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	Update docs
Parameter names in architecture docs were not updated after parameters were renamed.
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			@ -29,7 +29,7 @@ distance_embedding_size = 64
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conv_channels = 4
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window_size = 1
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max_distance = 128
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prefix = coref_head_clusters
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prefix = "coref_head_clusters"
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[model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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			@ -587,8 +587,8 @@ consists of either two or three subnetworks:
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  run once for each batch.
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- **lower**: Construct a feature-specific vector for each `(token, feature)`
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  pair. This is also run once for each batch. Constructing the state
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  representation is then a matter of summing the component features and
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  applying the non-linearity.
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  representation is then a matter of summing the component features and applying
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  the non-linearity.
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- **upper** (optional): A feed-forward network that predicts scores from the
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  state representation. If not present, the output from the lower model is used
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  as action scores directly.
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			@ -628,8 +628,8 @@ same signature, but the `use_upper` argument was `True` by default.
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> ```
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Build a tagger model, using a provided token-to-vector component. The tagger
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model adds a linear layer with softmax activation to predict scores given
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the token vectors.
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model adds a linear layer with softmax activation to predict scores given the
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token vectors.
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| Name        | Description                                                                                |
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| ----------- | ------------------------------------------------------------------------------------------ |
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			@ -920,8 +920,8 @@ A function that reads an existing `KnowledgeBase` from file.
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A function that takes as input a [`KnowledgeBase`](/api/kb) and a
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[`Span`](/api/span) object denoting a named entity, and returns a list of
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plausible [`Candidate`](/api/kb/#candidate) objects. The default
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`CandidateGenerator` uses the text of a mention to find its potential
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aliases in the `KnowledgeBase`. Note that this function is case-dependent.
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`CandidateGenerator` uses the text of a mention to find its potential aliases in
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the `KnowledgeBase`. Note that this function is case-dependent.
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## Coreference Architectures
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			@ -975,7 +975,11 @@ The `Coref` model architecture is a Thinc `Model`.
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> [model]
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> @architectures = "spacy.SpanPredictor.v1"
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> hidden_size = 1024
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> dist_emb_size = 64
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> distance_embedding_size = 64
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> conv_channels = 4
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> window_size = 1
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> max_distance = 128
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> prefix = "coref_head_clusters"
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>
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> [model.tok2vec]
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> @architectures = "spacy-transformers.TransformerListener.v1"
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			@ -987,12 +991,13 @@ The `Coref` model architecture is a Thinc `Model`.
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The `SpanPredictor` model architecture is a Thinc `Model`.
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| Name                      | Description                                                                                                                   |
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| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
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| `tok2vec`                 | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                       |
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| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~                                                              |
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| `dropout`                 | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ |
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| `hidden_size`             | Size of the main internal layers. ~~int~~                                                                                     |
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| `depth`                   | Depth of the internal network. ~~int~~                                                                                                                                                   |
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| `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~~ |
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| `antecedent_batch_size`   | Internal batch size. ~~int~~                                                                                                                                                             |
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| `conv_channels`           | The number of channels in the internal CNN. ~~int~~                                                                           |
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| `window_size`             | The number of neighboring tokens to consider in the internal CNN. `1` means consider one token on each side. ~~int~~          |
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| `max_distance`            | The longest possible length of a predicted span. ~~int~~                                                                      |
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| `prefix`                  | The prefix that indicates spans to use for input data. ~~string~~                                                             |
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| **CREATES**               | The model using the architecture. ~~Model[List[Doc], TupleFloats2d]~~                                                         |
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