmatizer.model.ops.xp
-def test_overfitting_IO():
+ # Missing annotations.
+ assert xp.count_nonzero(dX[0][0]) == 0
+ assert xp.count_nonzero(dX[0][3]) == 0
+ assert xp.count_nonzero(dX[1][0]) == 0
+ assert xp.count_nonzero(dX[1][3]) == 0
+
+ # Misaligned annotations.
+ assert xp.count_nonzero(dX[1][1]) == 0
+
+
+@pytest.mark.parametrize("top_k", (1, 5, 30))
+def test_overfitting_IO(top_k):
nlp = English()
- lemmatizer = nlp.add_pipe("trainable_lemmatizer")
+ lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in TRAIN_DATA:
@@ -177,7 +194,7 @@ def test_overfitting_IO():
# Check model after a {to,from}_bytes roundtrip
nlp_bytes = nlp.to_bytes()
nlp3 = English()
- nlp3.add_pipe("trainable_lemmatizer")
+ nlp3.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp3.from_bytes(nlp_bytes)
doc3 = nlp3(test_text)
assert doc3[0].lemma_ == "she"
diff --git a/spacy/tests/test_cli.py b/spacy/tests/test_cli.py
index c88e20de2..42ffae22d 100644
--- a/spacy/tests/test_cli.py
+++ b/spacy/tests/test_cli.py
@@ -618,7 +618,6 @@ def test_string_to_list_intify(value):
assert string_to_list(value, intify=True) == [1, 2, 3]
-@pytest.mark.skip(reason="Temporarily skip for dev version")
def test_download_compatibility():
spec = SpecifierSet("==" + about.__version__)
spec.prereleases = False
@@ -629,7 +628,6 @@ def test_download_compatibility():
assert get_minor_version(about.__version__) == get_minor_version(version)
-@pytest.mark.skip(reason="Temporarily skip for dev version")
def test_validate_compatibility_table():
spec = SpecifierSet("==" + about.__version__)
spec.prereleases = False
@@ -1076,7 +1074,7 @@ def test_cli_find_threshold(capsys):
)
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
- res = find_threshold(
+ best_threshold, best_score, res = find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="tc_multi",
@@ -1084,10 +1082,10 @@ def test_cli_find_threshold(capsys):
scores_key="cats_macro_f",
silent=True,
)
- assert res[0] != thresholds[0]
- assert thresholds[0] < res[0] < thresholds[9]
- assert res[1] == 1.0
- assert res[2][1.0] == 0.0
+ assert best_threshold != thresholds[0]
+ assert thresholds[0] < best_threshold < thresholds[9]
+ assert best_score == max(res.values())
+ assert res[1.0] == 0.0
# Test with spancat.
nlp, _ = init_nlp((("spancat", {}),))
@@ -1209,3 +1207,69 @@ def test_walk_directory():
assert (len(walk_directory(d, suffix="iob"))) == 2
assert (len(walk_directory(d, suffix="conll"))) == 3
assert (len(walk_directory(d, suffix="pdf"))) == 0
+
+
+def test_debug_data_trainable_lemmatizer_basic():
+ examples = [
+ ("She likes green eggs", {"lemmas": ["she", "like", "green", "egg"]}),
+ ("Eat blue ham", {"lemmas": ["eat", "blue", "ham"]}),
+ ]
+ nlp = Language()
+ train_examples = []
+ for t in examples:
+ train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+ data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
+ # ref test_edit_tree_lemmatizer::test_initialize_from_labels
+ # this results in 4 trees
+ assert len(data["lemmatizer_trees"]) == 4
+
+
+def test_debug_data_trainable_lemmatizer_partial():
+ partial_examples = [
+ # partial annotation
+ ("She likes green eggs", {"lemmas": ["", "like", "green", ""]}),
+ # misaligned partial annotation
+ (
+ "He hates green eggs",
+ {
+ "words": ["He", "hat", "es", "green", "eggs"],
+ "lemmas": ["", "hat", "e", "green", ""],
+ },
+ ),
+ ]
+ nlp = Language()
+ train_examples = []
+ for t in partial_examples:
+ train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+ data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
+ assert data["partial_lemma_annotations"] == 2
+
+
+def test_debug_data_trainable_lemmatizer_low_cardinality():
+ low_cardinality_examples = [
+ ("She likes green eggs", {"lemmas": ["no", "no", "no", "no"]}),
+ ("Eat blue ham", {"lemmas": ["no", "no", "no"]}),
+ ]
+ nlp = Language()
+ train_examples = []
+ for t in low_cardinality_examples:
+ train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+ data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
+ assert data["n_low_cardinality_lemmas"] == 2
+
+
+def test_debug_data_trainable_lemmatizer_not_annotated():
+ unannotated_examples = [
+ ("She likes green eggs", {}),
+ ("Eat blue ham", {}),
+ ]
+ nlp = Language()
+ train_examples = []
+ for t in unannotated_examples:
+ train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
+
+ data = _compile_gold(train_examples, ["trainable_lemmatizer"], nlp, True)
+ assert data["no_lemma_annotations"] == 2
diff --git a/spacy/tests/test_cli_app.py b/spacy/tests/test_cli_app.py
index 873a3ff66..80da5a447 100644
--- a/spacy/tests/test_cli_app.py
+++ b/spacy/tests/test_cli_app.py
@@ -1,6 +1,7 @@
import os
from pathlib import Path
from typer.testing import CliRunner
+from spacy.tokens import DocBin, Doc
from spacy.cli._util import app
from .util import make_tempdir
@@ -31,3 +32,60 @@ def test_convert_auto_conflict():
assert "All input files must be same type" in result.stdout
out_files = os.listdir(d_out)
assert len(out_files) == 0
+
+
+def test_benchmark_accuracy_alias():
+ # Verify that the `evaluate` alias works correctly.
+ result_benchmark = CliRunner().invoke(app, ["benchmark", "accuracy", "--help"])
+ result_evaluate = CliRunner().invoke(app, ["evaluate", "--help"])
+ assert result_benchmark.stdout == result_evaluate.stdout.replace(
+ "spacy evaluate", "spacy benchmark accuracy"
+ )
+
+
+def test_debug_data_trainable_lemmatizer_cli(en_vocab):
+ train_docs = [
+ Doc(en_vocab, words=["I", "like", "cats"], lemmas=["I", "like", "cat"]),
+ Doc(
+ en_vocab,
+ words=["Dogs", "are", "great", "too"],
+ lemmas=["dog", "be", "great", "too"],
+ ),
+ ]
+ dev_docs = [
+ Doc(en_vocab, words=["Cats", "are", "cute"], lemmas=["cat", "be", "cute"]),
+ Doc(en_vocab, words=["Pets", "are", "great"], lemmas=["pet", "be", "great"]),
+ ]
+ with make_tempdir() as d_in:
+ train_bin = DocBin(docs=train_docs)
+ train_bin.to_disk(d_in / "train.spacy")
+ dev_bin = DocBin(docs=dev_docs)
+ dev_bin.to_disk(d_in / "dev.spacy")
+ # `debug data` requires an input pipeline config
+ CliRunner().invoke(
+ app,
+ [
+ "init",
+ "config",
+ f"{d_in}/config.cfg",
+ "--lang",
+ "en",
+ "--pipeline",
+ "trainable_lemmatizer",
+ ],
+ )
+ result_debug_data = CliRunner().invoke(
+ app,
+ [
+ "debug",
+ "data",
+ f"{d_in}/config.cfg",
+ "--paths.train",
+ f"{d_in}/train.spacy",
+ "--paths.dev",
+ f"{d_in}/dev.spacy",
+ ],
+ )
+ # Instead of checking specific wording of the output, which may change,
+ # we'll check that this section of the debug output is present.
+ assert "= Trainable Lemmatizer =" in result_debug_data.stdout
diff --git a/spacy/tests/training/test_corpus.py b/spacy/tests/training/test_corpus.py
new file mode 100644
index 000000000..b4f9cc13a
--- /dev/null
+++ b/spacy/tests/training/test_corpus.py
@@ -0,0 +1,78 @@
+from typing import IO, Generator, Iterable, List, TextIO, Tuple
+from contextlib import contextmanager
+from pathlib import Path
+import pytest
+import tempfile
+
+from spacy.lang.en import English
+from spacy.training import Example, PlainTextCorpus
+from spacy.util import make_tempdir
+
+# Intentional newlines to check that they are skipped.
+PLAIN_TEXT_DOC = """
+
+This is a doc. It contains two sentences.
+This is another doc.
+
+A third doc.
+
+"""
+
+PLAIN_TEXT_DOC_TOKENIZED = [
+ [
+ "This",
+ "is",
+ "a",
+ "doc",
+ ".",
+ "It",
+ "contains",
+ "two",
+ "sentences",
+ ".",
+ ],
+ ["This", "is", "another", "doc", "."],
+ ["A", "third", "doc", "."],
+]
+
+
+@pytest.mark.parametrize("min_length", [0, 5])
+@pytest.mark.parametrize("max_length", [0, 5])
+def test_plain_text_reader(min_length, max_length):
+ nlp = English()
+ with _string_to_tmp_file(PLAIN_TEXT_DOC) as file_path:
+ corpus = PlainTextCorpus(
+ file_path, min_length=min_length, max_length=max_length
+ )
+
+ check = [
+ doc
+ for doc in PLAIN_TEXT_DOC_TOKENIZED
+ if len(doc) >= min_length and (max_length == 0 or len(doc) <= max_length)
+ ]
+ reference, predicted = _examples_to_tokens(corpus(nlp))
+
+ assert reference == check
+ assert predicted == check
+
+
+@contextmanager
+def _string_to_tmp_file(s: str) -> Generator[Path, None, None]:
+ with make_tempdir() as d:
+ file_path = Path(d) / "string.txt"
+ with open(file_path, "w", encoding="utf-8") as f:
+ f.write(s)
+ yield file_path
+
+
+def _examples_to_tokens(
+ examples: Iterable[Example],
+) -> Tuple[List[List[str]], List[List[str]]]:
+ reference = []
+ predicted = []
+
+ for eg in examples:
+ reference.append([t.text for t in eg.reference])
+ predicted.append([t.text for t in eg.predicted])
+
+ return reference, predicted
diff --git a/spacy/training/__init__.py b/spacy/training/__init__.py
index 454437104..f8e69b1c8 100644
--- a/spacy/training/__init__.py
+++ b/spacy/training/__init__.py
@@ -1,4 +1,4 @@
-from .corpus import Corpus, JsonlCorpus # noqa: F401
+from .corpus import Corpus, JsonlCorpus, PlainTextCorpus # noqa: F401
from .example import Example, validate_examples, validate_get_examples # noqa: F401
from .example import validate_distillation_examples # noqa: F401
from .alignment import Alignment # noqa: F401
diff --git a/spacy/training/corpus.py b/spacy/training/corpus.py
index b9f929fcd..d626ad0e0 100644
--- a/spacy/training/corpus.py
+++ b/spacy/training/corpus.py
@@ -58,6 +58,28 @@ def read_labels(path: Path, *, require: bool = False):
return srsly.read_json(path)
+@util.registry.readers("spacy.PlainTextCorpus.v1")
+def create_plain_text_reader(
+ path: Optional[Path],
+ min_length: int = 0,
+ max_length: int = 0,
+) -> Callable[["Language"], Iterable[Doc]]:
+ """Iterate Example objects from a file or directory of plain text
+ UTF-8 files with one line per doc.
+
+ path (Path): The directory or filename to read from.
+ min_length (int): Minimum document length (in tokens). Shorter documents
+ will be skipped. Defaults to 0, which indicates no limit.
+ max_length (int): Maximum document length (in tokens). Longer documents will
+ be skipped. Defaults to 0, which indicates no limit.
+
+ DOCS: https://spacy.io/api/corpus#plaintextcorpus
+ """
+ if path is None:
+ raise ValueError(Errors.E913)
+ return PlainTextCorpus(path, min_length=min_length, max_length=max_length)
+
+
def walk_corpus(path: Union[str, Path], file_type) -> List[Path]:
path = util.ensure_path(path)
if not path.is_dir() and path.parts[-1].endswith(file_type):
@@ -257,3 +279,52 @@ class JsonlCorpus:
# We don't *need* an example here, but it seems nice to
# make it match the Corpus signature.
yield Example(doc, Doc(nlp.vocab, words=words, spaces=spaces))
+
+
+class PlainTextCorpus:
+ """Iterate Example objects from a file or directory of plain text
+ UTF-8 files with one line per doc.
+
+ path (Path): The directory or filename to read from.
+ min_length (int): Minimum document length (in tokens). Shorter documents
+ will be skipped. Defaults to 0, which indicates no limit.
+ max_length (int): Maximum document length (in tokens). Longer documents will
+ be skipped. Defaults to 0, which indicates no limit.
+
+ DOCS: https://spacy.io/api/corpus#plaintextcorpus
+ """
+
+ file_type = "txt"
+
+ def __init__(
+ self,
+ path: Optional[Union[str, Path]],
+ *,
+ min_length: int = 0,
+ max_length: int = 0,
+ ) -> None:
+ self.path = util.ensure_path(path)
+ self.min_length = min_length
+ self.max_length = max_length
+
+ def __call__(self, nlp: "Language") -> Iterator[Example]:
+ """Yield examples from the data.
+
+ nlp (Language): The current nlp object.
+ YIELDS (Example): The example objects.
+
+ DOCS: https://spacy.io/api/corpus#plaintextcorpus-call
+ """
+ for loc in walk_corpus(self.path, ".txt"):
+ with open(loc, encoding="utf-8") as f:
+ for text in f:
+ text = text.rstrip("\r\n")
+ if len(text):
+ doc = nlp.make_doc(text)
+ if self.min_length >= 1 and len(doc) < self.min_length:
+ continue
+ elif self.max_length >= 1 and len(doc) > self.max_length:
+ continue
+ # We don't *need* an example here, but it seems nice to
+ # make it match the Corpus signature.
+ yield Example(doc, doc.copy())
diff --git a/website/.dockerignore b/website/.dockerignore
new file mode 100644
index 000000000..e4a88552e
--- /dev/null
+++ b/website/.dockerignore
@@ -0,0 +1,9 @@
+.cache/
+.next/
+public/
+node_modules
+.npm
+logs
+*.log
+npm-debug.log*
+quickstart-training-generator.js
diff --git a/website/.gitignore b/website/.gitignore
index 70ef99fa5..599c0953a 100644
--- a/website/.gitignore
+++ b/website/.gitignore
@@ -1,5 +1,7 @@
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+quickstart-training-generator.js
+
# dependencies
/node_modules
/.pnp
@@ -41,4 +43,4 @@ next-env.d.ts
public/robots.txt
public/sitemap*
public/sw.js*
-public/workbox*
\ No newline at end of file
+public/workbox*
diff --git a/website/Dockerfile b/website/Dockerfile
index f71733e55..9b2f6cac4 100644
--- a/website/Dockerfile
+++ b/website/Dockerfile
@@ -1,16 +1,14 @@
-FROM node:11.15.0
+FROM node:18
-WORKDIR /spacy-io
-
-RUN npm install -g gatsby-cli@2.7.4
-
-COPY package.json .
-COPY package-lock.json .
-
-RUN npm install
+USER node
# This is so the installed node_modules will be up one directory
# from where a user mounts files, so that they don't accidentally mount
# their own node_modules from a different build
# https://nodejs.org/api/modules.html#modules_loading_from_node_modules_folders
-WORKDIR /spacy-io/website/
+WORKDIR /home/node
+COPY --chown=node package.json .
+COPY --chown=node package-lock.json .
+RUN npm install
+
+WORKDIR /home/node/website/
diff --git a/website/README.md b/website/README.md
index e9d7aec26..a434efe9a 100644
--- a/website/README.md
+++ b/website/README.md
@@ -41,33 +41,27 @@ If you'd like to do this, **be sure you do _not_ include your local
`node_modules` folder**, since there are some dependencies that need to be built
for the image system. Rename it before using.
-```bash
-docker run -it \
- -v $(pwd):/spacy-io/website \
- -p 8000:8000 \
- ghcr.io/explosion/spacy-io \
- gatsby develop -H 0.0.0.0
-```
-
-This will allow you to access the built website at http://0.0.0.0:8000/ in your
-browser, and still edit code in your editor while having the site reflect those
-changes.
-
-**Note**: If you're working on a Mac with an M1 processor, you might see
-segfault errors from `qemu` if you use the default image. To fix this use the
-`arm64` tagged image in the `docker run` command
-(ghcr.io/explosion/spacy-io:arm64).
-
-### Building the Docker image
-
-If you'd like to build the image locally, you can do so like this:
+First build the Docker image. This only needs to be done on the first run
+or when changes are made to `Dockerfile` or the website dependencies:
```bash
docker build -t spacy-io .
```
-This will take some time, so if you want to use the prebuilt image you'll save a
-bit of time.
+You can then build and run the website with:
+
+```bash
+docker run -it \
+ --rm \
+ -v $(pwd):/home/node/website \
+ -p 3000:3000 \
+ spacy-io \
+ npm run dev -- -H 0.0.0.0
+```
+
+This will allow you to access the built website at http://0.0.0.0:3000/ in your
+browser, and still edit code in your editor while having the site reflect those
+changes.
## Project structure
diff --git a/website/docs/api/cli.mdx b/website/docs/api/cli.mdx
index 80b1362bc..d96f8b743 100644
--- a/website/docs/api/cli.mdx
+++ b/website/docs/api/cli.mdx
@@ -12,6 +12,7 @@ menu:
- ['train', 'train']
- ['pretrain', 'pretrain']
- ['evaluate', 'evaluate']
+ - ['benchmark', 'benchmark']
- ['apply', 'apply']
- ['find-threshold', 'find-threshold']
- ['assemble', 'assemble']
@@ -269,10 +270,10 @@ $ python -m spacy convert [input_file] [output_dir] [--converter] [--file-type]
| `--file-type`, `-t` | Type of file to create. Either `spacy` (default) for binary [`DocBin`](/api/docbin) data or `json` for v2.x JSON format. ~~str (option)~~ |
| `--n-sents`, `-n` | Number of sentences per document. Supported for: `conll`, `conllu`, `iob`, `ner` ~~int (option)~~ |
| `--seg-sents`, `-s` | Segment sentences. Supported for: `conll`, `ner` ~~bool (flag)~~ |
-| `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str](option)~~ |
+| `--base`, `-b`, `--model` | Trained spaCy pipeline for sentence segmentation to use as base (for `--seg-sents`). ~~Optional[str] (option)~~ |
| `--morphology`, `-m` | Enable appending morphology to tags. Supported for: `conllu` ~~bool (flag)~~ |
| `--merge-subtokens`, `-T` | Merge CoNLL-U subtokens ~~bool (flag)~~ |
-| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path](option)~~ |
+| `--ner-map`, `-nm` | NER tag mapping (as JSON-encoded dict of entity types). Supported for: `conllu` ~~Optional[Path] (option)~~ |
| `--lang`, `-l` | Language code (if tokenizer required). ~~Optional[str] \(option)~~ |
| `--concatenate`, `-C` | Concatenate output to a single file ~~bool (flag)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
@@ -1135,8 +1136,19 @@ $ python -m spacy pretrain [config_path] [output_dir] [--code] [--resume-path] [
## evaluate {id="evaluate",version="2",tag="command"}
-Evaluate a trained pipeline. Expects a loadable spaCy pipeline (package name or
-path) and evaluation data in the
+The `evaluate` subcommand is superseded by
+[`spacy benchmark accuracy`](#benchmark-accuracy). `evaluate` is provided as an
+alias to `benchmark accuracy` for compatibility.
+
+## benchmark {id="benchmark", version="3.5"}
+
+The `spacy benchmark` CLI includes commands for benchmarking the accuracy and
+speed of your spaCy pipelines.
+
+### accuracy {id="benchmark-accuracy", version="3.5", tag="command"}
+
+Evaluate the accuracy of a trained pipeline. Expects a loadable spaCy pipeline
+(package name or path) and evaluation data in the
[binary `.spacy` format](/api/data-formats#binary-training). The
`--gold-preproc` option sets up the evaluation examples with gold-standard
sentences and tokens for the predictions. Gold preprocessing helps the
@@ -1147,7 +1159,7 @@ skew. To render a sample of dependency parses in a HTML file using the
`--displacy-path` argument.
```bash
-$ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
+$ python -m spacy benchmark accuracy [model] [data_path] [--output] [--code] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
```
| Name | Description |
@@ -1163,6 +1175,29 @@ $ python -m spacy evaluate [model] [data_path] [--output] [--code] [--gold-prepr
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| **CREATES** | Training results and optional metrics and visualizations. |
+### speed {id="benchmark-speed", version="3.5", tag="command"}
+
+Benchmark the speed of a trained pipeline with a 95% confidence interval.
+Expects a loadable spaCy pipeline (package name or path) and benchmark data in
+the [binary `.spacy` format](/api/data-formats#binary-training). The pipeline is
+warmed up before any measurements are taken.
+
+```cli
+$ python -m spacy benchmark speed [model] [data_path] [--batch_size] [--no-shuffle] [--gpu-id] [--batches] [--warmup]
+```
+
+| Name | Description |
+| -------------------- | -------------------------------------------------------------------------------------------------------- |
+| `model` | Pipeline to benchmark the speed of. Can be a package or a path to a data directory. ~~str (positional)~~ |
+| `data_path` | Location of benchmark data in spaCy's [binary format](/api/data-formats#training). ~~Path (positional)~~ |
+| `--batch-size`, `-b` | Set the batch size. If not set, the pipeline's batch size is used. ~~Optional[int] \(option)~~ |
+| `--no-shuffle` | Do not shuffle documents in the benchmark data. ~~bool (flag)~~ |
+| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
+| `--batches` | Number of batches to benchmark on. Defaults to `50`. ~~Optional[int] \(option)~~ |
+| `--warmup`, `-w` | Iterations over the benchmark data for warmup. Defaults to `3` ~~Optional[int] \(option)~~ |
+| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
+| **PRINTS** | Pipeline speed in words per second with a 95% confidence interval. |
+
## apply {id="apply", version="3.5", tag="command"}
Applies a trained pipeline to data and stores the resulting annotated documents
@@ -1176,24 +1211,23 @@ input formats are:
When a directory is provided it is traversed recursively to collect all files.
-```cli
+```bash
$ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key] [--force-overwrite] [--gpu-id] [--batch-size] [--n-process]
```
-| Name | Description |
-| ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `model` | Pipeline to apply to the data. Can be a package or a path to a data directory. ~~str (positional)~~ |
-| `data_path` | Location of data to be evaluated in spaCy's [binary format](/api/data-formats#training), jsonl, or plain text. ~~Path (positional)~~ |
-| `output-file`, `-o` | Output `DocBin` path. ~~str (positional)~~ |
-| `--code`, `-c` 3 | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
-| `--text-key`, `-tk` | The key for `.jsonl` files to use to grab the texts from. Defaults to `text`. ~~Optional[str] \(option)~~ |
-| `--force-overwrite`, `-F` | If the provided `output-file` already exists, then force `apply` to overwrite it. If this is `False` (default) then quits with a warning instead. ~~bool (flag)~~ |
-| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
-| `--batch-size`, `-b` | Batch size to use for prediction. Defaults to `1`. ~~int (option)~~ |
-| `--n-process`, `-n` | Number of processes to use for prediction. Defaults to `1`. ~~int (option)~~ |
-| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
-| **CREATES** | A `DocBin` with the annotations from the `model` for all the files found in `data-path`. |
-
+| Name | Description |
+| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| `model` | Pipeline to apply to the data. Can be a package or a path to a data directory. ~~str (positional)~~ |
+| `data_path` | Location of data to be evaluated in spaCy's [binary format](/api/data-formats#training), jsonl, or plain text. ~~Path (positional)~~ |
+| `output-file`, `-o` | Output `DocBin` path. ~~str (positional)~~ |
+| `--code`, `-c` | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-functions) for new architectures. ~~Optional[Path] \(option)~~ |
+| `--text-key`, `-tk` | The key for `.jsonl` files to use to grab the texts from. Defaults to `text`. ~~Optional[str] \(option)~~ |
+| `--force-overwrite`, `-F` | If the provided `output-file` already exists, then force `apply` to overwrite it. If this is `False` (default) then quits with a warning instead. ~~bool (flag)~~ |
+| `--gpu-id`, `-g` | GPU to use, if any. Defaults to `-1` for CPU. ~~int (option)~~ |
+| `--batch-size`, `-b` | Batch size to use for prediction. Defaults to `1`. ~~int (option)~~ |
+| `--n-process`, `-n` | Number of processes to use for prediction. Defaults to `1`. ~~int (option)~~ |
+| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
+| **CREATES** | A `DocBin` with the annotations from the `model` for all the files found in `data-path`. |
## find-threshold {id="find-threshold",version="3.5",tag="command"}
diff --git a/website/docs/api/corpus.mdx b/website/docs/api/corpus.mdx
index c58723e82..75e8f5c0f 100644
--- a/website/docs/api/corpus.mdx
+++ b/website/docs/api/corpus.mdx
@@ -175,3 +175,68 @@ Yield examples from the data.
| ---------- | -------------------------------------- |
| `nlp` | The current `nlp` object. ~~Language~~ |
| **YIELDS** | The examples. ~~Example~~ |
+
+## PlainTextCorpus {id="plaintextcorpus",tag="class",version="3.5.1"}
+
+Iterate over documents from a plain text file. Can be used to read the raw text
+corpus for language model
+[pretraining](/usage/embeddings-transformers#pretraining). The expected file
+format is:
+
+- UTF-8 encoding
+- One document per line
+- Blank lines are ignored.
+
+```text {title="Example"}
+Can I ask where you work now and what you do, and if you enjoy it?
+They may just pull out of the Seattle market completely, at least until they have autonomous vehicles.
+My cynical view on this is that it will never be free to the public. Reason: what would be the draw of joining the military? Right now their selling point is free Healthcare and Education. Ironically both are run horribly and most, that I've talked to, come out wishing they never went in.
+```
+
+### PlainTextCorpus.\_\_init\_\_ {id="plaintextcorpus-init",tag="method"}
+
+Initialize the reader.
+
+> #### Example
+>
+> ```python
+> from spacy.training import PlainTextCorpus
+>
+> corpus = PlainTextCorpus("./data/docs.txt")
+> ```
+>
+> ```ini
+> ### Example config
+> [corpora.pretrain]
+> @readers = "spacy.PlainTextCorpus.v1"
+> path = "corpus/raw_text.txt"
+> min_length = 0
+> max_length = 0
+> ```
+
+| Name | Description |
+| -------------- | -------------------------------------------------------------------------------------------------------------------------- |
+| `path` | The directory or filename to read from. Expects newline-delimited documents in UTF8 format. ~~Union[str, Path]~~ |
+| _keyword-only_ | |
+| `min_length` | Minimum document length (in tokens). Shorter documents will be skipped. Defaults to `0`, which indicates no limit. ~~int~~ |
+| `max_length` | Maximum document length (in tokens). Longer documents will be skipped. Defaults to `0`, which indicates no limit. ~~int~~ |
+
+### PlainTextCorpus.\_\_call\_\_ {id="plaintextcorpus-call",tag="method"}
+
+Yield examples from the data.
+
+> #### Example
+>
+> ```python
+> from spacy.training import PlainTextCorpus
+> import spacy
+>
+> corpus = PlainTextCorpus("./docs.txt")
+> nlp = spacy.blank("en")
+> data = corpus(nlp)
+> ```
+
+| Name | Description |
+| ---------- | -------------------------------------- |
+| `nlp` | The current `nlp` object. ~~Language~~ |
+| **YIELDS** | The examples. ~~Example~~ |
diff --git a/website/docs/api/entitylinker.mdx b/website/docs/api/entitylinker.mdx
index b4e331bb5..238b62a2e 100644
--- a/website/docs/api/entitylinker.mdx
+++ b/website/docs/api/entitylinker.mdx
@@ -15,7 +15,7 @@ world". It requires a `KnowledgeBase`, as well as a function to generate
plausible candidates from that `KnowledgeBase` given a certain textual mention,
and a machine learning model to pick the right candidate, given the local
context of the mention. `EntityLinker` defaults to using the
-[`InMemoryLookupKB`](/api/kb_in_memory) implementation.
+[`InMemoryLookupKB`](/api/inmemorylookupkb) implementation.
## Assigned Attributes {id="assigned-attributes"}
diff --git a/website/docs/api/kb_in_memory.mdx b/website/docs/api/inmemorylookupkb.mdx
similarity index 96%
rename from website/docs/api/kb_in_memory.mdx
rename to website/docs/api/inmemorylookupkb.mdx
index e85b63c45..c24fe78d6 100644
--- a/website/docs/api/kb_in_memory.mdx
+++ b/website/docs/api/inmemorylookupkb.mdx
@@ -43,7 +43,7 @@ The length of the fixed-size entity vectors in the knowledge base.
Add an entity to the knowledge base, specifying its corpus frequency and entity
vector, which should be of length
-[`entity_vector_length`](/api/kb_in_memory#entity_vector_length).
+[`entity_vector_length`](/api/inmemorylookupkb#entity_vector_length).
> #### Example
>
@@ -79,8 +79,9 @@ frequency and entity vector for each entity.
Add an alias or mention to the knowledge base, specifying its potential KB
identifiers and their prior probabilities. The entity identifiers should refer
-to entities previously added with [`add_entity`](/api/kb_in_memory#add_entity)
-or [`set_entities`](/api/kb_in_memory#set_entities). The sum of the prior
+to entities previously added with
+[`add_entity`](/api/inmemorylookupkb#add_entity) or
+[`set_entities`](/api/inmemorylookupkb#set_entities). The sum of the prior
probabilities should not exceed 1. Note that an empty string can not be used as
alias.
@@ -156,7 +157,7 @@ Get a list of all aliases in the knowledge base.
Given a certain textual mention as input, retrieve a list of candidate entities
of type [`Candidate`](/api/kb#candidate). Wraps
-[`get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
+[`get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
> #### Example
>
@@ -174,7 +175,7 @@ of type [`Candidate`](/api/kb#candidate). Wraps
## InMemoryLookupKB.get_candidates_batch {id="get_candidates_batch",tag="method"}
-Same as [`get_candidates()`](/api/kb_in_memory#get_candidates), but for an
+Same as [`get_candidates()`](/api/inmemorylookupkb#get_candidates), but for an
arbitrary number of mentions. The [`EntityLinker`](/api/entitylinker) component
will call `get_candidates_batch()` instead of `get_candidates()`, if the config
parameter `candidates_batch_size` is greater or equal than 1.
@@ -231,7 +232,7 @@ Given a certain entity ID, retrieve its pretrained entity vector.
## InMemoryLookupKB.get_vectors {id="get_vectors",tag="method"}
-Same as [`get_vector()`](/api/kb_in_memory#get_vector), but for an arbitrary
+Same as [`get_vector()`](/api/inmemorylookupkb#get_vector), but for an arbitrary
number of entity IDs.
The default implementation of `get_vectors()` executes `get_vector()` in a loop.
diff --git a/website/docs/api/kb.mdx b/website/docs/api/kb.mdx
index 887b7fe97..2b0d4d9d6 100644
--- a/website/docs/api/kb.mdx
+++ b/website/docs/api/kb.mdx
@@ -21,8 +21,8 @@ functions called by the [`EntityLinker`](/api/entitylinker) component.
This class was not abstract up to spaCy version 3.5. The `KnowledgeBase`
-implementation up to that point is available as `InMemoryLookupKB` from 3.5
-onwards.
+implementation up to that point is available as
+[`InMemoryLookupKB`](/api/inmemorylookupkb) from 3.5 onwards.
@@ -110,14 +110,15 @@ to you.
From spaCy 3.5 on `KnowledgeBase` is an abstract class (with
-[`InMemoryLookupKB`](/api/kb_in_memory) being a drop-in replacement) to allow
-more flexibility in customizing knowledge bases. Some of its methods were moved
-to [`InMemoryLookupKB`](/api/kb_in_memory) during this refactoring, one of those
-being `get_alias_candidates()`. This method is now available as
-[`InMemoryLookupKB.get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
-Note: [`InMemoryLookupKB.get_candidates()`](/api/kb_in_memory#get_candidates)
+[`InMemoryLookupKB`](/api/inmemorylookupkb) being a drop-in replacement) to
+allow more flexibility in customizing knowledge bases. Some of its methods were
+moved to [`InMemoryLookupKB`](/api/inmemorylookupkb) during this refactoring,
+one of those being `get_alias_candidates()`. This method is now available as
+[`InMemoryLookupKB.get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
+Note:
+[`InMemoryLookupKB.get_candidates()`](/api/inmemorylookupkb#get_candidates)
defaults to
-[`InMemoryLookupKB.get_alias_candidates()`](/api/kb_in_memory#get_alias_candidates).
+[`InMemoryLookupKB.get_alias_candidates()`](/api/inmemorylookupkb#get_alias_candidates).
## KnowledgeBase.get_vector {id="get_vector",tag="method"}
diff --git a/website/docs/api/top-level.mdx b/website/docs/api/top-level.mdx
index 39d7f8f3f..b13a6d28b 100644
--- a/website/docs/api/top-level.mdx
+++ b/website/docs/api/top-level.mdx
@@ -236,17 +236,17 @@ browser. Will run a simple web server.
> displacy.serve([doc1, doc2], style="dep")
> ```
-| Name | Description |
-| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~ |
-| `style` | Visualization style, `"dep"`, `"ent"` or `"span"` 3.3. Defaults to `"dep"`. ~~str~~ |
-| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
-| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
-| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
-| `manual` | Don't parse `Doc` and instead expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. Defaults to `False`. ~~bool~~ |
-| `port` | Port to serve visualization. Defaults to `5000`. ~~int~~ |
-| `host` | Host to serve visualization. Defaults to `"0.0.0.0"`. ~~str~~ |
-| `auto_select_port` | If `True`, automatically switch to a different port if the specified port is already in use. Defaults to `False`. ~~bool~~ |
+| Name | Description |
+| ----------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `docs` | Document(s) or span(s) to visualize. ~~Union[Iterable[Union[Doc, Span]], Doc, Span]~~ |
+| `style` 3.3 | Visualization style, `"dep"`, `"ent"` or `"span"`. Defaults to `"dep"`. ~~str~~ |
+| `page` | Render markup as full HTML page. Defaults to `True`. ~~bool~~ |
+| `minify` | Minify HTML markup. Defaults to `False`. ~~bool~~ |
+| `options` | [Visualizer-specific options](#displacy_options), e.g. colors. ~~Dict[str, Any]~~ |
+| `manual` | Don't parse `Doc` and instead expect a dict or list of dicts. [See here](/usage/visualizers#manual-usage) for formats and examples. Defaults to `False`. ~~bool~~ |
+| `port` | Port to serve visualization. Defaults to `5000`. ~~int~~ |
+| `host` | Host to serve visualization. Defaults to `"0.0.0.0"`. ~~str~~ |
+| `auto_select_port` 3.5 | If `True`, automatically switch to a different port if the specified port is already in use. Defaults to `False`. ~~bool~~ |
### displacy.render {id="displacy.render",tag="method",version="2"}
diff --git a/website/docs/usage/101/_architecture.mdx b/website/docs/usage/101/_architecture.mdx
index 5dd56e486..35c36088a 100644
--- a/website/docs/usage/101/_architecture.mdx
+++ b/website/docs/usage/101/_architecture.mdx
@@ -81,7 +81,7 @@ operates on a `Doc` and gives you access to the matched tokens **in context**.
| ------------------------------------------------ | -------------------------------------------------------------------------------------------------- |
| [`Corpus`](/api/corpus) | Class for managing annotated corpora for training and evaluation data. |
| [`KnowledgeBase`](/api/kb) | Abstract base class for storage and retrieval of data for entity linking. |
-| [`InMemoryLookupKB`](/api/kb_in_memory) | Implementation of `KnowledgeBase` storing all data in memory. |
+| [`InMemoryLookupKB`](/api/inmemorylookupkb) | Implementation of `KnowledgeBase` storing all data in memory. |
| [`Candidate`](/api/kb#candidate) | Object associating a textual mention with a specific entity contained in a `KnowledgeBase`. |
| [`Lookups`](/api/lookups) | Container for convenient access to large lookup tables and dictionaries. |
| [`MorphAnalysis`](/api/morphology#morphanalysis) | A morphological analysis. |
diff --git a/website/docs/usage/101/_vectors-similarity.mdx b/website/docs/usage/101/_vectors-similarity.mdx
index c27f777d8..6deab926d 100644
--- a/website/docs/usage/101/_vectors-similarity.mdx
+++ b/website/docs/usage/101/_vectors-similarity.mdx
@@ -134,6 +134,7 @@ useful for your purpose. Here are some important considerations to keep in mind:
[`sense2vec`](https://github.com/explosion/sense2vec) is a library developed by
diff --git a/website/docs/usage/layers-architectures.mdx b/website/docs/usage/layers-architectures.mdx
index 37f11e8e2..8f6bf3a20 100644
--- a/website/docs/usage/layers-architectures.mdx
+++ b/website/docs/usage/layers-architectures.mdx
@@ -113,6 +113,7 @@ code.
diff --git a/website/docs/usage/projects.mdx b/website/docs/usage/projects.mdx
index 8ec035942..f3cca8013 100644
--- a/website/docs/usage/projects.mdx
+++ b/website/docs/usage/projects.mdx
@@ -943,7 +943,7 @@ full embedded visualizer, as well as individual components.
> $ pip install spacy-streamlit --pre
> ```
-
+
Using [`spacy-streamlit`](https://github.com/explosion/spacy-streamlit), your
projects can easily define their own scripts that spin up an interactive
diff --git a/website/docs/usage/rule-based-matching.mdx b/website/docs/usage/rule-based-matching.mdx
index 1c3c6e3b8..0c2bd7a66 100644
--- a/website/docs/usage/rule-based-matching.mdx
+++ b/website/docs/usage/rule-based-matching.mdx
@@ -384,14 +384,14 @@ the more specific attributes `FUZZY1`..`FUZZY9` you can specify the maximum
allowed edit distance directly.
```python
-# Match lowercase with fuzzy matching (allows 2 edits)
+# Match lowercase with fuzzy matching (allows 3 edits)
pattern = [{"LOWER": {"FUZZY": "definitely"}}]
-# Match custom attribute values with fuzzy matching (allows 2 edits)
+# Match custom attribute values with fuzzy matching (allows 3 edits)
pattern = [{"_": {"country": {"FUZZY": "Kyrgyzstan"}}}]
-# Match with exact Levenshtein edit distance limits (allows 3 edits)
-pattern = [{"_": {"country": {"FUZZY3": "Kyrgyzstan"}}}]
+# Match with exact Levenshtein edit distance limits (allows 4 edits)
+pattern = [{"_": {"country": {"FUZZY4": "Kyrgyzstan"}}}]
```
#### Regex and fuzzy matching with lists {id="regex-fuzzy-lists", version="3.5"}
diff --git a/website/docs/usage/saving-loading.mdx b/website/docs/usage/saving-loading.mdx
index d4f5cda76..cdc587273 100644
--- a/website/docs/usage/saving-loading.mdx
+++ b/website/docs/usage/saving-loading.mdx
@@ -304,6 +304,28 @@ installed in the same environment β that's it.
| `spacy_lookups` | Group of entry points for custom [`Lookups`](/api/lookups), including lemmatizer data. Used by spaCy's [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) package. |
| [`spacy_displacy_colors`](#entry-points-displacy) | Group of entry points of custom label colors for the [displaCy visualizer](/usage/visualizers#ent). The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
+### Loading probability tables into existing models
+
+You can load a probability table from [spacy-lookups-data](https://github.com/explosion/spacy-lookups-data) into an existing spaCy model like `en_core_web_sm`.
+
+```python
+# Requirements: pip install spacy-lookups-data
+import spacy
+from spacy.lookups import load_lookups
+nlp = spacy.load("en_core_web_sm")
+lookups = load_lookups("en", ["lexeme_prob"])
+nlp.vocab.lookups.add_table("lexeme_prob", lookups.get_table("lexeme_prob"))
+```
+
+When training a model from scratch you can also specify probability tables in the `config.cfg`.
+
+```ini {title="config.cfg (excerpt)"}
+[initialize.lookups]
+@misc = "spacy.LookupsDataLoader.v1"
+lang = ${nlp.lang}
+tables = ["lexeme_prob"]
+```
+
### Custom components via entry points {id="entry-points-components"}
When you load a pipeline, spaCy will generally use its `config.cfg` to set up
@@ -684,10 +706,15 @@ If your pipeline includes
[custom components](/usage/processing-pipelines#custom-components), model
architectures or other [code](/usage/training#custom-code), those functions need
to be registered **before** your pipeline is loaded. Otherwise, spaCy won't know
-how to create the objects referenced in the config. The
-[`spacy package`](/api/cli#package) command lets you provide one or more paths
-to Python files containing custom registered functions using the `--code`
-argument.
+how to create the objects referenced in the config. If you're loading your own
+pipeline in Python, you can make custom components available just by importing
+the code that defines them before calling
+[`spacy.load`](/api/top-level#spacy.load). This is also how the `--code`
+argument to CLI commands works.
+
+With the [`spacy package`](/api/cli#package) command, you can provide one or
+more paths to Python files containing custom registered functions using the
+`--code` argument.
> #### \_\_init\_\_.py (excerpt)
>
diff --git a/website/docs/usage/spacy-101.mdx b/website/docs/usage/spacy-101.mdx
index a02e73508..6d444a1e9 100644
--- a/website/docs/usage/spacy-101.mdx
+++ b/website/docs/usage/spacy-101.mdx
@@ -567,7 +567,10 @@ If you would like to use the spaCy logo on your site, please get in touch and
ask us first. However, if you want to show support and tell others that your
project is using spaCy, you can grab one of our **spaCy badges** here:
-
+
```markdown
[](https://spacy.io)
@@ -575,8 +578,9 @@ project is using spaCy, you can grab one of our **spaCy badges** here:
```markdown
-[](https://spacy.io)
+[](https://spacy.io)
```
diff --git a/website/docs/usage/v3-5.mdx b/website/docs/usage/v3-5.mdx
new file mode 100644
index 000000000..ac61338e3
--- /dev/null
+++ b/website/docs/usage/v3-5.mdx
@@ -0,0 +1,215 @@
+---
+title: What's New in v3.5
+teaser: New features and how to upgrade
+menu:
+ - ['New Features', 'features']
+ - ['Upgrading Notes', 'upgrading']
+---
+
+## New features {id="features",hidden="true"}
+
+spaCy v3.5 introduces three new CLI commands, `apply`, `benchmark` and
+`find-threshold`, adds fuzzy matching, provides improvements to our entity
+linking functionality, and includes a range of language updates and bug fixes.
+
+### New CLI commands {id="cli"}
+
+#### apply CLI
+
+The [`apply` CLI](/api/cli#apply) can be used to apply a pipeline to one or more
+`.txt`, `.jsonl` or `.spacy` input files, saving the annotated docs in a single
+`.spacy` file.
+
+```bash
+$ spacy apply en_core_web_sm my_texts/ output.spacy
+```
+
+#### benchmark CLI
+
+The [`benchmark` CLI](/api/cli#benchmark) has been added to extend the existing
+`evaluate` functionality with a wider range of profiling subcommands.
+
+The `benchmark accuracy` CLI is introduced as an alias for `evaluate`. The new
+`benchmark speed` CLI performs warmup rounds before measuring the speed in words
+per second on batches of randomly shuffled documents from the provided data.
+
+```bash
+$ spacy benchmark speed my_pipeline data.spacy
+```
+
+The output is the mean performance using batches (`nlp.pipe`) with a 95%
+confidence interval, e.g., profiling `en_core_web_sm` on CPU:
+
+```none
+Outliers: 2.0%, extreme outliers: 0.0%
+Mean: 18904.1 words/s (95% CI: -256.9 +244.1)
+```
+
+#### find-threshold CLI
+
+The [`find-threshold` CLI](/api/cli#find-threshold) runs a series of trials
+across threshold values from `0.0` to `1.0` and identifies the best threshold
+for the provided score metric.
+
+The following command runs 20 trials for the `spancat` component in
+`my_pipeline`, recording the `spans_sc_f` score for each value of the threshold
+`[components.spancat.threshold]` from `0.0` to `1.0`:
+
+```bash
+$ spacy find-threshold my_pipeline data.spacy spancat threshold spans_sc_f --n_trials 20
+```
+
+The `find-threshold` CLI can be used with `textcat_multilabel`, `spancat` and
+custom components with thresholds that are applied while predicting or scoring.
+
+### Fuzzy matching {id="fuzzy"}
+
+New `FUZZY` operators support [fuzzy matching](/usage/rule-based-matching#fuzzy)
+with the `Matcher`. By default, the `FUZZY` operator allows a Levenshtein edit
+distance of 2 and up to 30% of the pattern string length. `FUZZY1`..`FUZZY9` can
+be used to specify the exact number of allowed edits.
+
+```python
+# Match lowercase with fuzzy matching (allows up to 3 edits)
+pattern = [{"LOWER": {"FUZZY": "definitely"}}]
+
+# Match custom attribute values with fuzzy matching (allows up to 3 edits)
+pattern = [{"_": {"country": {"FUZZY": "Kyrgyzstan"}}}]
+
+# Match with exact Levenshtein edit distance limits (allows up to 4 edits)
+pattern = [{"_": {"country": {"FUZZY4": "Kyrgyzstan"}}}]
+```
+
+Note that `FUZZY` uses Levenshtein edit distance rather than Damerau-Levenshtein
+edit distance, so a transposition like `teh` for `the` counts as two edits, one
+insertion and one deletion.
+
+If you'd prefer an alternate fuzzy matching algorithm, you can provide your own
+custom method to the `Matcher` or as a config option for an entity ruler and
+span ruler.
+
+### FUZZY and REGEX with lists {id="fuzzy-regex-lists"}
+
+The `FUZZY` and `REGEX` operators are also now supported for lists with `IN` and
+`NOT_IN`:
+
+```python
+pattern = [{"TEXT": {"FUZZY": {"IN": ["awesome", "cool", "wonderful"]}}}]
+pattern = [{"TEXT": {"REGEX": {"NOT_IN": ["^awe(some)?$", "^wonder(ful)?"]}}}]
+```
+
+### Entity linking generalization {id="el"}
+
+The knowledge base used for entity linking is now easier to customize and has a
+new default implementation [`InMemoryLookupKB`](/api/inmemorylookupkb).
+
+### Additional features and improvements {id="additional-features-and-improvements"}
+
+- Language updates:
+ - Extended support for Slovenian
+ - Fixed lookup fallback for French and Catalan lemmatizers
+ - Switch Russian and Ukrainian lemmatizers to `pymorphy3`
+ - Support for editorial punctuation in Ancient Greek
+ - Update to Russian tokenizer exceptions
+ - Small fix for Dutch stop words
+- Allow up to `typer` v0.7.x, `mypy` 0.990 and `typing_extensions` v4.4.x.
+- New `spacy.ConsoleLogger.v3` with expanded progress
+ [tracking](/api/top-level#ConsoleLogger).
+- Improved scoring behavior for `textcat` with `spacy.textcat_scorer.v2` and
+ `spacy.textcat_multilabel_scorer.v2`.
+- Updates so that downstream components can train properly on a frozen `tok2vec`
+ or `transformer` layer.
+- Allow interpolation of variables in directory names in projects.
+- Support for local file system [remotes](/usage/projects#remote) for projects.
+- Improve UX around `displacy.serve` when the default port is in use.
+- Optional `before_update` callback that is invoked at the start of each
+ [training step](/api/data-formats#config-training).
+- Improve performance of `SpanGroup` and fix typing issues for `SpanGroup` and
+ `Span` objects.
+- Patch a
+ [security vulnerability](https://github.com/advisories/GHSA-gw9q-c7gh-j9vm) in
+ extracting tar files.
+- Add equality definition for `Vectors`.
+- Ensure `Vocab.to_disk` respects the exclude setting for `lookups` and
+ `vectors`.
+- Correctly handle missing annotations in the edit tree lemmatizer.
+
+### Trained pipeline updates {id="pipelines"}
+
+- The CNN pipelines add `IS_SPACE` as a `tok2vec` feature for `tagger` and
+ `morphologizer` components to improve tagging of non-whitespace vs. whitespace
+ tokens.
+- The transformer pipelines require `spacy-transformers` v1.2, which uses the
+ exact alignment from `tokenizers` for fast tokenizers instead of the heuristic
+ alignment from `spacy-alignments`. For all trained pipelines except
+ `ja_core_news_trf`, the alignments between spaCy tokens and transformer tokens
+ may be slightly different. More details about the `spacy-transformers` changes
+ in the
+ [v1.2.0 release notes](https://github.com/explosion/spacy-transformers/releases/tag/v1.2.0).
+
+## Notes about upgrading from v3.4 {id="upgrading"}
+
+### Validation of textcat values {id="textcat-validation"}
+
+An error is now raised when unsupported values are given as input to train a
+`textcat` or `textcat_multilabel` model - ensure that values are `0.0` or `1.0`
+as explained in the [docs](/api/textcategorizer#assigned-attributes).
+
+### Updated scorers for tokenization and textcat {id="scores"}
+
+We fixed a bug that inflated the `token_acc` scores in v3.0-v3.4. The reported
+`token_acc` will drop from v3.4 to v3.5, but if `token_p/r/f` stay the same,
+your tokenization performance has not changed from v3.4.
+
+For new `textcat` or `textcat_multilabel` configs, the new default `v2` scorers:
+
+- ignore `threshold` for `textcat`, so the reported `cats_p/r/f` may increase
+ slightly in v3.5 even though the underlying predictions are unchanged
+- report the performance of only the **final** `textcat` or `textcat_multilabel`
+ component in the pipeline by default
+- allow custom scorers to be used to score multiple `textcat` and
+ `textcat_multilabel` components with `Scorer.score_cats` by restricting the
+ evaluation to the component's provided labels
+
+### 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": ">=3.4.0,<3.5.0",
++ "spacy_version": ">=3.4.0,<3.6.0",
+```
+
+### Updating v3.4 configs
+
+To update a config from spaCy v3.4 with the new v3.5 settings, run
+[`init fill-config`](/api/cli#init-fill-config):
+
+```cli
+$ python -m spacy init fill-config config-v3.4.cfg config-v3.5.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).
diff --git a/website/docs/usage/visualizers.mdx b/website/docs/usage/visualizers.mdx
index f1ff6dd3d..1d3682af4 100644
--- a/website/docs/usage/visualizers.mdx
+++ b/website/docs/usage/visualizers.mdx
@@ -437,6 +437,6 @@ Alternatively, if you're using [Streamlit](https://streamlit.io), check out the
helps you integrate spaCy visualizations into your apps. It includes a full
embedded visualizer, as well as individual components.
-
+
diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json
index 339e4085b..b5c555da6 100644
--- a/website/meta/sidebars.json
+++ b/website/meta/sidebars.json
@@ -13,7 +13,8 @@
{ "text": "New in v3.1", "url": "/usage/v3-1" },
{ "text": "New in v3.2", "url": "/usage/v3-2" },
{ "text": "New in v3.3", "url": "/usage/v3-3" },
- { "text": "New in v3.4", "url": "/usage/v3-4" }
+ { "text": "New in v3.4", "url": "/usage/v3-4" },
+ { "text": "New in v3.5", "url": "/usage/v3-5" }
]
},
{
@@ -129,6 +130,7 @@
"items": [
{ "text": "Attributes", "url": "/api/attributes" },
{ "text": "Corpus", "url": "/api/corpus" },
+ { "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" },
{ "text": "KnowledgeBase", "url": "/api/kb" },
{ "text": "Lookups", "url": "/api/lookups" },
{ "text": "MorphAnalysis", "url": "/api/morphology#morphanalysis" },
diff --git a/website/meta/site.json b/website/meta/site.json
index 5dcb89443..3d4f2d5ee 100644
--- a/website/meta/site.json
+++ b/website/meta/site.json
@@ -27,7 +27,7 @@
"indexName": "spacy"
},
"binderUrl": "explosion/spacy-io-binder",
- "binderVersion": "3.4",
+ "binderVersion": "3.5",
"sections": [
{ "id": "usage", "title": "Usage Documentation", "theme": "blue" },
{ "id": "models", "title": "Models Documentation", "theme": "blue" },
diff --git a/website/meta/universe.json b/website/meta/universe.json
index f15d461e8..e35a4f045 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -2381,7 +2381,7 @@
"author": "Nikita Kitaev",
"author_links": {
"github": "nikitakit",
- "website": " http://kitaev.io"
+ "website": "http://kitaev.io"
},
"category": ["research", "pipeline"]
},
diff --git a/website/pages/_app.tsx b/website/pages/_app.tsx
index 8db80a672..a837d9ce8 100644
--- a/website/pages/_app.tsx
+++ b/website/pages/_app.tsx
@@ -17,7 +17,7 @@ export default function App({ Component, pageProps }: AppProps) {
diff --git a/website/pages/index.tsx b/website/pages/index.tsx
index 170bca137..fc0dba378 100644
--- a/website/pages/index.tsx
+++ b/website/pages/index.tsx
@@ -13,7 +13,7 @@ import {
LandingBanner,
} from '../src/components/landing'
import { H2 } from '../src/components/typography'
-import { InlineCode } from '../src/components/code'
+import { InlineCode } from '../src/components/inlineCode'
import { Ul, Li } from '../src/components/list'
import Button from '../src/components/button'
import Link from '../src/components/link'
@@ -89,8 +89,8 @@ const Landing = () => {
- In the five years since its release, spaCy has become an industry standard with
- a huge ecosystem. Choose from a variety of plugins, integrate with your machine
+ Since its release in 2015, spaCy has become an industry standard with a huge
+ ecosystem. Choose from a variety of plugins, integrate with your machine
learning stack and build custom components and workflows.
@@ -162,7 +162,7 @@ const Landing = () => {
small
>
-
+
{
-
+
diff --git a/website/src/components/accordion.js b/website/src/components/accordion.js
index 504f415a5..9ff145bd2 100644
--- a/website/src/components/accordion.js
+++ b/website/src/components/accordion.js
@@ -33,7 +33,7 @@ export default function Accordion({ title, id, expanded = false, spaced = false,
event.stopPropagation()}
>
¶
diff --git a/website/src/components/card.js b/website/src/components/card.js
index 9eb597b7b..ef43eb866 100644
--- a/website/src/components/card.js
+++ b/website/src/components/card.js
@@ -1,6 +1,7 @@
import React from 'react'
import PropTypes from 'prop-types'
import classNames from 'classnames'
+import ImageNext from 'next/image'
import Link from './link'
import { H5 } from './typography'
@@ -10,7 +11,7 @@ export default function Card({ title, to, image, header, small, onClick, childre
return (
+
```markdown
-[](https://spacy.io)
+[](https://spacy.io)
```
diff --git a/website/docs/usage/v3-5.mdx b/website/docs/usage/v3-5.mdx
new file mode 100644
index 000000000..ac61338e3
--- /dev/null
+++ b/website/docs/usage/v3-5.mdx
@@ -0,0 +1,215 @@
+---
+title: What's New in v3.5
+teaser: New features and how to upgrade
+menu:
+ - ['New Features', 'features']
+ - ['Upgrading Notes', 'upgrading']
+---
+
+## New features {id="features",hidden="true"}
+
+spaCy v3.5 introduces three new CLI commands, `apply`, `benchmark` and
+`find-threshold`, adds fuzzy matching, provides improvements to our entity
+linking functionality, and includes a range of language updates and bug fixes.
+
+### New CLI commands {id="cli"}
+
+#### apply CLI
+
+The [`apply` CLI](/api/cli#apply) can be used to apply a pipeline to one or more
+`.txt`, `.jsonl` or `.spacy` input files, saving the annotated docs in a single
+`.spacy` file.
+
+```bash
+$ spacy apply en_core_web_sm my_texts/ output.spacy
+```
+
+#### benchmark CLI
+
+The [`benchmark` CLI](/api/cli#benchmark) has been added to extend the existing
+`evaluate` functionality with a wider range of profiling subcommands.
+
+The `benchmark accuracy` CLI is introduced as an alias for `evaluate`. The new
+`benchmark speed` CLI performs warmup rounds before measuring the speed in words
+per second on batches of randomly shuffled documents from the provided data.
+
+```bash
+$ spacy benchmark speed my_pipeline data.spacy
+```
+
+The output is the mean performance using batches (`nlp.pipe`) with a 95%
+confidence interval, e.g., profiling `en_core_web_sm` on CPU:
+
+```none
+Outliers: 2.0%, extreme outliers: 0.0%
+Mean: 18904.1 words/s (95% CI: -256.9 +244.1)
+```
+
+#### find-threshold CLI
+
+The [`find-threshold` CLI](/api/cli#find-threshold) runs a series of trials
+across threshold values from `0.0` to `1.0` and identifies the best threshold
+for the provided score metric.
+
+The following command runs 20 trials for the `spancat` component in
+`my_pipeline`, recording the `spans_sc_f` score for each value of the threshold
+`[components.spancat.threshold]` from `0.0` to `1.0`:
+
+```bash
+$ spacy find-threshold my_pipeline data.spacy spancat threshold spans_sc_f --n_trials 20
+```
+
+The `find-threshold` CLI can be used with `textcat_multilabel`, `spancat` and
+custom components with thresholds that are applied while predicting or scoring.
+
+### Fuzzy matching {id="fuzzy"}
+
+New `FUZZY` operators support [fuzzy matching](/usage/rule-based-matching#fuzzy)
+with the `Matcher`. By default, the `FUZZY` operator allows a Levenshtein edit
+distance of 2 and up to 30% of the pattern string length. `FUZZY1`..`FUZZY9` can
+be used to specify the exact number of allowed edits.
+
+```python
+# Match lowercase with fuzzy matching (allows up to 3 edits)
+pattern = [{"LOWER": {"FUZZY": "definitely"}}]
+
+# Match custom attribute values with fuzzy matching (allows up to 3 edits)
+pattern = [{"_": {"country": {"FUZZY": "Kyrgyzstan"}}}]
+
+# Match with exact Levenshtein edit distance limits (allows up to 4 edits)
+pattern = [{"_": {"country": {"FUZZY4": "Kyrgyzstan"}}}]
+```
+
+Note that `FUZZY` uses Levenshtein edit distance rather than Damerau-Levenshtein
+edit distance, so a transposition like `teh` for `the` counts as two edits, one
+insertion and one deletion.
+
+If you'd prefer an alternate fuzzy matching algorithm, you can provide your own
+custom method to the `Matcher` or as a config option for an entity ruler and
+span ruler.
+
+### FUZZY and REGEX with lists {id="fuzzy-regex-lists"}
+
+The `FUZZY` and `REGEX` operators are also now supported for lists with `IN` and
+`NOT_IN`:
+
+```python
+pattern = [{"TEXT": {"FUZZY": {"IN": ["awesome", "cool", "wonderful"]}}}]
+pattern = [{"TEXT": {"REGEX": {"NOT_IN": ["^awe(some)?$", "^wonder(ful)?"]}}}]
+```
+
+### Entity linking generalization {id="el"}
+
+The knowledge base used for entity linking is now easier to customize and has a
+new default implementation [`InMemoryLookupKB`](/api/inmemorylookupkb).
+
+### Additional features and improvements {id="additional-features-and-improvements"}
+
+- Language updates:
+ - Extended support for Slovenian
+ - Fixed lookup fallback for French and Catalan lemmatizers
+ - Switch Russian and Ukrainian lemmatizers to `pymorphy3`
+ - Support for editorial punctuation in Ancient Greek
+ - Update to Russian tokenizer exceptions
+ - Small fix for Dutch stop words
+- Allow up to `typer` v0.7.x, `mypy` 0.990 and `typing_extensions` v4.4.x.
+- New `spacy.ConsoleLogger.v3` with expanded progress
+ [tracking](/api/top-level#ConsoleLogger).
+- Improved scoring behavior for `textcat` with `spacy.textcat_scorer.v2` and
+ `spacy.textcat_multilabel_scorer.v2`.
+- Updates so that downstream components can train properly on a frozen `tok2vec`
+ or `transformer` layer.
+- Allow interpolation of variables in directory names in projects.
+- Support for local file system [remotes](/usage/projects#remote) for projects.
+- Improve UX around `displacy.serve` when the default port is in use.
+- Optional `before_update` callback that is invoked at the start of each
+ [training step](/api/data-formats#config-training).
+- Improve performance of `SpanGroup` and fix typing issues for `SpanGroup` and
+ `Span` objects.
+- Patch a
+ [security vulnerability](https://github.com/advisories/GHSA-gw9q-c7gh-j9vm) in
+ extracting tar files.
+- Add equality definition for `Vectors`.
+- Ensure `Vocab.to_disk` respects the exclude setting for `lookups` and
+ `vectors`.
+- Correctly handle missing annotations in the edit tree lemmatizer.
+
+### Trained pipeline updates {id="pipelines"}
+
+- The CNN pipelines add `IS_SPACE` as a `tok2vec` feature for `tagger` and
+ `morphologizer` components to improve tagging of non-whitespace vs. whitespace
+ tokens.
+- The transformer pipelines require `spacy-transformers` v1.2, which uses the
+ exact alignment from `tokenizers` for fast tokenizers instead of the heuristic
+ alignment from `spacy-alignments`. For all trained pipelines except
+ `ja_core_news_trf`, the alignments between spaCy tokens and transformer tokens
+ may be slightly different. More details about the `spacy-transformers` changes
+ in the
+ [v1.2.0 release notes](https://github.com/explosion/spacy-transformers/releases/tag/v1.2.0).
+
+## Notes about upgrading from v3.4 {id="upgrading"}
+
+### Validation of textcat values {id="textcat-validation"}
+
+An error is now raised when unsupported values are given as input to train a
+`textcat` or `textcat_multilabel` model - ensure that values are `0.0` or `1.0`
+as explained in the [docs](/api/textcategorizer#assigned-attributes).
+
+### Updated scorers for tokenization and textcat {id="scores"}
+
+We fixed a bug that inflated the `token_acc` scores in v3.0-v3.4. The reported
+`token_acc` will drop from v3.4 to v3.5, but if `token_p/r/f` stay the same,
+your tokenization performance has not changed from v3.4.
+
+For new `textcat` or `textcat_multilabel` configs, the new default `v2` scorers:
+
+- ignore `threshold` for `textcat`, so the reported `cats_p/r/f` may increase
+ slightly in v3.5 even though the underlying predictions are unchanged
+- report the performance of only the **final** `textcat` or `textcat_multilabel`
+ component in the pipeline by default
+- allow custom scorers to be used to score multiple `textcat` and
+ `textcat_multilabel` components with `Scorer.score_cats` by restricting the
+ evaluation to the component's provided labels
+
+### 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": ">=3.4.0,<3.5.0",
++ "spacy_version": ">=3.4.0,<3.6.0",
+```
+
+### Updating v3.4 configs
+
+To update a config from spaCy v3.4 with the new v3.5 settings, run
+[`init fill-config`](/api/cli#init-fill-config):
+
+```cli
+$ python -m spacy init fill-config config-v3.4.cfg config-v3.5.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).
diff --git a/website/docs/usage/visualizers.mdx b/website/docs/usage/visualizers.mdx
index f1ff6dd3d..1d3682af4 100644
--- a/website/docs/usage/visualizers.mdx
+++ b/website/docs/usage/visualizers.mdx
@@ -437,6 +437,6 @@ Alternatively, if you're using [Streamlit](https://streamlit.io), check out the
helps you integrate spaCy visualizations into your apps. It includes a full
embedded visualizer, as well as individual components.
-
+
diff --git a/website/meta/sidebars.json b/website/meta/sidebars.json
index 339e4085b..b5c555da6 100644
--- a/website/meta/sidebars.json
+++ b/website/meta/sidebars.json
@@ -13,7 +13,8 @@
{ "text": "New in v3.1", "url": "/usage/v3-1" },
{ "text": "New in v3.2", "url": "/usage/v3-2" },
{ "text": "New in v3.3", "url": "/usage/v3-3" },
- { "text": "New in v3.4", "url": "/usage/v3-4" }
+ { "text": "New in v3.4", "url": "/usage/v3-4" },
+ { "text": "New in v3.5", "url": "/usage/v3-5" }
]
},
{
@@ -129,6 +130,7 @@
"items": [
{ "text": "Attributes", "url": "/api/attributes" },
{ "text": "Corpus", "url": "/api/corpus" },
+ { "text": "InMemoryLookupKB", "url": "/api/inmemorylookupkb" },
{ "text": "KnowledgeBase", "url": "/api/kb" },
{ "text": "Lookups", "url": "/api/lookups" },
{ "text": "MorphAnalysis", "url": "/api/morphology#morphanalysis" },
diff --git a/website/meta/site.json b/website/meta/site.json
index 5dcb89443..3d4f2d5ee 100644
--- a/website/meta/site.json
+++ b/website/meta/site.json
@@ -27,7 +27,7 @@
"indexName": "spacy"
},
"binderUrl": "explosion/spacy-io-binder",
- "binderVersion": "3.4",
+ "binderVersion": "3.5",
"sections": [
{ "id": "usage", "title": "Usage Documentation", "theme": "blue" },
{ "id": "models", "title": "Models Documentation", "theme": "blue" },
diff --git a/website/meta/universe.json b/website/meta/universe.json
index f15d461e8..e35a4f045 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -2381,7 +2381,7 @@
"author": "Nikita Kitaev",
"author_links": {
"github": "nikitakit",
- "website": " http://kitaev.io"
+ "website": "http://kitaev.io"
},
"category": ["research", "pipeline"]
},
diff --git a/website/pages/_app.tsx b/website/pages/_app.tsx
index 8db80a672..a837d9ce8 100644
--- a/website/pages/_app.tsx
+++ b/website/pages/_app.tsx
@@ -17,7 +17,7 @@ export default function App({ Component, pageProps }: AppProps) {
diff --git a/website/pages/index.tsx b/website/pages/index.tsx
index 170bca137..fc0dba378 100644
--- a/website/pages/index.tsx
+++ b/website/pages/index.tsx
@@ -13,7 +13,7 @@ import {
LandingBanner,
} from '../src/components/landing'
import { H2 } from '../src/components/typography'
-import { InlineCode } from '../src/components/code'
+import { InlineCode } from '../src/components/inlineCode'
import { Ul, Li } from '../src/components/list'
import Button from '../src/components/button'
import Link from '../src/components/link'
@@ -89,8 +89,8 @@ const Landing = () => {
+ 
+)
+
const Project = ({ data, components }) => (
<>