From 14eb20f07a0c57992ee9bcd755e985ddd25b8c4e Mon Sep 17 00:00:00 2001 From: Paul O'Leary McCann Date: Thu, 12 May 2022 13:47:06 +0900 Subject: [PATCH 1/4] Add span predictor docs --- website/docs/api/coref.md | 8 +- website/docs/api/span-predictor.md | 340 +++++++++++++++++++++++++++++ 2 files changed, 344 insertions(+), 4 deletions(-) create mode 100644 website/docs/api/span-predictor.md diff --git a/website/docs/api/coref.md b/website/docs/api/coref.md index 53ed6a4c8..4d43645f3 100644 --- a/website/docs/api/coref.md +++ b/website/docs/api/coref.md @@ -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 > diff --git a/website/docs/api/span-predictor.md b/website/docs/api/span-predictor.md new file mode 100644 index 000000000..1e99b49b2 --- /dev/null +++ b/website/docs/api/span-predictor.md @@ -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. | From 6a8625e7116a58c284a390580058a732f7d6c5f0 Mon Sep 17 00:00:00 2001 From: Paul O'Leary McCann Date: Fri, 13 May 2022 19:28:55 +0900 Subject: [PATCH 2/4] First draft for architecture docs These parameters are probably going to be renamed / have defaults adjusted. Also Model types are off. --- website/docs/api/architectures.md | 63 +++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md index 2bddcb28c..fab07af65 100644 --- a/website/docs/api/architectures.md +++ b/website/docs/api/architectures.md @@ -922,3 +922,66 @@ 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" +> embedding_size = 20 +> dropout = 0.3 +> hidden_size = 1024 +> n_hidden_layers = 2 +> rough_k = 50 +> a_scoring_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~~ | +| `embedding_size` | ~~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~~ | +| `n_hidden_layers` | Depth of the internal network. ~~int~~ | +| `rough_k` | 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~~ | +| `a_scoring_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`. From 13481fbcc2b4e35cf26de356e5cd3c6b49a2c93f Mon Sep 17 00:00:00 2001 From: Paul O'Leary McCann Date: Fri, 13 May 2022 19:29:28 +0900 Subject: [PATCH 3/4] Remove unused param, add TODOs about typing --- spacy/ml/models/coref.py | 3 +-- spacy/ml/models/span_predictor.py | 1 + 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/spacy/ml/models/coref.py b/spacy/ml/models/coref.py index 24b5500a2..cfbe83a7a 100644 --- a/spacy/ml/models/coref.py +++ b/spacy/ml/models/coref.py @@ -22,9 +22,8 @@ def build_wl_coref_model( rough_k: int = 50, # TODO is this not a training loop setting? a_scoring_batch_size: int = 512, - # span predictor embeddings - sp_embedding_size: int = 64, ): + # TODO add model return types # TODO fix this try: dim = tok2vec.get_dim("nO") diff --git a/spacy/ml/models/span_predictor.py b/spacy/ml/models/span_predictor.py index b990b4019..c5cbb328c 100644 --- a/spacy/ml/models/span_predictor.py +++ b/spacy/ml/models/span_predictor.py @@ -17,6 +17,7 @@ def build_span_predictor( hidden_size: int = 1024, dist_emb_size: int = 64, ): + # TODO add model return types # TODO fix this try: dim = tok2vec.get_dim("nO") From 2e8f0e9168fe8a05b3f40ac84995273d31691d37 Mon Sep 17 00:00:00 2001 From: Paul O'Leary McCann Date: Mon, 16 May 2022 16:50:10 +0900 Subject: [PATCH 4/4] Rename coref params --- spacy/ml/models/coref.py | 59 +++++++++++++++---------------- spacy/pipeline/coref.py | 9 +++-- website/docs/api/architectures.md | 39 ++++++++++++-------- 3 files changed, 58 insertions(+), 49 deletions(-) diff --git a/spacy/ml/models/coref.py b/spacy/ml/models/coref.py index cfbe83a7a..299abdc6b 100644 --- a/spacy/ml/models/coref.py +++ b/spacy/ml/models/coref.py @@ -14,14 +14,13 @@ 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, - n_hidden_layers: int = 1, # TODO rename to "depth"? + depth: int = 1, dropout: float = 0.3, # pairs to keep per mention after rough scoring - rough_k: int = 50, - # TODO is this not a training loop setting? - a_scoring_batch_size: int = 512, + antecedent_limit: int = 50, + antecedent_batch_size: int = 512, ): # TODO add model return types # TODO fix this @@ -35,12 +34,12 @@ def build_wl_coref_model( coref_scorer = PyTorchWrapper( CorefScorer( dim, - embedding_size, + distance_embedding_size, hidden_size, - n_hidden_layers, + depth, dropout, - rough_k, - a_scoring_batch_size, + antecedent_limit, + antecedent_batch_size, ), convert_inputs=convert_coref_scorer_inputs, convert_outputs=convert_coref_scorer_outputs, @@ -99,7 +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, ): @@ -109,31 +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( @@ -190,18 +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) @@ -243,7 +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) @@ -289,11 +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 @@ -317,7 +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 @@ -325,15 +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( diff --git a/spacy/pipeline/coref.py b/spacy/pipeline/coref.py index 5237788cc..c5bf8fbbe 100644 --- a/spacy/pipeline/coref.py +++ b/spacy/pipeline/coref.py @@ -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 -n_hidden_layers = 1 +depth = 1 dropout = 0.3 -rough_k = 50 -a_scoring_batch_size = 512 -sp_embedding_size = 64 +antecedent_limit = 50 +antecedent_batch_size = 512 [model.tok2vec] @architectures = "spacy.Tok2Vec.v2" diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md index fab07af65..1a807928d 100644 --- a/website/docs/api/architectures.md +++ b/website/docs/api/architectures.md @@ -939,12 +939,12 @@ performance if working with only token-level clusters is acceptable. > > [model] > @architectures = "spacy.Coref.v1" -> embedding_size = 20 +> distance_embedding_size = 20 > dropout = 0.3 > hidden_size = 1024 -> n_hidden_layers = 2 -> rough_k = 50 -> a_scoring_batch_size = 512 +> depth = 2 +> antecedent_limit = 50 +> antecedent_batch_size = 512 > > [model.tok2vec] > @architectures = "spacy-transformers.TransformerListener.v1" @@ -955,16 +955,16 @@ performance if working with only token-level clusters is acceptable. The `Coref` model architecture is a Thinc `Model`. -| Name | Description | -| ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ | -| `embedding_size` | ~~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~~ | -| `n_hidden_layers` | Depth of the internal network. ~~int~~ | -| `rough_k` | 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~~ | -| `a_scoring_batch_size` | Internal batch size. ~~int~~ | -| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ | +| 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} @@ -985,3 +985,14 @@ The `Coref` model architecture is a Thinc `Model`. > ``` 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]~~ |