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Merge branch 'develop' of https://github.com/explosion/spaCy into develop

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This commit is contained in:
Matthew Honnibal 2020-09-24 16:11:47 +02:00
commit 74ee456374
22 changed files with 306 additions and 103 deletions
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2
spacy/about.py
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@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy-nightly"
__version__ = "3.0.0a23"
__version__ = "3.0.0a24"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

1
spacy/cli/download.py
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@ -88,7 +88,6 @@ def get_compatibility() -> dict:
def get_version(model: str, comp: dict) -> str:
model = get_base_version(model)
if model not in comp:
msg.fail(
f"No compatible package found for '{model}' (spaCy v{about.__version__})",

4
spacy/cli/info.py
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@ -91,7 +91,9 @@ def info_model(model: str, *, silent: bool = True) -> Dict[str, Any]:
meta["source"] = str(model_path.resolve())
else:
meta["source"] = str(model_path)
return {k: v for k, v in meta.items() if k not in ("accuracy", "speed")}
return {
k: v for k, v in meta.items() if k not in ("accuracy", "performance", "speed")
}
def get_markdown(data: Dict[str, Any], title: Optional[str] = None) -> str:

18
spacy/cli/train.py
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@ -97,6 +97,7 @@ def train(
dev_corpus = dot_to_object(config, T_cfg["dev_corpus"])
batcher = T_cfg["batcher"]
train_logger = T_cfg["logger"]
before_to_disk = create_before_to_disk_callback(T_cfg["before_to_disk"])
# Components that shouldn't be updated during training
frozen_components = T_cfg["frozen_components"]
# Sourced components that require resume_training
@ -167,6 +168,7 @@ def train(
with nlp.select_pipes(disable=frozen_components):
update_meta(T_cfg, nlp, info)
with nlp.use_params(optimizer.averages):
nlp = before_to_disk(nlp)
nlp.to_disk(output_path / "model-best")
progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
progress.set_description(f"Epoch {info['epoch']}")
@ -179,6 +181,7 @@ def train(
f"Aborting and saving the final best model. "
f"Encountered exception: {str(e)}"
)
nlp = before_to_disk(nlp)
nlp.to_disk(output_path / "model-final")
raise e
finally:
@ -233,6 +236,21 @@ def create_evaluation_callback(
return evaluate
def create_before_to_disk_callback(
callback: Optional[Callable[[Language], Language]]
) -> Callable[[Language], Language]:
def before_to_disk(nlp: Language) -> Language:
if not callback:
return nlp
modified_nlp = callback(nlp)
if not isinstance(modified_nlp, Language):
err = Errors.E914.format(name="before_to_disk", value=type(modified_nlp))
raise ValueError(err)
return modified_nlp
return before_to_disk
def train_while_improving(
nlp: Language,
optimizer: Optimizer,

2
spacy/default_config.cfg
View File

@ -72,6 +72,8 @@ frozen_components = []
dev_corpus = "corpora.dev"
# Location in the config where the train corpus is defined
train_corpus = "corpora.train"
# Optional callback before nlp object is saved to disk after training
before_to_disk = null
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"

9
spacy/errors.py
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@ -480,6 +480,9 @@ class Errors:
E201 = ("Span index out of range.")
# TODO: fix numbering after merging develop into master
E914 = ("Executing {name} callback failed. Expected the function to "
"return the nlp object but got: {value}. Maybe you forgot to return "
"the modified object in your function?")
E915 = ("Can't use score '{name}' to calculate final weighted score. Expected "
"float or int but got: {score_type}. To exclude the score from the "
"final score, set its weight to null in the [training.score_weights] "
@ -693,6 +696,12 @@ class Errors:
E1009 = ("String for hash '{val}' not found in StringStore. Set the value "
"through token.morph_ instead or add the string to the "
"StringStore with `nlp.vocab.strings.add(string)`.")
E1010 = ("Unable to set entity information for token {i} which is included "
"in more than one span in entities, blocked, missing or outside.")
E1011 = ("Unsupported default '{default}' in doc.set_ents. Available "
"options: {modes}")
E1012 = ("Entity spans and blocked/missing/outside spans should be "
"provided to doc.set_ents as lists of `Span` objects.")
@add_codes

4
spacy/schemas.py
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@ -182,8 +182,7 @@ class ModelMetaSchema(BaseModel):
sources: Optional[Union[List[StrictStr], List[Dict[str, str]]]] = Field(None, title="Training data sources")
vectors: Dict[str, Any] = Field({}, title="Included word vectors")
labels: Dict[str, List[str]] = Field({}, title="Component labels, keyed by component name")
accuracy: Dict[str, Union[float, Dict[str, float]]] = Field({}, title="Accuracy numbers")
speed: Dict[str, Union[float, int]] = Field({}, title="Speed evaluation numbers")
performance: Dict[str, Union[float, Dict[str, float]]] = Field({}, title="Accuracy and speed numbers")
spacy_git_version: StrictStr = Field("", title="Commit of spaCy version used")
# fmt: on
@ -217,6 +216,7 @@ class ConfigSchemaTraining(BaseModel):
optimizer: Optimizer = Field(..., title="The optimizer to use")
logger: Logger = Field(..., title="The logger to track training progress")
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
# fmt: on
class Config:

4
spacy/tests/doc/test_add_entities.py
View File

@ -29,10 +29,10 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
ner.begin_training(lambda: [_ner_example(ner)])
ner(doc)
doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
doc.ents = [("ANIMAL", 3, 4)]
assert [w.ent_iob_ for w in doc] == ["O", "O", "O", "B"]
doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
doc.ents = [("WORD", 0, 2)]
assert [w.ent_iob_ for w in doc] == ["B", "I", "O", "O"]

73
spacy/tests/doc/test_doc_api.py
View File

@ -152,7 +152,7 @@ def test_doc_api_set_ents(en_tokenizer):
assert len(tokens.ents) == 0
tokens.ents = [(tokens.vocab.strings["PRODUCT"], 2, 4)]
assert len(list(tokens.ents)) == 1
assert [t.ent_iob for t in tokens] == [0, 0, 3, 1, 0, 0, 0, 0]
assert [t.ent_iob for t in tokens] == [2, 2, 3, 1, 2, 2, 2, 2]
assert tokens.ents[0].label_ == "PRODUCT"
assert tokens.ents[0].start == 2
assert tokens.ents[0].end == 4
@ -427,7 +427,7 @@ def test_has_annotation(en_vocab):
doc[0].lemma_ = "a"
doc[0].dep_ = "dep"
doc[0].head = doc[1]
doc.ents = [Span(doc, 0, 1, label="HELLO")]
doc.set_ents([Span(doc, 0, 1, label="HELLO")], default="missing")
for attr in attrs:
assert doc.has_annotation(attr)
@ -457,7 +457,74 @@ def test_is_flags_deprecated(en_tokenizer):
doc.is_sentenced
def test_doc_set_ents():
def test_doc_set_ents(en_tokenizer):
# set ents
doc = en_tokenizer("a b c d e")
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 2]
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
# add ents, invalid IOB repaired
doc = en_tokenizer("a b c d e")
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
doc.set_ents([Span(doc, 0, 2, 12)], default="unmodified")
assert [t.ent_iob for t in doc] == [3, 1, 3, 2, 2]
assert [t.ent_type for t in doc] == [12, 12, 11, 0, 0]
# missing ents
doc = en_tokenizer("a b c d e")
doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)], missing=[doc[4:5]])
assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 0]
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
# outside ents
doc = en_tokenizer("a b c d e")
doc.set_ents(
[Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)],
outside=[doc[4:5]],
default="missing",
)
assert [t.ent_iob for t in doc] == [3, 3, 1, 0, 2]
assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]
# blocked ents
doc = en_tokenizer("a b c d e")
doc.set_ents([], blocked=[doc[1:2], doc[3:5]], default="unmodified")
assert [t.ent_iob for t in doc] == [0, 3, 0, 3, 3]
assert [t.ent_type for t in doc] == [0, 0, 0, 0, 0]
assert doc.ents == tuple()
# invalid IOB repaired after blocked
doc.ents = [Span(doc, 3, 5, "ENT")]
assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 1]
doc.set_ents([], blocked=[doc[3:4]], default="unmodified")
assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 3]
# all types
doc = en_tokenizer("a b c d e")
doc.set_ents(
[Span(doc, 0, 1, 10)],
blocked=[doc[1:2]],
missing=[doc[2:3]],
outside=[doc[3:4]],
default="unmodified",
)
assert [t.ent_iob for t in doc] == [3, 3, 0, 2, 0]
assert [t.ent_type for t in doc] == [10, 0, 0, 0, 0]
doc = en_tokenizer("a b c d e")
# single span instead of a list
with pytest.raises(ValueError):
doc.set_ents([], missing=doc[1:2])
# invalid default mode
with pytest.raises(ValueError):
doc.set_ents([], missing=[doc[1:2]], default="none")
# conflicting/overlapping specifications
with pytest.raises(ValueError):
doc.set_ents([], missing=[doc[1:2]], outside=[doc[1:2]])
def test_doc_ents_setter():
"""Test that both strings and integers can be used to set entities in
tuple format via doc.ents."""
words = ["a", "b", "c", "d", "e"]

4
spacy/tests/parser/test_ner.py
View File

@ -168,7 +168,7 @@ def test_accept_blocked_token():
ner2 = nlp2.create_pipe("ner", config=config)
# set "New York" to a blocked entity
doc2.ents = [(0, 3, 5)]
doc2.set_ents([], blocked=[doc2[3:5]], default="unmodified")
assert [token.ent_iob_ for token in doc2] == ["", "", "", "B", "B"]
assert [token.ent_type_ for token in doc2] == ["", "", "", "", ""]
@ -358,5 +358,5 @@ class BlockerComponent1:
self.name = name
def __call__(self, doc):
doc.ents = [(0, self.start, self.end)]
doc.set_ents([], blocked=[doc[self.start:self.end]], default="unmodified")
return doc

139
spacy/tokens/doc.pyx
View File

@ -7,6 +7,8 @@ from libc.stdint cimport int32_t, uint64_t
import copy
from collections import Counter
from enum import Enum
import itertools
import numpy
import srsly
from thinc.api import get_array_module
@ -86,6 +88,17 @@ cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name)
return get_token_attr(token, feat_name)
class SetEntsDefault(str, Enum):
blocked = "blocked"
missing = "missing"
outside = "outside"
unmodified = "unmodified"
@classmethod
def values(cls):
return list(cls.__members__.keys())
cdef class Doc:
"""A sequence of Token objects. Access sentences and named entities, export
annotations to numpy arrays, losslessly serialize to compressed binary
@ -660,50 +673,100 @@ cdef class Doc:
# TODO:
# 1. Test basic data-driven ORTH gazetteer
# 2. Test more nuanced date and currency regex
tokens_in_ents = {}
cdef attr_t entity_type
cdef attr_t kb_id
cdef int ent_start, ent_end, token_index
cdef attr_t entity_type, kb_id
cdef int ent_start, ent_end
ent_spans = []
for ent_info in ents:
entity_type_, kb_id, ent_start, ent_end = get_entity_info(ent_info)
if isinstance(entity_type_, str):
self.vocab.strings.add(entity_type_)
entity_type = self.vocab.strings.as_int(entity_type_)
for token_index in range(ent_start, ent_end):
if token_index in tokens_in_ents:
raise ValueError(Errors.E103.format(
span1=(tokens_in_ents[token_index][0],
tokens_in_ents[token_index][1],
self.vocab.strings[tokens_in_ents[token_index][2]]),
span2=(ent_start, ent_end, self.vocab.strings[entity_type])))
tokens_in_ents[token_index] = (ent_start, ent_end, entity_type, kb_id)
cdef int i
span = Span(self, ent_start, ent_end, label=entity_type_, kb_id=kb_id)
ent_spans.append(span)
self.set_ents(ent_spans, default=SetEntsDefault.outside)
def set_ents(self, entities, *, blocked=None, missing=None, outside=None, default=SetEntsDefault.outside):
"""Set entity annotation.
entities (List[Span]): Spans with labels to set as entities.
blocked (Optional[List[Span]]): Spans to set as 'blocked' (never an
entity) for spacy's built-in NER component. Other components may
ignore this setting.
missing (Optional[List[Span]]): Spans with missing/unknown entity
information.
outside (Optional[List[Span]]): Spans outside of entities (O in IOB).
default (str): How to set entity annotation for tokens outside of any
provided spans. Options: "blocked", "missing", "outside" and
"unmodified" (preserve current state). Defaults to "outside".
"""
if default not in SetEntsDefault.values():
raise ValueError(Errors.E1011.format(default=default, modes=", ".join(SetEntsDefault)))
# Ignore spans with missing labels
entities = [ent for ent in entities if ent.label > 0]
if blocked is None:
blocked = tuple()
if missing is None:
missing = tuple()
if outside is None:
outside = tuple()
# Find all tokens covered by spans and check that none are overlapping
cdef int i
seen_tokens = set()
for span in itertools.chain.from_iterable([entities, blocked, missing, outside]):
if not isinstance(span, Span):
raise ValueError(Errors.E1012.format(span=span))
for i in range(span.start, span.end):
if i in seen_tokens:
raise ValueError(Errors.E1010.format(i=i))
seen_tokens.add(i)
# Set all specified entity information
for span in entities:
for i in range(span.start, span.end):
if i == span.start:
self.c[i].ent_iob = 3
else:
self.c[i].ent_iob = 1
self.c[i].ent_type = span.label
self.c[i].ent_kb_id = span.kb_id
for span in blocked:
for i in range(span.start, span.end):
self.c[i].ent_iob = 3
self.c[i].ent_type = 0
for span in missing:
for i in range(span.start, span.end):
self.c[i].ent_iob = 0
self.c[i].ent_type = 0
for span in outside:
for i in range(span.start, span.end):
self.c[i].ent_iob = 2
self.c[i].ent_type = 0
# Set tokens outside of all provided spans
if default != SetEntsDefault.unmodified:
for i in range(self.length):
# default values
entity_type = 0
kb_id = 0
if i not in seen_tokens:
self.c[i].ent_type = 0
if default == SetEntsDefault.outside:
self.c[i].ent_iob = 2
elif default == SetEntsDefault.missing:
self.c[i].ent_iob = 0
elif default == SetEntsDefault.blocked:
self.c[i].ent_iob = 3
# Set ent_iob to Missing (0) by default unless this token was nered before
ent_iob = 0
if self.c[i].ent_iob != 0:
ent_iob = 2
# overwrite if the token was part of a specified entity
if i in tokens_in_ents.keys():
ent_start, ent_end, entity_type, kb_id = tokens_in_ents[i]
if entity_type is None or entity_type <= 0:
# Blocking this token from being overwritten by downstream NER
ent_iob = 3
elif ent_start == i:
# Marking the start of an entity
ent_iob = 3
else:
# Marking the inside of an entity
ent_iob = 1
self.c[i].ent_type = entity_type
self.c[i].ent_kb_id = kb_id
self.c[i].ent_iob = ent_iob
# Fix any resulting inconsistent annotation
for i in range(self.length - 1):
# I must follow B or I: convert I to B
if (self.c[i].ent_iob == 0 or self.c[i].ent_iob == 2) and \
self.c[i+1].ent_iob == 1:
self.c[i+1].ent_iob = 3
# Change of type with BI or II: convert second I to B
if self.c[i].ent_type != self.c[i+1].ent_type and \
(self.c[i].ent_iob == 3 or self.c[i].ent_iob == 1) and \
self.c[i+1].ent_iob == 1:
self.c[i+1].ent_iob = 3
@property
def noun_chunks(self):

12
spacy/training/example.pyx
View File

@ -288,6 +288,7 @@ def _annot2array(vocab, tok_annot, doc_annot):
def _add_entities_to_doc(doc, ner_data):
print(ner_data)
if ner_data is None:
return
elif ner_data == []:
@ -303,9 +304,14 @@ def _add_entities_to_doc(doc, ner_data):
biluo_tags_to_spans(doc, ner_data)
)
elif isinstance(ner_data[0], Span):
# Ugh, this is super messy. Really hard to set O entities
doc.ents = ner_data
doc.ents = [span for span in ner_data if span.label_]
entities = []
missing = []
for span in ner_data:
if span.label:
entities.append(span)
else:
missing.append(span)
doc.set_ents(entities, missing=missing)
else:
raise ValueError(Errors.E973)

17
spacy/training/iob_utils.py
View File

@ -151,9 +151,10 @@ def biluo_tags_to_spans(doc: Doc, tags: Iterable[str]) -> List[Span]:
doc (Doc): The document that the BILUO tags refer to.
entities (iterable): A sequence of BILUO tags with each tag describing one
token. Each tags string will be of the form of either "", "O" or
token. Each tag string will be of the form of either "", "O" or
"{action}-{label}", where action is one of "B", "I", "L", "U".
RETURNS (list): A sequence of Span objects.
RETURNS (list): A sequence of Span objects. Each token with a missing IOB
tag is returned as a Span with an empty label.
"""
token_offsets = tags_to_entities(tags)
spans = []
@ -186,22 +187,18 @@ def tags_to_entities(tags: Iterable[str]) -> List[Tuple[str, int, int]]:
entities = []
start = None
for i, tag in enumerate(tags):
if tag is None:
continue
if tag.startswith("O"):
if tag is None or tag.startswith("-"):
# TODO: We shouldn't be getting these malformed inputs. Fix this.
if start is not None:
start = None
else:
entities.append(("", i, i))
continue
elif tag == "-":
continue
elif tag.startswith("O"):
pass
elif tag.startswith("I"):
if start is None:
raise ValueError(Errors.E067.format(start="I", tags=tags[: i + 1]))
continue
if tag.startswith("U"):
elif tag.startswith("U"):
entities.append((tag[2:], i, i))
elif tag.startswith("B"):
start = i

47
website/docs/api/data-formats.md
View File

@ -180,26 +180,27 @@ single corpus once and then divide it up into `train` and `dev` partitions.
This section defines settings and controls for the training and evaluation
process that are used when you run [`spacy train`](/api/cli#train).
| Name | Description |
| --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ |
| `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. ~~Optional[str]~~ |
| `lookups` | Additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `null`. ~~Optional[Lookups]~~ |
| `max_epochs` | Maximum number of epochs to train for. Defaults to `0`. ~~int~~ |
| `max_steps` | Maximum number of update steps to train for. Defaults to `20000`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `patience` | How many steps to continue without improvement in evaluation score. Defaults to `1600`. ~~int~~ |
| `raw_text` | Optional path to a jsonl file with unlabelled text documents for a [rehearsal](/api/language#rehearse) step. Defaults to variable `${paths.raw}`. ~~Optional[str]~~ |
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ |
| `vectors` | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vocab`](/api/cli#init-vocab). Defaults to `null`. ~~Optional[str]~~ |
| Name | Description |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `accumulate_gradient` | Whether to divide the batch up into substeps. Defaults to `1`. ~~int~~ |
| `batcher` | Callable that takes an iterator of [`Doc`](/api/doc) objects and yields batches of `Doc`s. Defaults to [`batch_by_words`](/api/top-level#batch_by_words). ~~Callable[[Iterator[Doc], Iterator[List[Doc]]]]~~ |
| `before_to_disk` | Optional callback to modify `nlp` object right before it is saved to disk during and after training. Can be used to remove or reset config values or disable components. Defaults to `null`. ~~Optional[Callable[[Language], Language]]~~ |
| `dev_corpus` | Dot notation of the config location defining the dev corpus. Defaults to `corpora.dev`. ~~str~~ |
| `dropout` | The dropout rate. Defaults to `0.1`. ~~float~~ |
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. ~~Optional[str]~~ |
| `lookups` | Additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `null`. ~~Optional[Lookups]~~ |
| `max_epochs` | Maximum number of epochs to train for. Defaults to `0`. ~~int~~ |
| `max_steps` | Maximum number of update steps to train for. Defaults to `20000`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
| `patience` | How many steps to continue without improvement in evaluation score. Defaults to `1600`. ~~int~~ |
| `raw_text` | Optional path to a jsonl file with unlabelled text documents for a [rehearsal](/api/language#rehearse) step. Defaults to variable `${paths.raw}`. ~~Optional[str]~~ |
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ |
| `vectors` | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vocab`](/api/cli#init-vocab). Defaults to `null`. ~~Optional[str]~~ |
### pretraining {#config-pretraining tag="section,optional"}
@ -275,8 +276,8 @@ $ python -m spacy convert ./data.json ./output.spacy
> entity label, prefixed by the BILUO marker. For example `"B-ORG"` describes
> the first token of a multi-token `ORG` entity and `"U-PERSON"` a single token
> representing a `PERSON` entity. The
> [`offsets_to_biluo_tags`](/api/top-level#offsets_to_biluo_tags) function
> can help you convert entity offsets to the right format.
> [`offsets_to_biluo_tags`](/api/top-level#offsets_to_biluo_tags) function can
> help you convert entity offsets to the right format.
```python
### Example structure
@ -518,7 +519,7 @@ source of truth** used for loading a pipeline.
> "ner": ["PERSON", "ORG", "PRODUCT"],
> "textcat": ["POSITIVE", "NEGATIVE"]
> },
> "accuracy": {
> "performance": {
> "ents_f": 82.7300930714,
> "ents_p": 82.135523614,
> "ents_r": 83.3333333333,

25
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@ -219,6 +219,30 @@ alignment mode `"strict".
| `alignment_mode` | How character indices snap to token boundaries. Options: `"strict"` (no snapping), `"contract"` (span of all tokens completely within the character span), `"expand"` (span of all tokens at least partially covered by the character span). Defaults to `"strict"`. ~~str~~ |
| **RETURNS** | The newly constructed object or `None`. ~~Optional[Span]~~ |
## Doc.set_ents {#ents tag="method" new="3"}
Set the named entities in the document.
> #### Example
>
> ```python
> from spacy.tokens import Span
> doc = nlp("Mr. Best flew to New York on Saturday morning.")
> doc.set_ents([Span(doc, 0, 2, "PERSON")])
> ents = list(doc.ents)
> assert ents[0].label_ == "PERSON"
> assert ents[0].text == "Mr. Best"
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| entities | Spans with labels to set as entities. ~~List[Span]~~ |
| _keyword-only_ | |
| blocked | Spans to set as "blocked" (never an entity) for spacy's built-in NER component. Other components may ignore this setting. ~~Optional[List[Span]]~~ |
| missing | Spans with missing/unknown entity information. ~~Optional[List[Span]]~~ |
| outside | Spans outside of entities (O in IOB). ~~Optional[List[Span]]~~ |
| default | How to set entity annotation for tokens outside of any provided spans. Options: "blocked", "missing", "outside" and "unmodified" (preserve current state). Defaults to "outside". ~~str~~ |
## Doc.similarity {#similarity tag="method" model="vectors"}
Make a semantic similarity estimate. The default estimate is cosine similarity
@ -542,7 +566,6 @@ objects, if the entity recognizer has been applied.
> ```python
> doc = nlp("Mr. Best flew to New York on Saturday morning.")
> ents = list(doc.ents)
> assert ents[0].label == 346
> assert ents[0].label_ == "PERSON"
> assert ents[0].text == "Mr. Best"
> ```

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@ -1,13 +1,13 @@
import { Help } from 'components/typography'; import Link from 'components/link'
<!-- TODO: update, add project template -->
<!-- TODO: update numbers -->
<figure>
| System | Parser | Tagger | NER | WPS<br />CPU <Help>words per second on CPU, higher is better</Help> | WPS<br/>GPU <Help>words per second on GPU, higher is better</Help> |
| Pipeline | Parser | Tagger | NER | WPS<br />CPU <Help>words per second on CPU, higher is better</Help> | WPS<br/>GPU <Help>words per second on GPU, higher is better</Help> |
| ---------------------------------------------------------- | -----: | -----: | ---: | ------------------------------------------------------------------: | -----------------------------------------------------------------: |
| [`en_core_web_trf`](/models/en#en_core_web_trf) (spaCy v3) | | | | | 6k |
| [`en_core_web_lg`](/models/en#en_core_web_lg) (spaCy v3) | | | | | |
| [`en_core_web_lg`](/models/en#en_core_web_lg) (spaCy v3) | 92.1 | 97.4 | 87.0 | 7k | |
| `en_core_web_lg` (spaCy v2) | 91.9 | 97.2 | 85.9 | 10k | |
<figcaption class="caption">
@ -21,10 +21,10 @@ import { Help } from 'components/typography'; import Link from 'components/link'
<figure>
| Named Entity Recognition Model | OntoNotes | CoNLL '03 |
| Named Entity Recognition System | OntoNotes | CoNLL '03 |
| ------------------------------------------------------------------------------ | --------: | --------: |
| spaCy RoBERTa (2020) | | 92.2 |
| spaCy CNN (2020) | | 88.4 |
| spaCy CNN (2020) | 85.3 | 88.4 |
| spaCy CNN (2017) | 86.4 | |
| [Stanza](https://stanfordnlp.github.io/stanza/) (StanfordNLP)<sup>1</sup> | 88.8 | 92.1 |
| <Link to="https://github.com/flairNLP/flair" hideIcon>Flair</Link><sup>2</sup> | 89.7 | 93.1 |

2
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@ -235,8 +235,6 @@ The `Transformer` component sets the
[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
which lets you access the transformers outputs at runtime.
<!-- TODO: update/confirm once we have final models trained -->
```cli
$ python -m spacy download en_core_trf_lg
```

2
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@ -63,7 +63,7 @@ import Benchmarks from 'usage/\_benchmarks-models.md'
<figure>
| System | UAS | LAS |
| Dependency Parsing System | UAS | LAS |
| ------------------------------------------------------------------------------ | ---: | ---: |
| spaCy RoBERTa (2020)<sup>1</sup> | 96.8 | 95.0 |
| spaCy CNN (2020)<sup>1</sup> | 93.7 | 91.8 |

9
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@ -1654,9 +1654,12 @@ The [`SentenceRecognizer`](/api/sentencerecognizer) is a simple statistical
component that only provides sentence boundaries. Along with being faster and
smaller than the parser, its primary advantage is that it's easier to train
because it only requires annotated sentence boundaries rather than full
dependency parses.
<!-- TODO: update/confirm usage once we have final models trained -->
dependency parses. spaCy's [trained pipelines](/models) include both a parser
and a trained sentence segmenter, which is
[disabled](/usage/processing-pipelines#disabling) by default. If you only need
sentence boundaries and no parser, you can use the `enable` and `disable`
arguments on [`spacy.load`](/api/top-level#spacy.load) to enable the senter and
disable the parser.
> #### senter vs. parser
>

7
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@ -253,8 +253,6 @@ different mechanisms you can use:
Disabled and excluded component names can be provided to
[`spacy.load`](/api/top-level#spacy.load) as a list.
<!-- TODO: update with info on our models shipped with optional components -->
> #### 💡 Optional pipeline components
>
> The `disable` mechanism makes it easy to distribute pipeline packages with
@ -262,6 +260,11 @@ Disabled and excluded component names can be provided to
> your pipeline may include a statistical _and_ a rule-based component for
> sentence segmentation, and you can choose which one to run depending on your
> use case.
>
> For example, spaCy's [trained pipelines](/models) like
> [`en_core_web_sm`](/models/en#en_core_web_sm) contain both a `parser` and
> `senter` that perform sentence segmentation, but the `senter` is disabled by
> default.
```python
# Load the pipeline without the entity recognizer

5
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@ -733,7 +733,10 @@ workflows, but only one can be tracked by DVC.
<Infobox title="This section is still under construction" emoji="🚧" variant="warning">
The Prodigy integration will require a nightly version of Prodigy that supports
spaCy v3+.
spaCy v3+. You can already use annotations created with Prodigy in spaCy v3 by
exporting your data with
[`data-to-spacy`](https://prodi.gy/docs/recipes#data-to-spacy) and running
[`spacy convert`](/api/cli#convert) to convert it to the binary format.
</Infobox>

13
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@ -32,11 +32,17 @@ const MODEL_META = {
las: 'Labelled dependencies',
token_acc: 'Tokenization',
tok: 'Tokenization',
lemma: 'Statistical lemmatization',
morph: 'Morphological analysis',
tags_acc: 'Part-of-speech tags (fine grained tags, Token.tag)',
tag: 'Part-of-speech tags (fine grained tags, Token.tag)',
pos: 'Part-of-speech tags (coarse grained tags, Token.pos)',
ents_f: 'Named entities (F-score)',
ents_p: 'Named entities (precision)',
ents_r: 'Named entities (recall)',
ner_f: 'Named entities (F-score)',
ner_p: 'Named entities (precision)',
ner_r: 'Named entities (recall)',
sent_f: 'Sentence segmentation (F-score)',
sent_p: 'Sentence segmentation (precision)',
sent_r: 'Sentence segmentation (recall)',
@ -88,11 +94,12 @@ function formatVectors(data) {
}
function formatAccuracy(data) {
const exclude = ['speed']
if (!data) return []
return Object.keys(data)
.map(label => {
const value = data[label]
return isNaN(value)
return isNaN(value) || exclude.includes(label)
? null
: {
label,
@ -109,6 +116,7 @@ function formatModelMeta(data) {
version: data.version,
sizeFull: data.size,
pipeline: data.pipeline,
components: data.components,
notes: data.notes,
description: data.description,
sources: data.sources,
@ -117,7 +125,8 @@ function formatModelMeta(data) {
license: data.license,
labels: isEmptyObj(data.labels) ? null : data.labels,
vectors: formatVectors(data.vectors),
accuracy: formatAccuracy(data.accuracy),
// TODO: remove accuracy fallback
accuracy: formatAccuracy(data.accuracy || data.performance),
}
}