spaCy/spacy/language.py

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# coding: utf8
from __future__ import absolute_import, unicode_literals
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import random
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import itertools
import weakref
import functools
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from collections import OrderedDict
from contextlib import contextmanager
💫 Better support for semi-supervised learning (#3035) The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz
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from copy import copy, deepcopy
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from thinc.neural import Model
import srsly
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from .tokenizer import Tokenizer
from .vocab import Vocab
from .lemmatizer import Lemmatizer
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from .pipeline import DependencyParser, Tensorizer, Tagger, EntityRecognizer
from .pipeline import SimilarityHook, TextCategorizer, SentenceSegmenter
from .pipeline import merge_noun_chunks, merge_entities, merge_subtokens
from .pipeline import EntityRuler
from .compat import izip, basestring_
from .gold import GoldParse
from .scorer import Scorer
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from ._ml import link_vectors_to_models, create_default_optimizer
from .attrs import IS_STOP
from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
from .lang.punctuation import TOKENIZER_INFIXES
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from .lang.tokenizer_exceptions import TOKEN_MATCH
from .lang.tag_map import TAG_MAP
from .lang.lex_attrs import LEX_ATTRS, is_stop
from .errors import Errors
from . import util
from . import about
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class BaseDefaults(object):
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@classmethod
def create_lemmatizer(cls, nlp=None):
return Lemmatizer(
cls.lemma_index, cls.lemma_exc, cls.lemma_rules, cls.lemma_lookup
)
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@classmethod
def create_vocab(cls, nlp=None):
lemmatizer = cls.create_lemmatizer(nlp)
lex_attr_getters = dict(cls.lex_attr_getters)
# This is messy, but it's the minimal working fix to Issue #639.
lex_attr_getters[IS_STOP] = functools.partial(is_stop, stops=cls.stop_words)
vocab = Vocab(
lex_attr_getters=lex_attr_getters,
tag_map=cls.tag_map,
lemmatizer=lemmatizer,
)
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for tag_str, exc in cls.morph_rules.items():
for orth_str, attrs in exc.items():
vocab.morphology.add_special_case(tag_str, orth_str, attrs)
return vocab
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@classmethod
def create_tokenizer(cls, nlp=None):
rules = cls.tokenizer_exceptions
token_match = cls.token_match
prefix_search = (
util.compile_prefix_regex(cls.prefixes).search if cls.prefixes else None
)
suffix_search = (
util.compile_suffix_regex(cls.suffixes).search if cls.suffixes else None
)
infix_finditer = (
util.compile_infix_regex(cls.infixes).finditer if cls.infixes else None
)
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vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
return Tokenizer(
vocab,
rules=rules,
prefix_search=prefix_search,
suffix_search=suffix_search,
infix_finditer=infix_finditer,
token_match=token_match,
)
pipe_names = ["tagger", "parser", "ner"]
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token_match = TOKEN_MATCH
prefixes = tuple(TOKENIZER_PREFIXES)
suffixes = tuple(TOKENIZER_SUFFIXES)
infixes = tuple(TOKENIZER_INFIXES)
tag_map = dict(TAG_MAP)
tokenizer_exceptions = {}
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stop_words = set()
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lemma_rules = {}
lemma_exc = {}
lemma_index = {}
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lemma_lookup = {}
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morph_rules = {}
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lex_attr_getters = LEX_ATTRS
syntax_iterators = {}
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class Language(object):
"""A text-processing pipeline. Usually you'll load this once per process,
and pass the instance around your application.
Defaults (class): Settings, data and factory methods for creating the `nlp`
object and processing pipeline.
lang (unicode): Two-letter language ID, i.e. ISO code.
"""
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Defaults = BaseDefaults
lang = None
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factories = {
"tokenizer": lambda nlp: nlp.Defaults.create_tokenizer(nlp),
"tensorizer": lambda nlp, **cfg: Tensorizer(nlp.vocab, **cfg),
"tagger": lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
"parser": lambda nlp, **cfg: DependencyParser(nlp.vocab, **cfg),
"ner": lambda nlp, **cfg: EntityRecognizer(nlp.vocab, **cfg),
"similarity": lambda nlp, **cfg: SimilarityHook(nlp.vocab, **cfg),
"textcat": lambda nlp, **cfg: TextCategorizer(nlp.vocab, **cfg),
"sentencizer": lambda nlp, **cfg: SentenceSegmenter(nlp.vocab, **cfg),
"merge_noun_chunks": lambda nlp, **cfg: merge_noun_chunks,
"merge_entities": lambda nlp, **cfg: merge_entities,
"merge_subtokens": lambda nlp, **cfg: merge_subtokens,
"entity_ruler": lambda nlp, **cfg: EntityRuler(nlp, **cfg),
}
def __init__(
self, vocab=True, make_doc=True, max_length=10 ** 6, meta={}, **kwargs
):
"""Initialise a Language object.
vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
`Language.Defaults.create_vocab`.
make_doc (callable): A function that takes text and returns a `Doc`
object. Usually a `Tokenizer`.
meta (dict): Custom meta data for the Language class. Is written to by
models to add model meta data.
max_length (int) :
Maximum number of characters in a single text. The current v2 models
may run out memory on extremely long texts, due to large internal
allocations. You should segment these texts into meaningful units,
e.g. paragraphs, subsections etc, before passing them to spaCy.
Default maximum length is 1,000,000 characters (1mb). As a rule of
thumb, if all pipeline components are enabled, spaCy's default
models currently requires roughly 1GB of temporary memory per
100,000 characters in one text.
RETURNS (Language): The newly constructed object.
"""
user_factories = util.get_entry_points("spacy_factories")
self.factories.update(user_factories)
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self._meta = dict(meta)
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self._path = None
if vocab is True:
factory = self.Defaults.create_vocab
vocab = factory(self, **meta.get("vocab", {}))
if vocab.vectors.name is None:
vocab.vectors.name = meta.get("vectors", {}).get("name")
self.vocab = vocab
if make_doc is True:
factory = self.Defaults.create_tokenizer
make_doc = factory(self, **meta.get("tokenizer", {}))
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self.tokenizer = make_doc
self.pipeline = []
self.max_length = max_length
self._optimizer = None
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@property
def path(self):
return self._path
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@property
def meta(self):
self._meta.setdefault("lang", self.vocab.lang)
self._meta.setdefault("name", "model")
self._meta.setdefault("version", "0.0.0")
self._meta.setdefault("spacy_version", ">={}".format(about.__version__))
self._meta.setdefault("description", "")
self._meta.setdefault("author", "")
self._meta.setdefault("email", "")
self._meta.setdefault("url", "")
self._meta.setdefault("license", "")
self._meta["vectors"] = {
"width": self.vocab.vectors_length,
"vectors": len(self.vocab.vectors),
"keys": self.vocab.vectors.n_keys,
"name": self.vocab.vectors.name,
}
self._meta["pipeline"] = self.pipe_names
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return self._meta
@meta.setter
def meta(self, value):
self._meta = value
# Conveniences to access pipeline components
@property
def tensorizer(self):
return self.get_pipe("tensorizer")
@property
def tagger(self):
return self.get_pipe("tagger")
@property
def parser(self):
return self.get_pipe("parser")
@property
def entity(self):
return self.get_pipe("ner")
@property
def matcher(self):
return self.get_pipe("matcher")
@property
def pipe_names(self):
"""Get names of available pipeline components.
RETURNS (list): List of component name strings, in order.
"""
return [pipe_name for pipe_name, _ in self.pipeline]
def get_pipe(self, name):
"""Get a pipeline component for a given component name.
name (unicode): Name of pipeline component to get.
RETURNS (callable): The pipeline component.
"""
for pipe_name, component in self.pipeline:
if pipe_name == name:
return component
raise KeyError(Errors.E001.format(name=name, opts=self.pipe_names))
def create_pipe(self, name, config=dict()):
"""Create a pipeline component from a factory.
name (unicode): Factory name to look up in `Language.factories`.
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config (dict): Configuration parameters to initialise component.
RETURNS (callable): Pipeline component.
"""
if name not in self.factories:
if name == "sbd":
raise KeyError(Errors.E108.format(name=name))
else:
raise KeyError(Errors.E002.format(name=name))
factory = self.factories[name]
return factory(self, **config)
def add_pipe(
self, component, name=None, before=None, after=None, first=None, last=None
):
"""Add a component to the processing pipeline. Valid components are
callables that take a `Doc` object, modify it and return it. Only one
of before/after/first/last can be set. Default behaviour is "last".
component (callable): The pipeline component.
name (unicode): Name of pipeline component. Overwrites existing
component.name attribute if available. If no name is set and
the component exposes no name attribute, component.__name__ is
used. An error is raised if a name already exists in the pipeline.
before (unicode): Component name to insert component directly before.
after (unicode): Component name to insert component directly after.
first (bool): Insert component first / not first in the pipeline.
last (bool): Insert component last / not last in the pipeline.
EXAMPLE:
>>> nlp.add_pipe(component, before='ner')
>>> nlp.add_pipe(component, name='custom_name', last=True)
"""
if not hasattr(component, "__call__"):
msg = Errors.E003.format(component=repr(component), name=name)
if isinstance(component, basestring_) and component in self.factories:
msg += Errors.E004.format(component=component)
raise ValueError(msg)
if name is None:
if hasattr(component, "name"):
name = component.name
elif hasattr(component, "__name__"):
name = component.__name__
elif hasattr(component, "__class__") and hasattr(
component.__class__, "__name__"
):
name = component.__class__.__name__
else:
name = repr(component)
if name in self.pipe_names:
raise ValueError(Errors.E007.format(name=name, opts=self.pipe_names))
if sum([bool(before), bool(after), bool(first), bool(last)]) >= 2:
raise ValueError(Errors.E006)
pipe = (name, component)
if last or not any([first, before, after]):
self.pipeline.append(pipe)
elif first:
self.pipeline.insert(0, pipe)
elif before and before in self.pipe_names:
self.pipeline.insert(self.pipe_names.index(before), pipe)
elif after and after in self.pipe_names:
self.pipeline.insert(self.pipe_names.index(after) + 1, pipe)
else:
raise ValueError(
Errors.E001.format(name=before or after, opts=self.pipe_names)
)
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def has_pipe(self, name):
"""Check if a component name is present in the pipeline. Equivalent to
`name in nlp.pipe_names`.
name (unicode): Name of the component.
RETURNS (bool): Whether a component of the name exists in the pipeline.
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"""
return name in self.pipe_names
def replace_pipe(self, name, component):
"""Replace a component in the pipeline.
name (unicode): Name of the component to replace.
component (callable): Pipeline component.
"""
if name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
self.pipeline[self.pipe_names.index(name)] = (name, component)
def rename_pipe(self, old_name, new_name):
"""Rename a pipeline component.
old_name (unicode): Name of the component to rename.
new_name (unicode): New name of the component.
"""
if old_name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=old_name, opts=self.pipe_names))
if new_name in self.pipe_names:
raise ValueError(Errors.E007.format(name=new_name, opts=self.pipe_names))
i = self.pipe_names.index(old_name)
self.pipeline[i] = (new_name, self.pipeline[i][1])
def remove_pipe(self, name):
"""Remove a component from the pipeline.
name (unicode): Name of the component to remove.
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RETURNS (tuple): A `(name, component)` tuple of the removed component.
"""
if name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
return self.pipeline.pop(self.pipe_names.index(name))
def __call__(self, text, disable=[]):
"""Apply the pipeline to some text. The text can span multiple sentences,
and can contain arbtrary whitespace. Alignment into the original string
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is preserved.
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text (unicode): The text to be processed.
disable (list): Names of the pipeline components to disable.
RETURNS (Doc): A container for accessing the annotations.
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EXAMPLE:
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>>> tokens = nlp('An example sentence. Another example sentence.')
>>> tokens[0].text, tokens[0].head.tag_
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('An', 'NN')
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"""
💫 Port master changes over to develop (#2979) * Create aryaprabhudesai.md (#2681) * Update _install.jade (#2688) Typo fix: "models" -> "model" * Add FAC to spacy.explain (resolves #2706) * Remove docstrings for deprecated arguments (see #2703) * When calling getoption() in conftest.py, pass a default option (#2709) * When calling getoption() in conftest.py, pass a default option This is necessary to allow testing an installed spacy by running: pytest --pyargs spacy * Add contributor agreement * update bengali token rules for hyphen and digits (#2731) * Less norm computations in token similarity (#2730) * Less norm computations in token similarity * Contributor agreement * Remove ')' for clarity (#2737) Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know. * added contributor agreement for mbkupfer (#2738) * Basic support for Telugu language (#2751) * Lex _attrs for polish language (#2750) * Signed spaCy contributor agreement * Added polish version of english lex_attrs * Introduces a bulk merge function, in order to solve issue #653 (#2696) * Fix comment * Introduce bulk merge to increase performance on many span merges * Sign contributor agreement * Implement pull request suggestions * Describe converters more explicitly (see #2643) * Add multi-threading note to Language.pipe (resolves #2582) [ci skip] * Fix formatting * Fix dependency scheme docs (closes #2705) [ci skip] * Don't set stop word in example (closes #2657) [ci skip] * Add words to portuguese language _num_words (#2759) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Update Indonesian model (#2752) * adding e-KTP in tokenizer exceptions list * add exception token * removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception * add tokenizer exceptions list * combining base_norms with norm_exceptions * adding norm_exception * fix double key in lemmatizer * remove unused import on punctuation.py * reformat stop_words to reduce number of lines, improve readibility * updating tokenizer exception * implement is_currency for lang/id * adding orth_first_upper in tokenizer_exceptions * update the norm_exception list * remove bunch of abbreviations * adding contributors file * Fixed spaCy+Keras example (#2763) * bug fixes in keras example * created contributor agreement * Adding French hyphenated first name (#2786) * Fix typo (closes #2784) * Fix typo (#2795) [ci skip] Fixed typo on line 6 "regcognizer --> recognizer" * Adding basic support for Sinhala language. (#2788) * adding Sinhala language package, stop words, examples and lex_attrs. * Adding contributor agreement * Updating contributor agreement * Also include lowercase norm exceptions * Fix error (#2802) * Fix error ValueError: cannot resize an array that references or is referenced by another array in this way. Use the resize function * added spaCy Contributor Agreement * Add charlax's contributor agreement (#2805) * agreement of contributor, may I introduce a tiny pl languge contribution (#2799) * Contributors agreement * Contributors agreement * Contributors agreement * Add jupyter=True to displacy.render in documentation (#2806) * Revert "Also include lowercase norm exceptions" This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e. * Remove deprecated encoding argument to msgpack * Set up dependency tree pattern matching skeleton (#2732) * Fix bug when too many entity types. Fixes #2800 * Fix Python 2 test failure * Require older msgpack-numpy * Restore encoding arg on msgpack-numpy * Try to fix version pin for msgpack-numpy * Update Portuguese Language (#2790) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols * Extended punctuation and norm_exceptions in the Portuguese language * Correct error in spacy universe docs concerning spacy-lookup (#2814) * Update Keras Example for (Parikh et al, 2016) implementation (#2803) * bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround * Fix typo (closes #2815) [ci skip] * Update regex version dependency * Set version to 2.0.13.dev3 * Skip seemingly problematic test * Remove problematic test * Try previous version of regex * Revert "Remove problematic test" This reverts commit bdebbef45552d698d390aa430b527ee27830f11b. * Unskip test * Try older version of regex * 💫 Update training examples and use minibatching (#2830) <!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Visual C++ link updated (#2842) (closes #2841) [ci skip] * New landing page * Add contribution agreement * Correcting lang/ru/examples.py (#2845) * Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement * Correct some grammatical inaccuracies in lang\ru\examples.py * Move contributor agreement to separate file * Set version to 2.0.13.dev4 * Add Persian(Farsi) language support (#2797) * Also include lowercase norm exceptions * Remove in favour of https://github.com/explosion/spaCy/graphs/contributors * Rule-based French Lemmatizer (#2818) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class. ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> - Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version. - Add several files containing exhaustive list of words for each part of speech - Add some lemma rules - Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX - Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned - Modify the lemmatize function to check in lookup table as a last resort - Init files are updated so the model can support all the functionalities mentioned above - Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [X] I have submitted the spaCy Contributor Agreement. - [X] I ran the tests, and all new and existing tests passed. - [X] My changes don't require a change to the documentation, or if they do, I've added all required information. * Set version to 2.0.13 * Fix formatting and consistency * Update docs for new version [ci skip] * Increment version [ci skip] * Add info on wheels [ci skip] * Adding "This is a sentence" example to Sinhala (#2846) * Add wheels badge * Update badge [ci skip] * Update README.rst [ci skip] * Update murmurhash pin * Increment version to 2.0.14.dev0 * Update GPU docs for v2.0.14 * Add wheel to setup_requires * Import prefer_gpu and require_gpu functions from Thinc * Add tests for prefer_gpu() and require_gpu() * Update requirements and setup.py * Workaround bug in thinc require_gpu * Set version to v2.0.14 * Update push-tag script * Unhack prefer_gpu * Require thinc 6.10.6 * Update prefer_gpu and require_gpu docs [ci skip] * Fix specifiers for GPU * Set version to 2.0.14.dev1 * Set version to 2.0.14 * Update Thinc version pin * Increment version * Fix msgpack-numpy version pin * Increment version * Update version to 2.0.16 * Update version [ci skip] * Redundant ')' in the Stop words' example (#2856) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [ ] I have submitted the spaCy Contributor Agreement. - [ ] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information. * Documentation improvement regarding joblib and SO (#2867) Some documentation improvements ## Description 1. Fixed the dead URL to joblib 2. Fixed Stack Overflow brand name (with space) ### Types of change Documentation ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * raise error when setting overlapping entities as doc.ents (#2880) * Fix out-of-bounds access in NER training The helper method state.B(1) gets the index of the first token of the buffer, or -1 if no such token exists. Normally this is safe because we pass this to functions like state.safe_get(), which returns an empty token. Here we used it directly as an array index, which is not okay! This error may have been the cause of out-of-bounds access errors during training. Similar errors may still be around, so much be hunted down. Hunting this one down took a long time...I printed out values across training runs and diffed, looking for points of divergence between runs, when no randomness should be allowed. * Change PyThaiNLP Url (#2876) * Fix missing comma * Add example showing a fix-up rule for space entities * Set version to 2.0.17.dev0 * Update regex version * Revert "Update regex version" This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a. * Try setting older regex version, to align with conda * Set version to 2.0.17 * Add spacy-js to universe [ci-skip] * Add spacy-raspberry to universe (closes #2889) * Add script to validate universe json [ci skip] * Removed space in docs + added contributor indo (#2909) * - removed unneeded space in documentation * - added contributor info * Allow input text of length up to max_length, inclusive (#2922) * Include universe spec for spacy-wordnet component (#2919) * feat: include universe spec for spacy-wordnet component * chore: include spaCy contributor agreement * Minor formatting changes [ci skip] * Fix image [ci skip] Twitter URL doesn't work on live site * Check if the word is in one of the regular lists specific to each POS (#2886) * 💫 Create random IDs for SVGs to prevent ID clashes (#2927) Resolves #2924. ## Description Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.) ### Types of change bug fix ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix typo [ci skip] * fixes symbolic link on py3 and windows (#2949) * fixes symbolic link on py3 and windows during setup of spacy using command python -m spacy link en_core_web_sm en closes #2948 * Update spacy/compat.py Co-Authored-By: cicorias <cicorias@users.noreply.github.com> * Fix formatting * Update universe [ci skip] * Catalan Language Support (#2940) * Catalan language Support * Ddding Catalan to documentation * Sort languages alphabetically [ci skip] * Update tests for pytest 4.x (#2965) <!--- Provide a general summary of your changes in the title. --> ## Description - [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize)) - [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here) ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix regex pin to harmonize with conda (#2964) * Update README.rst * Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977) Fixes #2976 * Fix typo * Fix typo * Remove duplicate file * Require thinc 7.0.0.dev2 Fixes bug in gpu_ops that would use cupy instead of numpy on CPU * Add missing import * Fix error IDs * Fix tests
2018-11-29 18:30:29 +03:00
if len(text) > self.max_length:
raise ValueError(
Errors.E088.format(length=len(text), max_length=self.max_length)
)
doc = self.make_doc(text)
for name, proc in self.pipeline:
if name in disable:
continue
if not hasattr(proc, "__call__"):
raise ValueError(Errors.E003.format(component=type(proc), name=name))
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doc = proc(doc)
if doc is None:
raise ValueError(Errors.E005.format(name=name))
return doc
2015-08-25 16:37:17 +03:00
2017-10-25 14:46:41 +03:00
def disable_pipes(self, *names):
"""Disable one or more pipeline components. If used as a context
manager, the pipeline will be restored to the initial state at the end
of the block. Otherwise, a DisabledPipes object is returned, that has
a `.restore()` method you can use to undo your changes.
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EXAMPLE:
>>> nlp.add_pipe('parser')
>>> nlp.add_pipe('tagger')
>>> with nlp.disable_pipes('parser', 'tagger'):
>>> assert not nlp.has_pipe('parser')
>>> assert nlp.has_pipe('parser')
>>> disabled = nlp.disable_pipes('parser')
>>> assert len(disabled) == 1
>>> assert not nlp.has_pipe('parser')
>>> disabled.restore()
>>> assert nlp.has_pipe('parser')
"""
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return DisabledPipes(self, *names)
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def make_doc(self, text):
return self.tokenizer(text)
def update(self, docs, golds, drop=0.0, sgd=None, losses=None):
"""Update the models in the pipeline.
docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
EXAMPLE:
>>> with nlp.begin_training(gold) as (trainer, optimizer):
>>> for epoch in trainer.epochs(gold):
>>> for docs, golds in epoch:
>>> state = nlp.update(docs, golds, sgd=optimizer)
"""
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if len(docs) != len(golds):
raise IndexError(Errors.E009.format(n_docs=len(docs), n_golds=len(golds)))
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if len(docs) == 0:
return
if sgd is None:
if self._optimizer is None:
self._optimizer = create_default_optimizer(Model.ops)
sgd = self._optimizer
# Allow dict of args to GoldParse, instead of GoldParse objects.
gold_objs = []
doc_objs = []
for doc, gold in zip(docs, golds):
if isinstance(doc, basestring_):
doc = self.make_doc(doc)
if not isinstance(gold, GoldParse):
gold = GoldParse(doc, **gold)
doc_objs.append(doc)
gold_objs.append(gold)
golds = gold_objs
docs = doc_objs
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grads = {}
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def get_grads(W, dW, key=None):
grads[key] = (W, dW)
get_grads.alpha = sgd.alpha
get_grads.b1 = sgd.b1
get_grads.b2 = sgd.b2
pipes = list(self.pipeline)
random.shuffle(pipes)
for name, proc in pipes:
if not hasattr(proc, "update"):
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continue
grads = {}
proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
💫 Better support for semi-supervised learning (#3035) The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz
2018-12-10 18:25:33 +03:00
def rehearse(self, docs, sgd=None, losses=None, config=None):
"""Make a "rehearsal" update to the models in the pipeline, to prevent
forgetting. Rehearsal updates run an initial copy of the model over some
data, and update the model so its current predictions are more like the
initial ones. This is useful for keeping a pre-trained model on-track,
even if you're updating it with a smaller set of examples.
docs (iterable): A batch of `Doc` objects.
drop (float): The droput rate.
sgd (callable): An optimizer.
RETURNS (dict): Results from the update.
EXAMPLE:
>>> raw_text_batches = minibatch(raw_texts)
>>> for labelled_batch in minibatch(zip(train_docs, train_golds)):
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>>> docs, golds = zip(*train_docs)
💫 Better support for semi-supervised learning (#3035) The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz
2018-12-10 18:25:33 +03:00
>>> nlp.update(docs, golds)
>>> raw_batch = [nlp.make_doc(text) for text in next(raw_text_batches)]
>>> nlp.rehearse(raw_batch)
"""
if len(docs) == 0:
return
if sgd is None:
if self._optimizer is None:
self._optimizer = create_default_optimizer(Model.ops)
sgd = self._optimizer
docs = list(docs)
for i, doc in enumerate(docs):
if isinstance(doc, basestring_):
docs[i] = self.make_doc(doc)
pipes = list(self.pipeline)
random.shuffle(pipes)
if config is None:
config = {}
grads = {}
def get_grads(W, dW, key=None):
grads[key] = (W, dW)
get_grads.alpha = sgd.alpha
get_grads.b1 = sgd.b1
get_grads.b2 = sgd.b2
for name, proc in pipes:
if not hasattr(proc, "rehearse"):
continue
grads = {}
proc.rehearse(docs, sgd=get_grads, losses=losses, **config.get(name, {}))
for key, (W, dW) in grads.items():
sgd(W, dW, key=key)
return losses
2017-05-21 17:07:06 +03:00
def preprocess_gold(self, docs_golds):
"""Can be called before training to pre-process gold data. By default,
it handles nonprojectivity and adds missing tags to the tag map.
docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects.
"""
for name, proc in self.pipeline:
if hasattr(proc, "preprocess_gold"):
2017-05-21 17:07:06 +03:00
docs_golds = proc.preprocess_gold(docs_golds)
for doc, gold in docs_golds:
yield doc, gold
def begin_training(self, get_gold_tuples=None, sgd=None, **cfg):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer. Used as a contextmanager.
get_gold_tuples (function): Function returning gold data
**cfg: Config parameters.
RETURNS: An optimizer
"""
if get_gold_tuples is None:
get_gold_tuples = lambda: []
# Populate vocab
else:
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for _, annots_brackets in get_gold_tuples():
for annots, _ in annots_brackets:
for word in annots[1]:
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_ = self.vocab[word] # noqa: F841
if cfg.get("device", -1) >= 0:
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util.use_gpu(cfg["device"])
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if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data)
2017-09-23 04:11:52 +03:00
link_vectors_to_models(self.vocab)
if self.vocab.vectors.data.shape[1]:
cfg["pretrained_vectors"] = self.vocab.vectors.name
if sgd is None:
sgd = create_default_optimizer(Model.ops)
self._optimizer = sgd
for name, proc in self.pipeline:
if hasattr(proc, "begin_training"):
proc.begin_training(
get_gold_tuples, pipeline=self.pipeline, sgd=self._optimizer, **cfg
)
return self._optimizer
2017-05-21 17:07:06 +03:00
💫 Better support for semi-supervised learning (#3035) The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz
2018-12-10 18:25:33 +03:00
def resume_training(self, sgd=None, **cfg):
"""Continue training a pre-trained model.
2018-12-18 15:48:10 +03:00
💫 Better support for semi-supervised learning (#3035) The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting Support semi-supervised learning in spacy train One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing. Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning. Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective. Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage: python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze Implement rehearsal methods for pipeline components The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows: Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model. Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details. Implement rehearsal updates for tagger Implement rehearsal updates for text categoriz
2018-12-10 18:25:33 +03:00
Create and return an optimizer, and initialize "rehearsal" for any pipeline
component that has a .rehearse() method. Rehearsal is used to prevent
models from "forgetting" their initialised "knowledge". To perform
rehearsal, collect samples of text you want the models to retain performance
on, and call nlp.rehearse() with a batch of Doc objects.
"""
if cfg.get("device", -1) >= 0:
util.use_gpu(cfg["device"])
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data)
link_vectors_to_models(self.vocab)
if self.vocab.vectors.data.shape[1]:
cfg["pretrained_vectors"] = self.vocab.vectors.name
if sgd is None:
sgd = create_default_optimizer(Model.ops)
self._optimizer = sgd
for name, proc in self.pipeline:
if hasattr(proc, "_rehearsal_model"):
proc._rehearsal_model = deepcopy(proc.model)
return self._optimizer
def evaluate(self, docs_golds, verbose=False, batch_size=256):
scorer = Scorer()
2017-08-18 23:26:12 +03:00
docs, golds = zip(*docs_golds)
docs = list(docs)
golds = list(golds)
for name, pipe in self.pipeline:
if not hasattr(pipe, "pipe"):
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docs = (pipe(doc) for doc in docs)
2017-08-18 23:26:12 +03:00
else:
docs = pipe.pipe(docs, batch_size=batch_size)
2017-08-18 23:26:12 +03:00
for doc, gold in zip(docs, golds):
if verbose:
print(doc)
scorer.score(doc, gold, verbose=verbose)
2017-05-21 17:07:06 +03:00
return scorer
2017-05-18 12:25:19 +03:00
@contextmanager
def use_params(self, params, **cfg):
"""Replace weights of models in the pipeline with those provided in the
params dictionary. Can be used as a contextmanager, in which case,
models go back to their original weights after the block.
params (dict): A dictionary of parameters keyed by model ID.
**cfg: Config parameters.
EXAMPLE:
>>> with nlp.use_params(optimizer.averages):
>>> nlp.to_disk('/tmp/checkpoint')
"""
contexts = [
pipe.use_params(params)
for name, pipe in self.pipeline
if hasattr(pipe, "use_params")
]
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# TODO: Having trouble with contextlib
# Workaround: these aren't actually context managers atm.
for context in contexts:
try:
next(context)
except StopIteration:
pass
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yield
for context in contexts:
try:
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next(context)
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except StopIteration:
pass
def pipe(
self,
texts,
as_tuples=False,
n_threads=2,
batch_size=1000,
disable=[],
cleanup=False,
):
"""Process texts as a stream, and yield `Doc` objects in order.
texts (iterator): A sequence of texts to process.
as_tuples (bool):
If set to True, inputs should be a sequence of
(text, context) tuples. Output will then be a sequence of
(doc, context) tuples. Defaults to False.
n_threads (int): Currently inactive.
batch_size (int): The number of texts to buffer.
disable (list): Names of the pipeline components to disable.
cleanup (bool): If True, unneeded strings are freed,
to control memory use. Experimental.
YIELDS (Doc): Documents in the order of the original text.
EXAMPLE:
>>> texts = [u'One document.', u'...', u'Lots of documents']
>>> for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
>>> assert doc.is_parsed
"""
if as_tuples:
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text_context1, text_context2 = itertools.tee(texts)
texts = (tc[0] for tc in text_context1)
contexts = (tc[1] for tc in text_context2)
docs = self.pipe(
texts, n_threads=n_threads, batch_size=batch_size, disable=disable
)
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for doc, context in izip(docs, contexts):
yield (doc, context)
return
docs = (self.make_doc(text) for text in texts)
for name, proc in self.pipeline:
if name in disable:
continue
if hasattr(proc, "pipe"):
docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
else:
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# Apply the function, but yield the doc
docs = _pipe(proc, docs)
# Track weakrefs of "recent" documents, so that we can see when they
# expire from memory. When they do, we know we don't need old strings.
# This way, we avoid maintaining an unbounded growth in string entries
# in the string store.
recent_refs = weakref.WeakSet()
old_refs = weakref.WeakSet()
# Keep track of the original string data, so that if we flush old strings,
# we can recover the original ones. However, we only want to do this if we're
# really adding strings, to save up-front costs.
original_strings_data = None
nr_seen = 0
for doc in docs:
yield doc
if cleanup:
recent_refs.add(doc)
if nr_seen < 10000:
old_refs.add(doc)
nr_seen += 1
elif len(old_refs) == 0:
old_refs, recent_refs = recent_refs, old_refs
if original_strings_data is None:
original_strings_data = list(self.vocab.strings)
else:
keys, strings = self.vocab.strings._cleanup_stale_strings(
original_strings_data
)
self.vocab._reset_cache(keys, strings)
self.tokenizer._reset_cache(keys)
nr_seen = 0
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def to_disk(self, path, disable=tuple()):
"""Save the current state to a directory. If a model is loaded, this
will include the model.
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path (unicode or Path): A path to a directory, which will be created if
it doesn't exist. Paths may be strings or `Path`-like objects.
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disable (list): Names of pipeline components to disable and prevent
from being saved.
EXAMPLE:
>>> nlp.to_disk('/path/to/models')
"""
path = util.ensure_path(path)
serializers = OrderedDict(
(
("tokenizer", lambda p: self.tokenizer.to_disk(p, vocab=False)),
("meta.json", lambda p: p.open("w").write(srsly.json_dumps(self.meta))),
)
)
for name, proc in self.pipeline:
if not hasattr(proc, "name"):
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continue
if name in disable:
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continue
if not hasattr(proc, "to_disk"):
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continue
serializers[name] = lambda p, proc=proc: proc.to_disk(p, vocab=False)
serializers["vocab"] = lambda p: self.vocab.to_disk(p)
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util.to_disk(path, serializers, {p: False for p in disable})
def from_disk(self, path, disable=tuple()):
"""Loads state from a directory. Modifies the object in place and
returns it. If the saved `Language` object contains a model, the
model will be loaded.
path (unicode or Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
disable (list): Names of the pipeline components to disable.
RETURNS (Language): The modified `Language` object.
EXAMPLE:
>>> from spacy.language import Language
>>> nlp = Language().from_disk('/path/to/models')
"""
path = util.ensure_path(path)
deserializers = OrderedDict(
(
("meta.json", lambda p: self.meta.update(srsly.read_json(p))),
(
"vocab",
lambda p: (
self.vocab.from_disk(p) and _fix_pretrained_vectors_name(self)
),
),
("tokenizer", lambda p: self.tokenizer.from_disk(p, vocab=False)),
)
)
for name, proc in self.pipeline:
if name in disable:
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continue
if not hasattr(proc, "from_disk"):
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continue
deserializers[name] = lambda p, proc=proc: proc.from_disk(p, vocab=False)
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exclude = {p: False for p in disable}
if not (path / "vocab").exists():
exclude["vocab"] = True
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util.from_disk(path, deserializers, exclude)
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self._path = path
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return self
def to_bytes(self, disable=[], **exclude):
"""Serialize the current state to a binary string.
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disable (list): Nameds of pipeline components to disable and prevent
from being serialized.
RETURNS (bytes): The serialized form of the `Language` object.
"""
serializers = OrderedDict(
(
("vocab", lambda: self.vocab.to_bytes()),
("tokenizer", lambda: self.tokenizer.to_bytes(vocab=False)),
("meta", lambda: srsly.json_dumps(self.meta)),
)
)
for i, (name, proc) in enumerate(self.pipeline):
if name in disable:
continue
if not hasattr(proc, "to_bytes"):
continue
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serializers[i] = lambda proc=proc: proc.to_bytes(vocab=False)
return util.to_bytes(serializers, exclude)
def from_bytes(self, bytes_data, disable=[]):
"""Load state from a binary string.
bytes_data (bytes): The data to load from.
disable (list): Names of the pipeline components to disable.
RETURNS (Language): The `Language` object.
"""
deserializers = OrderedDict(
(
("meta", lambda b: self.meta.update(srsly.json_loads(b))),
(
"vocab",
lambda b: (
self.vocab.from_bytes(b) and _fix_pretrained_vectors_name(self)
),
),
("tokenizer", lambda b: self.tokenizer.from_bytes(b, vocab=False)),
)
)
for i, (name, proc) in enumerate(self.pipeline):
if name in disable:
continue
if not hasattr(proc, "from_bytes"):
continue
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deserializers[i] = lambda b, proc=proc: proc.from_bytes(b, vocab=False)
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util.from_bytes(bytes_data, deserializers, {})
return self
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def _fix_pretrained_vectors_name(nlp):
# TODO: Replace this once we handle vectors consistently as static
# data
if "vectors" in nlp.meta and nlp.meta["vectors"].get("name"):
nlp.vocab.vectors.name = nlp.meta["vectors"]["name"]
elif not nlp.vocab.vectors.size:
nlp.vocab.vectors.name = None
elif "name" in nlp.meta and "lang" in nlp.meta:
vectors_name = "%s_%s.vectors" % (nlp.meta["lang"], nlp.meta["name"])
nlp.vocab.vectors.name = vectors_name
else:
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raise ValueError(Errors.E092)
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if nlp.vocab.vectors.size != 0:
link_vectors_to_models(nlp.vocab)
for name, proc in nlp.pipeline:
if not hasattr(proc, "cfg"):
continue
proc.cfg.setdefault("deprecation_fixes", {})
proc.cfg["deprecation_fixes"]["vectors_name"] = nlp.vocab.vectors.name
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class DisabledPipes(list):
"""Manager for temporary pipeline disabling."""
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def __init__(self, nlp, *names):
self.nlp = nlp
self.names = names
# Important! Not deep copy -- we just want the container (but we also
# want to support people providing arbitrarily typed nlp.pipeline
# objects.)
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self.original_pipeline = copy(nlp.pipeline)
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list.__init__(self)
self.extend(nlp.remove_pipe(name) for name in names)
def __enter__(self):
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return self
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def __exit__(self, *args):
self.restore()
def restore(self):
"""Restore the pipeline to its state when DisabledPipes was created."""
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current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline
unexpected = [name for name, pipe in current if not self.nlp.has_pipe(name)]
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if unexpected:
# Don't change the pipeline if we're raising an error.
self.nlp.pipeline = current
raise ValueError(Errors.E008.format(names=unexpected))
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self[:] = []
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def _pipe(func, docs):
for doc in docs:
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doc = func(doc)
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yield doc