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Parameter names in architecture docs were not updated after parameters
were renamed.
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Paul O'Leary McCann 2022-07-06 17:13:31 +09:00
parent c59aeeb0ae
commit da9c379355
2 changed files with 23 additions and 18 deletions
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spacy/pipeline/span_predictor.py
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@ -29,7 +29,7 @@ distance_embedding_size = 64
conv_channels = 4
window_size = 1
max_distance = 128
prefix = coref_head_clusters
prefix = "coref_head_clusters"
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

39
website/docs/api/architectures.md
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@ -587,8 +587,8 @@ consists of either two or three subnetworks:
run once for each batch.
- **lower**: Construct a feature-specific vector for each `(token, feature)`
pair. This is also run once for each batch. Constructing the state
representation is then a matter of summing the component features and
applying the non-linearity.
representation is then a matter of summing the component features and applying
the non-linearity.
- **upper** (optional): A feed-forward network that predicts scores from the
state representation. If not present, the output from the lower model is used
as action scores directly.
@ -628,8 +628,8 @@ same signature, but the `use_upper` argument was `True` by default.
> ```
Build a tagger model, using a provided token-to-vector component. The tagger
model adds a linear layer with softmax activation to predict scores given
the token vectors.
model adds a linear layer with softmax activation to predict scores given the
token vectors.
| Name | Description |
| ----------- | ------------------------------------------------------------------------------------------ |
@ -920,8 +920,8 @@ A function that reads an existing `KnowledgeBase` from file.
A function that takes as input a [`KnowledgeBase`](/api/kb) and a
[`Span`](/api/span) object denoting a named entity, and returns a list of
plausible [`Candidate`](/api/kb/#candidate) objects. The default
`CandidateGenerator` uses the text of a mention to find its potential
aliases in the `KnowledgeBase`. Note that this function is case-dependent.
`CandidateGenerator` uses the text of a mention to find its potential aliases in
the `KnowledgeBase`. Note that this function is case-dependent.
## Coreference Architectures
@ -975,7 +975,11 @@ The `Coref` model architecture is a Thinc `Model`.
> [model]
> @architectures = "spacy.SpanPredictor.v1"
> hidden_size = 1024
> dist_emb_size = 64
> distance_embedding_size = 64
> conv_channels = 4
> window_size = 1
> max_distance = 128
> prefix = "coref_head_clusters"
>
> [model.tok2vec]
> @architectures = "spacy-transformers.TransformerListener.v1"
@ -986,13 +990,14 @@ 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]~~ |
| 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~~ |
| `conv_channels` | The number of channels in the internal CNN. ~~int~~ |
| `window_size` | The number of neighboring tokens to consider in the internal CNN. `1` means consider one token on each side. ~~int~~ |
| `max_distance` | The longest possible length of a predicted span. ~~int~~ |
| `prefix` | The prefix that indicates spans to use for input data. ~~string~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], TupleFloats2d]~~ |