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Update TextCatBOW to use the fixed SparseLinear layer (#13149)

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* Update `TextCatBOW` to use the fixed `SparseLinear` layer

A while ago, we fixed the `SparseLinear` layer to use all available
parameters: https://github.com/explosion/thinc/pull/754

This change updates `TextCatBOW` to `v3` which uses the new
`SparseLinear_v2` layer. This results in a sizeable improvement on a
text categorization task that was tested.

While at it, this `spacy.TextCatBOW.v3` also adds the `length_exponent`
option to make it possible to change the hidden size. Ideally, we'd just
have an option called `length`. But the way that `TextCatBOW` uses
hashes results in a non-uniform distribution of parameters when the
length is not a power of two.

* Replace TexCatBOW `length_exponent` parameter by `length`

We now round up the length to the next power of two if it isn't
a power of two.

* Remove some tests for TextCatBOW.v2

* Fix missing import
This commit is contained in:
Daniël de Kok 2023-11-29 09:11:54 +01:00 committed by GitHub
parent bf7c2ea99a
commit da7ad97519
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13 changed files with 144 additions and 50 deletions
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17
spacy/cli/templates/quickstart_training.jinja
View File

@ -271,8 +271,9 @@ grad_factor = 1.0
@layers = "reduce_mean.v1"
[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
@ -308,8 +309,9 @@ grad_factor = 1.0
@layers = "reduce_mean.v1"
[components.textcat_multilabel.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = false
length = 262144
ngram_size = 1
no_output_layer = false
@ -542,14 +544,15 @@ nO = null
width = ${components.tok2vec.model.encode.width}
[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
@ -570,15 +573,17 @@ nO = null
width = ${components.tok2vec.model.encode.width}
[components.textcat_multilabel.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = false
length = 262144
ngram_size = 1
no_output_layer = false
{% else -%}
[components.textcat_multilabel.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = false
length = 262144
ngram_size = 1
no_output_layer = false
{%- endif %}

1
spacy/errors.py
View File

@ -983,6 +983,7 @@ class Errors(metaclass=ErrorsWithCodes):
"predicted docs when training {component}.")
E1055 = ("The 'replace_listener' callback expects {num_params} parameters, "
"but only callbacks with one or three parameters are supported")
E1056 = ("The `TextCatBOW` architecture expects a length of at least 1, was {length}.")
# Deprecated model shortcuts, only used in errors and warnings

46
spacy/ml/models/textcat.py
View File

@ -1,5 +1,5 @@
from functools import partial
from typing import List, Optional, cast
from typing import List, Optional, Tuple, cast
from thinc.api import (
Dropout,
@ -12,6 +12,7 @@ from thinc.api import (
Relu,
Softmax,
SparseLinear,
SparseLinear_v2,
chain,
clone,
concatenate,
@ -25,9 +26,10 @@ from thinc.api import (
)
from thinc.layers.chain import init as init_chain
from thinc.layers.resizable import resize_linear_weighted, resize_model
from thinc.types import Floats2d
from thinc.types import ArrayXd, Floats2d
from ...attrs import ORTH
from ...errors import Errors
from ...tokens import Doc
from ...util import registry
from ..extract_ngrams import extract_ngrams
@ -95,10 +97,48 @@ def build_bow_text_classifier(
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}
with Model.define_operators({">>": chain}):
sparse_linear = SparseLinear(nO=nO)
output_layer = None
if not no_output_layer:
fill_defaults["b"] = NEG_VALUE

6
spacy/pipeline/textcat.py
View File

@ -36,8 +36,9 @@ maxout_pieces = 3
depth = 2
[model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
"""
@ -45,8 +46,9 @@ DEFAULT_SINGLE_TEXTCAT_MODEL = Config().from_str(single_label_default_config)["m
single_label_bow_config = """
[model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
"""

5
spacy/pipeline/textcat_multilabel.py
View File

@ -35,8 +35,9 @@ maxout_pieces = 3
depth = 2
[model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = false
length = 262144
ngram_size = 1
no_output_layer = false
"""
@ -44,7 +45,7 @@ DEFAULT_MULTI_TEXTCAT_MODEL = Config().from_str(multi_label_default_config)["mod
multi_label_bow_config = """
[model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = false
ngram_size = 1
no_output_layer = false

2
spacy/tests/pipeline/test_pipe_factories.py
View File

@ -203,7 +203,7 @@ def test_pipe_class_component_model():
"@architectures": "spacy.TextCatEnsemble.v2",
"tok2vec": DEFAULT_TOK2VEC_MODEL,
"linear_model": {
"@architectures": "spacy.TextCatBOW.v2",
"@architectures": "spacy.TextCatBOW.v3",
"exclusive_classes": False,
"ngram_size": 1,
"no_output_layer": False,

31
spacy/tests/pipeline/test_textcat.py
View File

@ -414,7 +414,7 @@ def test_implicit_label(name, get_examples):
@pytest.mark.parametrize(
"name,textcat_config",
[
# BOW
# BOW V1
("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
("textcat", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v1", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
@ -451,11 +451,11 @@ def test_no_resize(name, textcat_config):
@pytest.mark.parametrize(
"name,textcat_config",
[
# BOW
("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
# BOW V3
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
# CNN
("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
@ -480,11 +480,11 @@ def test_resize(name, textcat_config):
@pytest.mark.parametrize(
"name,textcat_config",
[
# BOW
("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
("textcat", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
# BOW v3
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": False, "ngram_size": 3}),
("textcat", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "no_output_layer": True, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": False, "ngram_size": 3}),
("textcat_multilabel", {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "no_output_layer": True, "ngram_size": 3}),
# CNN
("textcat", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
("textcat_multilabel", {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),
@ -693,9 +693,14 @@ def test_overfitting_IO_multi():
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
# BOW V3
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}),
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 4, "no_output_layer": False}),
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 3, "no_output_layer": True}),
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 2, "no_output_layer": True}),
# ENSEMBLE V2
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v2", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": False, "ngram_size": 1, "no_output_layer": False}}),
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatEnsemble.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "linear_model": {"@architectures": "spacy.TextCatBOW.v3", "exclusive_classes": True, "ngram_size": 5, "no_output_layer": False}}),
# CNN V2
("textcat", TRAIN_DATA_SINGLE_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": True}),
("textcat_multilabel", TRAIN_DATA_MULTI_LABEL, {"@architectures": "spacy.TextCatCNN.v2", "tok2vec": DEFAULT_TOK2VEC_MODEL, "exclusive_classes": False}),

4
spacy/tests/test_cli_app.py
View File

@ -238,7 +238,7 @@ def test_project_push_pull(project_dir):
def test_find_function_valid():
# example of architecture in main code base
function = "spacy.TextCatBOW.v2"
function = "spacy.TextCatBOW.v3"
result = CliRunner().invoke(app, ["find-function", function, "-r", "architectures"])
assert f"Found registered function '{function}'" in result.stdout
assert "textcat.py" in result.stdout
@ -257,7 +257,7 @@ def test_find_function_valid():
def test_find_function_invalid():
# invalid registry
function = "spacy.TextCatBOW.v2"
function = "spacy.TextCatBOW.v3"
registry = "foobar"
result = CliRunner().invoke(
app, ["find-function", function, "--registry", registry]

3
spacy/tests/test_misc.py
View File

@ -376,8 +376,9 @@ def test_util_dot_section():
factory = "textcat"
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
"""

39
website/docs/api/architectures.mdx
View File

@ -78,16 +78,16 @@ subword features, and a
[MaxoutWindowEncoder](/api/architectures#MaxoutWindowEncoder) encoding layer
consisting of a CNN and a layer-normalized maxout activation function.
| Name | Description |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~ |
| `depth` | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~ |
| `embed_size` | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~ |
| Name | Description |
| -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `width` | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~ |
| `depth` | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~ |
| `embed_size` | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~ |
| `window_size` | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * window_size * 2 + 1`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
| `maxout_pieces` | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~ |
| `subword_features` | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~ |
| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
| `maxout_pieces` | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~ |
| `subword_features` | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~ |
| `pretrained_vectors` | Whether to also use static vectors. ~~bool~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy.Tok2VecListener.v1 {id="Tok2VecListener"}
@ -962,8 +962,9 @@ single-label use-cases where `exclusive_classes = true`, while the
> nO = null
>
> [model.linear_model]
> @architectures = "spacy.TextCatBOW.v2"
> @architectures = "spacy.TextCatBOW.v3"
> exclusive_classes = true
> length = 262144
> ngram_size = 1
> no_output_layer = false
>
@ -1057,14 +1058,15 @@ after training.
</Accordion>
### spacy.TextCatBOW.v2 {id="TextCatBOW"}
### spacy.TextCatBOW.v3 {id="TextCatBOW"}
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy.TextCatBOW.v2"
> @architectures = "spacy.TextCatBOW.v3"
> exclusive_classes = false
> length = 262144
> ngram_size = 1
> no_output_layer = false
> nO = null
@ -1078,14 +1080,19 @@ the others, but may not be as accurate, especially if texts are short.
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~ |
| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~ |
| `length` | The size of the weights vector. The length will be rounded up to the next power of two if it is not a power of two. Defaults to `262144`. ~~int~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
<Accordion title="spacy.TextCatBOW.v1 definition" spaced>
<Accordion title="Previous versions of spacy.TextCatBOW" spaced>
[TextCatBOW.v1](/api/legacy#TextCatBOW_v1) had the exact same signature, but was
not yet resizable. Since v2, new labels can be added to this component, even
after training.
- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) was not yet resizable. Since v2,
new labels can be added to this component, even after training.
- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) and
[TextCatBOW.v2](/api/legacy#TextCatBOW_v2) used an erroneous sparse linear
layer that only used a small number of the allocated parameters.
- [TextCatBOW.v1](/api/legacy#TextCatBOW_v1) and
[TextCatBOW.v2](/api/legacy#TextCatBOW_v2) did not have the `length` argument.
</Accordion>

31
website/docs/api/legacy.mdx
View File

@ -198,7 +198,9 @@ architecture is usually less accurate than the ensemble, but runs faster.
Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
that you can add labels to a previously trained textcat. `TextCatBOW` v1 did not
yet support that.
yet support that. Versions of this model before `spacy.TextCatBOW.v3` used an
erroneous sparse linear layer that only used a small number of the allocated
parameters.
> #### Example Config
>
@ -222,6 +224,33 @@ the others, but may not be as accurate, especially if texts are short.
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TextCatBOW.v2 {id="TextCatBOW"}
Versions of this model before `spacy.TextCatBOW.v3` used an erroneous sparse
linear layer that only used a small number of the allocated parameters.
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy.TextCatBOW.v2"
> exclusive_classes = false
> ngram_size = 1
> no_output_layer = false
> nO = null
> ```
An n-gram "bag-of-words" model. This architecture should run much faster than
the others, but may not be as accurate, especially if texts are short.
| Name | Description |
| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `ngram_size` | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3` would give unigram, trigram and bigram features. ~~int~~ |
| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.TransitionBasedParser.v1 {id="TransitionBasedParser_v1"}
Identical to

6
website/docs/usage/layers-architectures.mdx
View File

@ -153,8 +153,9 @@ maxout_pieces = 3
depth = 2
[components.textcat.model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
```
@ -170,8 +171,9 @@ factory = "textcat"
labels = []
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false
nO = null

3
website/docs/usage/processing-pipelines.mdx
View File

@ -1328,8 +1328,9 @@ labels = []
# This function is created and then passed to the "textcat" component as
# the argument "model"
[components.textcat.model]
@architectures = "spacy.TextCatBOW.v2"
@architectures = "spacy.TextCatBOW.v3"
exclusive_classes = true
length = 262144
ngram_size = 1
no_output_layer = false