spaCy/spacy/ml/models/textcat.py

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from functools import partial
from typing import List, Optional, Tuple, cast
from thinc.api import (
Dropout,
LayerNorm,
Linear,
Logistic,
Maxout,
Model,
ParametricAttention,
Relu,
Softmax,
SparseLinear,
SparseLinear_v2,
chain,
clone,
concatenate,
list2ragged,
reduce_first,
reduce_last,
reduce_max,
reduce_mean,
reduce_sum,
residual,
resizable,
softmax_activation,
with_cpu,
)
from thinc.layers.chain import init as init_chain
from thinc.layers.resizable import resize_linear_weighted, resize_model
from thinc.types import ArrayXd, Floats2d
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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from ...attrs import ORTH
from ...errors import Errors
from ...tokens import Doc
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from ...util import registry
from ..extract_ngrams import extract_ngrams
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from ..staticvectors import StaticVectors
from .tok2vec import get_tok2vec_width
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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NEG_VALUE = -5000
@registry.architectures("spacy.TextCatCNN.v2")
Refactor pipeline components, config and language data (#5759) * Update with WIP * Update with WIP * Update with pipeline serialization * Update types and pipe factories * Add deep merge, tidy up and add tests * Fix pipe creation from config * Don't validate default configs on load * Update spacy/language.py Co-authored-by: Ines Montani <ines@ines.io> * Adjust factory/component meta error * Clean up factory args and remove defaults * Add test for failing empty dict defaults * Update pipeline handling and methods * provide KB as registry function instead of as object * small change in test to make functionality more clear * update example script for EL configuration * Fix typo * Simplify test * Simplify test * splitting pipes.pyx into separate files * moving default configs to each component file * fix batch_size type * removing default values from component constructors where possible (TODO: test 4725) * skip instead of xfail * Add test for config -> nlp with multiple instances * pipeline.pipes -> pipeline.pipe * Tidy up, document, remove kwargs * small cleanup/generalization for Tok2VecListener * use DEFAULT_UPSTREAM field * revert to avoid circular imports * Fix tests * Replace deprecated arg * Make model dirs require config * fix pickling of keyword-only arguments in constructor * WIP: clean up and integrate full config * Add helper to handle function args more reliably Now also includes keyword-only args * Fix config composition and serialization * Improve config debugging and add visual diff * Remove unused defaults and fix type * Remove pipeline and factories from meta * Update spacy/default_config.cfg Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update spacy/default_config.cfg * small UX edits * avoid printing stack trace for debug CLI commands * Add support for language-specific factories * specify the section of the config which holds the model to debug * WIP: add Language.from_config * Update with language data refactor WIP * Auto-format * Add backwards-compat handling for Language.factories * Update morphologizer.pyx * Fix morphologizer * Update and simplify lemmatizers * Fix Japanese tests * Port over tagger changes * Fix Chinese and tests * Update to latest Thinc * WIP: xfail first Russian lemmatizer test * Fix component-specific overrides * fix nO for output layers in debug_model * Fix default value * Fix tests and don't pass objects in config * Fix deep merging * Fix lemma lookup data registry Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed) * Add types * Add Vocab.from_config * Fix typo * Fix tests * Make config copying more elegant * Fix pipe analysis * Fix lemmatizers and is_base_form * WIP: move language defaults to config * Fix morphology type * Fix vocab * Remove comment * Update to latest Thinc * Add morph rules to config * Tidy up * Remove set_morphology option from tagger factory * Hack use_gpu * Move [pipeline] to top-level block and make [nlp.pipeline] list Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them * Fix use_gpu and resume in CLI * Auto-format * Remove resume from config * Fix formatting and error * [pipeline] -> [components] * Fix types * Fix tagger test: requires set_morphology? Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
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def build_simple_cnn_text_classifier(
tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
return build_reduce_text_classifier(
tok2vec=tok2vec,
exclusive_classes=exclusive_classes,
use_reduce_first=False,
use_reduce_last=False,
use_reduce_max=False,
use_reduce_mean=True,
nO=nO,
)
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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def resize_and_set_ref(model, new_nO, resizable_layer):
resizable_layer = resize_model(resizable_layer, new_nO)
model.set_ref("output_layer", resizable_layer.layers[0])
model.set_dim("nO", new_nO, force=True)
return model
@registry.architectures("spacy.TextCatBOW.v2")
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def build_bow_text_classifier(
exclusive_classes: bool,
ngram_size: int,
no_output_layer: bool,
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
return _build_bow_text_classifier(
exclusive_classes=exclusive_classes,
ngram_size=ngram_size,
no_output_layer=no_output_layer,
nO=nO,
sparse_linear=SparseLinear(nO=nO),
)
@registry.architectures("spacy.TextCatBOW.v3")
def build_bow_text_classifier_v3(
exclusive_classes: bool,
ngram_size: int,
no_output_layer: bool,
length: int = 262144,
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
if length < 1:
raise ValueError(Errors.E1056.format(length=length))
# Find k such that 2**(k-1) < length <= 2**k.
length = 2 ** (length - 1).bit_length()
return _build_bow_text_classifier(
exclusive_classes=exclusive_classes,
ngram_size=ngram_size,
no_output_layer=no_output_layer,
nO=nO,
sparse_linear=SparseLinear_v2(nO=nO, length=length),
)
def _build_bow_text_classifier(
exclusive_classes: bool,
ngram_size: int,
no_output_layer: bool,
sparse_linear: Model[Tuple[ArrayXd, ArrayXd, ArrayXd], ArrayXd],
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
fill_defaults = {"b": 0, "W": 0}
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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with Model.define_operators({">>": chain}):
output_layer = None
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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if not no_output_layer:
fill_defaults["b"] = NEG_VALUE
output_layer = softmax_activation() if exclusive_classes else Logistic()
resizable_layer: Model[Floats2d, Floats2d] = resizable(
sparse_linear,
resize_layer=partial(resize_linear_weighted, fill_defaults=fill_defaults),
)
model = extract_ngrams(ngram_size, attr=ORTH) >> resizable_layer
model = with_cpu(model, model.ops)
if output_layer:
model = model >> with_cpu(output_layer, output_layer.ops)
if nO is not None:
model.set_dim("nO", cast(int, nO))
model.set_ref("output_layer", sparse_linear)
model.attrs["multi_label"] = not exclusive_classes
model.attrs["resize_output"] = partial(
resize_and_set_ref, resizable_layer=resizable_layer
)
return model
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@registry.architectures("spacy.TextCatEnsemble.v2")
def build_text_classifier_v2(
tok2vec: Model[List[Doc], List[Floats2d]],
linear_model: Model[List[Doc], Floats2d],
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
exclusive_classes = not linear_model.attrs["multi_label"]
with Model.define_operators({">>": chain, "|": concatenate}):
width = tok2vec.maybe_get_dim("nO")
attention_layer = ParametricAttention(width)
maxout_layer = Maxout(nO=width, nI=width)
norm_layer = LayerNorm(nI=width)
cnn_model = (
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tok2vec
>> list2ragged()
>> attention_layer
>> reduce_sum()
>> residual(maxout_layer >> norm_layer >> Dropout(0.0))
)
nO_double = nO * 2 if nO else None
if exclusive_classes:
output_layer = Softmax(nO=nO, nI=nO_double)
else:
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output_layer = Linear(nO=nO, nI=nO_double) >> Logistic()
model = (linear_model | cnn_model) >> output_layer
model.set_ref("tok2vec", tok2vec)
if model.has_dim("nO") is not False and nO is not None:
model.set_dim("nO", cast(int, nO))
model.set_ref("output_layer", linear_model.get_ref("output_layer"))
model.set_ref("attention_layer", attention_layer)
model.set_ref("maxout_layer", maxout_layer)
model.set_ref("norm_layer", norm_layer)
model.attrs["multi_label"] = not exclusive_classes
🏷 Add Mypy check to CI and ignore all existing Mypy errors (#9167) * 🚨 Ignore all existing Mypy errors * 🏗 Add Mypy check to CI * Add types-mock and types-requests as dev requirements * Add additional type ignore directives * Add types packages to dev-only list in reqs test * Add types-dataclasses for python 3.6 * Add ignore to pretrain * 🏷 Improve type annotation on `run_command` helper The `run_command` helper previously declared that it returned an `Optional[subprocess.CompletedProcess]`, but it isn't actually possible for the function to return `None`. These changes modify the type annotation of the `run_command` helper and remove all now-unnecessary `# type: ignore` directives. * 🔧 Allow variable type redefinition in limited contexts These changes modify how Mypy is configured to allow variables to have their type automatically redefined under certain conditions. The Mypy documentation contains the following example: ```python def process(items: List[str]) -> None: # 'items' has type List[str] items = [item.split() for item in items] # 'items' now has type List[List[str]] ... ``` This configuration change is especially helpful in reducing the number of `# type: ignore` directives needed to handle the common pattern of: * Accepting a filepath as a string * Overwriting the variable using `filepath = ensure_path(filepath)` These changes enable redefinition and remove all `# type: ignore` directives rendered redundant by this change. * 🏷 Add type annotation to converters mapping * 🚨 Fix Mypy error in convert CLI argument verification * 🏷 Improve type annotation on `resolve_dot_names` helper * 🏷 Add type annotations for `Vocab` attributes `strings` and `vectors` * 🏷 Add type annotations for more `Vocab` attributes * 🏷 Add loose type annotation for gold data compilation * 🏷 Improve `_format_labels` type annotation * 🏷 Fix `get_lang_class` type annotation * 🏷 Loosen return type of `Language.evaluate` * 🏷 Don't accept `Scorer` in `handle_scores_per_type` * 🏷 Add `string_to_list` overloads * 🏷 Fix non-Optional command-line options * 🙈 Ignore redefinition of `wandb_logger` in `loggers.py` * ➕ Install `typing_extensions` in Python 3.8+ The `typing_extensions` package states that it should be used when "writing code that must be compatible with multiple Python versions". Since SpaCy needs to support multiple Python versions, it should be used when newer `typing` module members are required. One example of this is `Literal`, which is available starting with Python 3.8. Previously SpaCy tried to import `Literal` from `typing`, falling back to `typing_extensions` if the import failed. However, Mypy doesn't seem to be able to understand what `Literal` means when the initial import means. Therefore, these changes modify how `compat` imports `Literal` by always importing it from `typing_extensions`. These changes also modify how `typing_extensions` is installed, so that it is a requirement for all Python versions, including those greater than or equal to 3.8. * 🏷 Improve type annotation for `Language.pipe` These changes add a missing overload variant to the type signature of `Language.pipe`. Additionally, the type signature is enhanced to allow type checkers to differentiate between the two overload variants based on the `as_tuple` parameter. Fixes #8772 * ➖ Don't install `typing-extensions` in Python 3.8+ After more detailed analysis of how to implement Python version-specific type annotations using SpaCy, it has been determined that by branching on a comparison against `sys.version_info` can be statically analyzed by Mypy well enough to enable us to conditionally use `typing_extensions.Literal`. This means that we no longer need to install `typing_extensions` for Python versions greater than or equal to 3.8! 🎉 These changes revert previous changes installing `typing-extensions` regardless of Python version and modify how we import the `Literal` type to ensure that Mypy treats it properly. * resolve mypy errors for Strict pydantic types * refactor code to avoid missing return statement * fix types of convert CLI command * avoid list-set confustion in debug_data * fix typo and formatting * small fixes to avoid type ignores * fix types in profile CLI command and make it more efficient * type fixes in projects CLI * put one ignore back * type fixes for render * fix render types - the sequel * fix BaseDefault in language definitions * fix type of noun_chunks iterator - yields tuple instead of span * fix types in language-specific modules * 🏷 Expand accepted inputs of `get_string_id` `get_string_id` accepts either a string (in which case it returns its ID) or an ID (in which case it immediately returns the ID). These changes extend the type annotation of `get_string_id` to indicate that it can accept either strings or IDs. * 🏷 Handle override types in `combine_score_weights` The `combine_score_weights` function allows users to pass an `overrides` mapping to override data extracted from the `weights` argument. Since it allows `Optional` dictionary values, the return value may also include `Optional` dictionary values. These changes update the type annotations for `combine_score_weights` to reflect this fact. * 🏷 Fix tokenizer serialization method signatures in `DummyTokenizer` * 🏷 Fix redefinition of `wandb_logger` These changes fix the redefinition of `wandb_logger` by giving a separate name to each `WandbLogger` version. For backwards-compatibility, `spacy.train` still exports `wandb_logger_v3` as `wandb_logger` for now. * more fixes for typing in language * type fixes in model definitions * 🏷 Annotate `_RandomWords.probs` as `NDArray` * 🏷 Annotate `tok2vec` layers to help Mypy * 🐛 Fix `_RandomWords.probs` type annotations for Python 3.6 Also remove an import that I forgot to move to the top of the module 😅 * more fixes for matchers and other pipeline components * quick fix for entity linker * fixing types for spancat, textcat, etc * bugfix for tok2vec * type annotations for scorer * add runtime_checkable for Protocol * type and import fixes in tests * mypy fixes for training utilities * few fixes in util * fix import * 🐵 Remove unused `# type: ignore` directives * 🏷 Annotate `Language._components` * 🏷 Annotate `spacy.pipeline.Pipe` * add doc as property to span.pyi * small fixes and cleanup * explicit type annotations instead of via comment Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: svlandeg <svlandeg@github.com>
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model.init = init_ensemble_textcat # type: ignore[assignment]
return model
def init_ensemble_textcat(model, X, Y) -> Model:
tok2vec_width = get_tok2vec_width(model)
model.get_ref("attention_layer").set_dim("nO", tok2vec_width)
model.get_ref("maxout_layer").set_dim("nO", tok2vec_width)
model.get_ref("maxout_layer").set_dim("nI", tok2vec_width)
model.get_ref("norm_layer").set_dim("nI", tok2vec_width)
model.get_ref("norm_layer").set_dim("nO", tok2vec_width)
init_chain(model, X, Y)
return model
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@registry.architectures("spacy.TextCatLowData.v1")
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def build_text_classifier_lowdata(
width: int, dropout: Optional[float], nO: Optional[int] = None
) -> Model[List[Doc], Floats2d]:
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# Don't document this yet, I'm not sure it's right.
# Note, before v.3, this was the default if setting "low_data" and "pretrained_dims"
with Model.define_operators({">>": chain, "**": clone}):
model = (
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StaticVectors(width)
>> list2ragged()
>> ParametricAttention(width)
>> reduce_sum()
>> residual(Relu(width, width)) ** 2
>> Linear(nO, width)
)
if dropout:
model = model >> Dropout(dropout)
model = model >> Logistic()
Default settings to configurations (#4995) * fix grad_clip naming * cleaning up pretrained_vectors out of cfg * further refactoring Model init's * move Model building out of pipes * further refactor to require a model config when creating a pipe * small fixes * making cfg in nn_parser more consistent * fixing nr_class for parser * fixing nn_parser's nO * fix printing of loss * architectures in own file per type, consistent naming * convenience methods default_tagger_config and default_tok2vec_config * let create_pipe access default config if available for that component * default_parser_config * move defaults to separate folder * allow reading nlp from package or dir with argument 'name' * architecture spacy.VocabVectors.v1 to read static vectors from file * cleanup * default configs for nel, textcat, morphologizer, tensorizer * fix imports * fixing unit tests * fixes and clean up * fixing defaults, nO, fix unit tests * restore parser IO * fix IO * 'fix' serialization test * add *.cfg to manifest * fix example configs with additional arguments * replace Morpohologizer with Tagger * add IO bit when testing overfitting of tagger (currently failing) * fix IO - don't initialize when reading from disk * expand overfitting tests to also check IO goes OK * remove dropout from HashEmbed to fix Tagger performance * add defaults for sentrec * update thinc * always pass a Model instance to a Pipe * fix piped_added statement * remove obsolete W029 * remove obsolete errors * restore byte checking tests (work again) * clean up test * further test cleanup * convert from config to Model in create_pipe * bring back error when component is not initialized * cleanup * remove calls for nlp2.begin_training * use thinc.api in imports * allow setting charembed's nM and nC * fix for hardcoded nM/nC + unit test * formatting fixes * trigger build
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return model
@registry.architectures("spacy.TextCatReduce.v1")
def build_reduce_text_classifier(
tok2vec: Model,
exclusive_classes: bool,
use_reduce_first: bool,
use_reduce_last: bool,
use_reduce_max: bool,
use_reduce_mean: bool,
nO: Optional[int] = None,
) -> Model[List[Doc], Floats2d]:
"""Build a model that classifies pooled `Doc` representations.
Pooling is performed using reductions. Reductions are concatenated when
multiple reductions are used.
tok2vec (Model): the tok2vec layer to pool over.
exclusive_classes (bool): Whether or not classes are mutually exclusive.
use_reduce_first (bool): Pool by using the hidden representation of the
first token of a `Doc`.
use_reduce_last (bool): Pool by using the hidden representation of the
last token of a `Doc`.
use_reduce_max (bool): Pool by taking the maximum values of the hidden
representations of a `Doc`.
use_reduce_mean (bool): Pool by taking the mean of all hidden
representations of a `Doc`.
nO (Optional[int]): Number of classes.
"""
fill_defaults = {"b": 0, "W": 0}
reductions = []
if use_reduce_first:
reductions.append(reduce_first())
if use_reduce_last:
reductions.append(reduce_last())
if use_reduce_max:
reductions.append(reduce_max())
if use_reduce_mean:
reductions.append(reduce_mean())
if not len(reductions):
raise ValueError(Errors.E1057)
with Model.define_operators({">>": chain}):
cnn = tok2vec >> list2ragged() >> concatenate(*reductions)
nO_tok2vec = tok2vec.maybe_get_dim("nO")
nI = nO_tok2vec * len(reductions) if nO_tok2vec is not None else None
if exclusive_classes:
output_layer = Softmax(nO=nO, nI=nI)
fill_defaults["b"] = NEG_VALUE
resizable_layer: Model = resizable(
output_layer,
resize_layer=partial(
resize_linear_weighted, fill_defaults=fill_defaults
),
)
model = cnn >> resizable_layer
else:
output_layer = Linear(nO=nO, nI=nI)
resizable_layer = resizable(
output_layer,
resize_layer=partial(
resize_linear_weighted, fill_defaults=fill_defaults
),
)
model = cnn >> resizable_layer >> Logistic()
model.set_ref("output_layer", output_layer)
model.attrs["resize_output"] = partial(
resize_and_set_ref,
resizable_layer=resizable_layer,
)
model.set_ref("tok2vec", tok2vec)
if nO is not None:
model.set_dim("nO", cast(int, nO))
model.attrs["multi_label"] = not exclusive_classes
return model