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Update architectures

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Paul O'Leary McCann 2022-08-04 15:36:40 +09:00
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website/docs/api/architectures.md
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@ -923,15 +923,15 @@ 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.
## Coreference Architectures
## Coreference Architectures {#coref-architectures tag="experimental" new="3.4"}
A [`CoreferenceResolver`](/api/coref) component identifies tokens that refer to
the same entity. A [`SpanPredictor`](/api/span-predictor) component infers spans
the same entity. A [`SpanResolver`](/api/span-resolver) 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
traditional coreference models. You can also omit the `SpanResolver` for faster
performance if working with only token-level clusters is acceptable.
### spacy.Coref.v1 {#Coref}
### spacy.Coref.v1 {#Coref tag="experimental"}
> #### Example Config
>
@ -966,14 +966,14 @@ The `Coref` model architecture is a Thinc `Model`.
| `antecedent_batch_size` | Internal batch size. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
### spacy.SpanPredictor.v1 {#SpanPredictor}
### spacy.SpanResolver.v1 {#SpanResolver tag="experimental"}
> #### Example Config
>
> ```ini
>
> [model]
> @architectures = "spacy.SpanPredictor.v1"
> @architectures = "spacy.SpanResolver.v1"
> hidden_size = 1024
> distance_embedding_size = 64
> conv_channels = 4
@ -988,7 +988,7 @@ The `Coref` model architecture is a Thinc `Model`.
> pooling = {"@layers":"reduce_mean.v1"}
> ```
The `SpanPredictor` model architecture is a Thinc `Model`.
The `SpanResolver` model architecture is a Thinc `Model`.
| Name | Description |
| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |