explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-07-18 20:22:25 +03:00
Code Issues Packages Projects Releases Wiki Activity

merge

Browse Source
This commit is contained in:
kadarakos 2022-05-17 06:56:34 +00:00
commit 403fb95d56
6 changed files with 450 additions and 38 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

58
spacy/ml/models/coref.py
View File

@ -14,17 +14,15 @@ from .coref_util import add_dummy
@registry.architectures("spacy.Coref.v1")
def build_wl_coref_model(
tok2vec: Model[List[Doc], List[Floats2d]],
embedding_size: int = 20,
distance_embedding_size: int = 20,
hidden_size: int = 1024,
depth: int = 1,
dropout: float = 0.3,
# pairs to keep per mention after rough scoring
rough_candidates: int = 50,
# TODO is this not a training loop setting?
a_scoring_batch_size: int = 512,
# span predictor embeddings
sp_embedding_size: int = 64,
antecedent_limit: int = 50,
antecedent_batch_size: int = 512,
):
# TODO add model return types
# TODO fix this
try:
dim = tok2vec.get_dim("nO")
@ -36,12 +34,12 @@ def build_wl_coref_model(
coref_scorer = PyTorchWrapper(
CorefScorer(
dim,
embedding_size,
distance_embedding_size,
hidden_size,
depth,
dropout,
rough_candidates,
a_scoring_batch_size,
antecedent_limit,
antecedent_batch_size,
),
convert_inputs=convert_coref_scorer_inputs,
convert_outputs=convert_coref_scorer_outputs,
@ -100,7 +98,7 @@ class CorefScorer(torch.nn.Module):
dist_emb_size: int,
hidden_size: int,
n_layers: int,
dropout_rate: float,
dropout: float,
roughk: int,
batch_size: int,
):
@ -110,31 +108,31 @@ class CorefScorer(torch.nn.Module):
dist_emb_size: Size of the distance embeddings.
hidden_size: Size of the coreference candidate embeddings.
n_layers: Numbers of layers in the AnaphoricityScorer.
dropout_rate: Dropout probability to apply across all modules.
dropout: Dropout probability to apply across all modules.
roughk: Number of candidates the RoughScorer returns.
batch_size: Internal batch-size for the more expensive scorer.
"""
self.dropout = torch.nn.Dropout(dropout_rate)
self.dropout = torch.nn.Dropout(dropout)
self.batch_size = batch_size
# Modules
self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate)
self.pw = DistancePairwiseEncoder(dist_emb_size, dropout)
pair_emb = dim * 3 + self.pw.shape
self.a_scorer = AnaphoricityScorer(
pair_emb,
hidden_size,
n_layers,
dropout_rate
dropout
)
self.lstm = torch.nn.LSTM(
input_size=dim,
hidden_size=dim,
batch_first=True,
)
self.rough_scorer = RoughScorer(dim, dropout_rate, roughk)
self.pw = DistancePairwiseEncoder(dist_emb_size, dropout_rate)
self.rough_scorer = RoughScorer(dim, dropout, roughk)
self.pw = DistancePairwiseEncoder(dist_emb_size, dropout)
pair_emb = dim * 3 + self.pw.shape
self.a_scorer = AnaphoricityScorer(
pair_emb, hidden_size, n_layers, dropout_rate
pair_emb, hidden_size, n_layers, dropout
)
def forward(
@ -191,18 +189,18 @@ class CorefScorer(torch.nn.Module):
class AnaphoricityScorer(torch.nn.Module):
"""Calculates anaphoricity scores by passing the inputs into a FFNN"""
def __init__(self, in_features: int, hidden_size, n_hidden_layers, dropout_rate):
def __init__(self, in_features: int, hidden_size, depth, dropout):
super().__init__()
hidden_size = hidden_size
if not n_hidden_layers:
if not depth:
hidden_size = in_features
layers = []
for i in range(n_hidden_layers):
for i in range(depth):
layers.extend(
[
torch.nn.Linear(hidden_size if i else in_features, hidden_size),
torch.nn.LeakyReLU(),
torch.nn.Dropout(dropout_rate),
torch.nn.Dropout(dropout),
]
)
self.hidden = torch.nn.Sequential(*layers)
@ -244,7 +242,7 @@ class AnaphoricityScorer(torch.nn.Module):
def _ffnn(self, x: torch.Tensor) -> torch.Tensor:
"""
x: tensor of shape (batch_size x roughk x n_features
returns: tensor of shape (batch_size x rough_k)
returns: tensor of shape (batch_size x antecedent_limit)
"""
x = self.out(self.hidden(x))
return x.squeeze(2)
@ -290,11 +288,11 @@ class RoughScorer(torch.nn.Module):
steps to reduce computational cost.
"""
def __init__(self, features: int, dropout_rate: float, rough_k: float):
def __init__(self, features: int, dropout: float, antecedent_limit: int):
super().__init__()
self.dropout = torch.nn.Dropout(dropout_rate)
self.dropout = torch.nn.Dropout(dropout)
self.bilinear = torch.nn.Linear(features, features)
self.k = rough_k
self.k = antecedent_limit
def forward(
self, # type: ignore # pylint: disable=arguments-differ #35566 in pytorch
@ -318,7 +316,7 @@ class RoughScorer(torch.nn.Module):
class DistancePairwiseEncoder(torch.nn.Module):
def __init__(self, embedding_size, dropout_rate):
def __init__(self, distance_embedding_size, dropout):
"""
Takes the top_indices indicating, which is a ranked
list for each word and its most likely corresponding
@ -326,15 +324,15 @@ class DistancePairwiseEncoder(torch.nn.Module):
up a distance embedding from a table, where the distance
corresponds to the log-distance.
embedding_size: int,
distance_embedding_size: int,
Dimensionality of the distance-embeddings table.
dropout_rate: float,
dropout: float,
Dropout probability.
"""
super().__init__()
emb_size = embedding_size
emb_size = distance_embedding_size
self.distance_emb = torch.nn.Embedding(9, emb_size)
self.dropout = torch.nn.Dropout(dropout_rate)
self.dropout = torch.nn.Dropout(dropout)
self.shape = emb_size
def forward(

1
spacy/ml/models/span_predictor.py
View File

@ -18,6 +18,7 @@ def build_span_predictor(
dist_emb_size: int = 64,
prefix: str = "coref_head_clusters"
):
# TODO add model return types
# TODO fix this
try:
dim = tok2vec.get_dim("nO")

7
spacy/pipeline/coref.py
View File

@ -31,13 +31,12 @@ from ..coref_scorer import Evaluator, get_cluster_info, lea
default_config = """
[model]
@architectures = "spacy.Coref.v1"
embedding_size = 20
distance_embedding_size = 20
hidden_size = 1024
depth = 1
dropout = 0.3
rough_candidates = 50
a_scoring_batch_size = 512
sp_embedding_size = 64
antecedent_limit = 50
antecedent_batch_size = 512
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

74
website/docs/api/architectures.md
View File

@ -922,3 +922,77 @@ A function that takes as input a [`KnowledgeBase`](/api/kb) and a
plausible [`Candidate`](/api/kb/#candidate) objects. The default
`CandidateGenerator` simply uses the text of a mention to find its potential
aliases in the `KnowledgeBase`. Note that this function is case-dependent.
## Coreference Architectures
A [`CoreferenceResolver`](/api/coref) component identifies tokens that refer to
the same entity. A [`SpanPredictor`](/api/span-predictor) component infers spans
from single tokens. Together these components can be used to reproduce
traditional coreference models. You can also omit the `SpanPredictor` for faster
performance if working with only token-level clusters is acceptable.
### spacy.Coref.v1 {#Coref}
> #### Example Config
>
> ```ini
>
> [model]
> @architectures = "spacy.Coref.v1"
> distance_embedding_size = 20
> dropout = 0.3
> hidden_size = 1024
> depth = 2
> antecedent_limit = 50
> antecedent_batch_size = 512
>
> [model.tok2vec]
> @architectures = "spacy-transformers.TransformerListener.v1"
> grad_factor = 1.0
> upstream = "transformer"
> pooling = {"@layers":"reduce_mean.v1"}
> ```
The `Coref` model architecture is a Thinc `Model`.
| Name | Description |
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `distance_embedding_size` | A representation of the distance between candidates. ~~int~~ |
| `dropout` | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ |
| `hidden_size` | Size of the main internal layers. ~~int~~ |
| `depth` | Depth of the internal network. ~~int~~ |
| `antecedent_limit` | How many candidate antecedents to keep after rough scoring. This has a significant effect on memory usage. Typical values would be 50 to 200, or higher for very long documents. ~~int~~ |
| `antecedent_batch_size` | Internal batch size. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.SpanPredictor.v1 {#SpanPredictor}
> #### Example Config
>
> ```ini
>
> [model]
> @architectures = "spacy.SpanPredictor.v1"
> hidden_size = 1024
> dist_emb_size = 64
>
> [model.tok2vec]
> @architectures = "spacy-transformers.TransformerListener.v1"
> grad_factor = 1.0
> upstream = "transformer"
> pooling = {"@layers":"reduce_mean.v1"}
> ```
The `SpanPredictor` model architecture is a Thinc `Model`.
| Name | Description |
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~ |
| `dropout` | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ |
| `hidden_size` | Size of the main internal layers. ~~int~~ |
| `depth` | Depth of the internal network. ~~int~~ |
| `antecedent_limit` | How many candidate antecedents to keep after rough scoring. This has a significant effect on memory usage. Typical values would be 50 to 200, or higher for very long documents. ~~int~~ |
| `antecedent_batch_size` | Internal batch size. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], TupleFloats2d]~~ |

8
website/docs/api/coref.md
View File

@ -92,9 +92,9 @@ shortcut for this and instantiate the component using its string name and
Apply the pipe to one document. The document is modified in place and returned.
This usually happens under the hood when the `nlp` object is called on a text
and all pipeline components are applied to the `Doc` in order. Both
[`__call__`](/api/entitylinker#call) and [`pipe`](/api/entitylinker#pipe)
delegate to the [`predict`](/api/entitylinker#predict) and
[`set_annotations`](/api/entitylinker#set_annotations) methods.
[`__call__`](/api/coref#call) and [`pipe`](/api/coref#pipe) delegate to the
[`predict`](/api/coref#predict) and
[`set_annotations`](/api/coref#set_annotations) methods.
> #### Example
>
@ -197,7 +197,7 @@ Modify a batch of documents, saving coreference clusters in `Doc.spans`.
## CoreferenceResolver.update {#update tag="method"}
Learn from a batch of [`Example`](/api/example) objects. Delegates to
[`predict`](/api/entitylinker#predict).
[`predict`](/api/coref#predict).
> #### Example
>

340
website/docs/api/span-predictor.md Normal file
View File

@ -0,0 +1,340 @@
---
title: SpanPredictor
tag: class
source: spacy/pipeline/span_predictor.py
new: 3.4
teaser: 'Pipeline component for resolving tokens into spans'
api_base_class: /api/pipe
api_string_name: span_predictor
api_trainable: true
---
A `SpanPredictor` component takes in tokens (represented as `Span`s of length
1. and resolves them into `Span`s of arbitrary length. The initial use case is
as a post-processing step on word-level [coreference resolution](/api/coref).
The input and output keys used to store `Span`s are configurable.
## Assigned Attributes {#assigned-attributes}
Predictions will be saved to `Doc.spans` as [`SpanGroup`s](/api/spangroup).
Input token spans will be read in using an input prefix, by default
`"coref_head_clusters"`, and output spans will be saved using an output prefix
(default `"coref_clusters"`) plus a serial number starting from zero. The
prefixes are configurable.
| Location | Value |
| ------------------------------------------------- | ------------------------------------------- |
| `Doc.spans[output_prefix + "_" + cluster_number]` | One group of predicted spans. ~~SpanGroup~~ |
## Config and implementation {#config}
The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config). See the
[model architectures](/api/architectures) documentation for details on the
architectures and their arguments and hyperparameters.
> #### Example
>
> ```python
> from spacy.pipeline.span_predictor import DEFAULT_SPAN_PREDICTOR_MODEL
> config={
> "model": DEFAULT_SPAN_PREDICTOR_MODEL,
> "span_cluster_prefix": DEFAULT_CLUSTER_PREFIX,
> },
> nlp.add_pipe("span_predictor", config=config)
> ```
| Setting | Description |
| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [SpanPredictor](/api/architectures#SpanPredictor). ~~Model~~ |
| `input_prefix` | The prefix to use for input `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ |
| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/span_predictor.py
```
## SpanPredictor.\_\_init\_\_ {#init tag="method"}
> #### Example
>
> ```python
> # Construction via add_pipe with default model
> span_predictor = nlp.add_pipe("span_predictor")
>
> # Construction via add_pipe with custom model
> config = {"model": {"@architectures": "my_span_predictor.v1"}}
> span_predictor = nlp.add_pipe("span_predictor", config=config)
>
> # Construction from class
> from spacy.pipeline import SpanPredictor
> span_predictor = SpanPredictor(nlp.vocab, model)
> ```
Create a new pipeline instance. In your application, you would normally use a
shortcut for this and instantiate the component using its string name and
[`nlp.add_pipe`](/api/language#add_pipe).
| Name | Description |
| --------------- | --------------------------------------------------------------------------------------------------- |
| `vocab` | The shared vocabulary. ~~Vocab~~ |
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | |
| `input_prefix` | The prefix to use for input `SpanGroup`s. Defaults to `coref_head_clusters`. ~~str~~ |
| `output_prefix` | The prefix for predicted `SpanGroup`s. Defaults to `coref_clusters`. ~~str~~ |
## SpanPredictor.\_\_call\_\_ {#call tag="method"}
Apply the pipe to one document. The document is modified in place and returned.
This usually happens under the hood when the `nlp` object is called on a text
and all pipeline components are applied to the `Doc` in order. Both
[`__call__`](#call) and [`pipe`](#pipe) delegate to the [`predict`](#predict)
and [`set_annotations`](#set_annotations) methods.
> #### Example
>
> ```python
> doc = nlp("This is a sentence.")
> span_predictor = nlp.add_pipe("span_predictor")
> # This usually happens under the hood
> processed = span_predictor(doc)
> ```
| Name | Description |
| ----------- | -------------------------------- |
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## SpanPredictor.pipe {#pipe tag="method"}
Apply the pipe to a stream of documents. This usually happens under the hood
when the `nlp` object is called on a text and all pipeline components are
applied to the `Doc` in order. Both [`__call__`](/api/span-predictor#call) and
[`pipe`](/api/span-predictor#pipe) delegate to the
[`predict`](/api/span-predictor#predict) and
[`set_annotations`](/api/span-predictor#set_annotations) methods.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> for doc in span_predictor.pipe(docs, batch_size=50):
> pass
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------- |
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
| _keyword-only_ | |
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
| **YIELDS** | The processed documents in order. ~~Doc~~ |
## SpanPredictor.initialize {#initialize tag="method"}
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> span_predictor.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## SpanPredictor.predict {#predict tag="method"}
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
modifying them. Predictions are returned as a list of `MentionClusters`, one for
each input `Doc`. A `MentionClusters` instance is just a list of lists of pairs
of `int`s, where each item corresponds to an input `SpanGroup`, and the `int`s
correspond to token indices.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> spans = span_predictor.predict([doc1, doc2])
> ```
| Name | Description |
| ----------- | ------------------------------------------------------------- |
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
| **RETURNS** | The predicted spans for the `Doc`s. ~~List[MentionClusters]~~ |
## SpanPredictor.set_annotations {#set_annotations tag="method"}
Modify a batch of documents, saving predictions using the output prefix in
`Doc.spans`.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> spans = span_predictor.predict([doc1, doc2])
> span_predictor.set_annotations([doc1, doc2], spans)
> ```
| Name | Description |
| ------- | ------------------------------------------------------------- |
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
| `spans` | The predicted spans for the `docs`. ~~List[MentionClusters]~~ |
## SpanPredictor.update {#update tag="method"}
Learn from a batch of [`Example`](/api/example) objects. Delegates to
[`predict`](/api/span-predictor#predict).
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> optimizer = nlp.initialize()
> losses = span_predictor.update(examples, sgd=optimizer)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | The dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## SpanPredictor.create_optimizer {#create_optimizer tag="method"}
Create an optimizer for the pipeline component.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> optimizer = span_predictor.create_optimizer()
> ```
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
## SpanPredictor.use_params {#use_params tag="method, contextmanager"}
Modify the pipe's model, to use the given parameter values. At the end of the
context, the original parameters are restored.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> with span_predictor.use_params(optimizer.averages):
> span_predictor.to_disk("/best_model")
> ```
| Name | Description |
| -------- | -------------------------------------------------- |
| `params` | The parameter values to use in the model. ~~dict~~ |
## SpanPredictor.to_disk {#to_disk tag="method"}
Serialize the pipe to disk.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> span_predictor.to_disk("/path/to/span_predictor")
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
## SpanPredictor.from_disk {#from_disk tag="method"}
Load the pipe from disk. Modifies the object in place and returns it.
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> span_predictor.from_disk("/path/to/span_predictor")
> ```
| Name | Description |
| -------------- | ----------------------------------------------------------------------------------------------- |
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The modified `SpanPredictor` object. ~~SpanPredictor~~ |
## SpanPredictor.to_bytes {#to_bytes tag="method"}
> #### Example
>
> ```python
> span_predictor = nlp.add_pipe("span_predictor")
> span_predictor_bytes = span_predictor.to_bytes()
> ```
Serialize the pipe to a bytestring.
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The serialized form of the `SpanPredictor` object. ~~bytes~~ |
## SpanPredictor.from_bytes {#from_bytes tag="method"}
Load the pipe from a bytestring. Modifies the object in place and returns it.
> #### Example
>
> ```python
> span_predictor_bytes = span_predictor.to_bytes()
> span_predictor = nlp.add_pipe("span_predictor")
> span_predictor.from_bytes(span_predictor_bytes)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------- |
| `bytes_data` | The data to load from. ~~bytes~~ |
| _keyword-only_ | |
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `SpanPredictor` object. ~~SpanPredictor~~ |
## Serialization fields {#serialization-fields}
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
serialization by passing in the string names via the `exclude` argument.
> #### Example
>
> ```python
> data = span_predictor.to_disk("/path", exclude=["vocab"])
> ```
| Name | Description |
| ------- | -------------------------------------------------------------- |
| `vocab` | The shared [`Vocab`](/api/vocab). |
| `cfg` | The config file. You usually don't want to exclude this. |
| `model` | The binary model data. You usually don't want to exclude this. |