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First draft for architecture docs

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These parameters are probably going to be renamed / have defaults
adjusted. Also Model types are off.
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Paul O'Leary McCann 2022-05-13 19:28:55 +09:00
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website/docs/api/architectures.md
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@ -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`.