mirror of https://github.com/explosion/spaCy.git
115 lines
4.8 KiB
Markdown
115 lines
4.8 KiB
Markdown
---
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title: Model Architectures
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teaser: Pre-defined model architectures included with the core library
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source: spacy/ml/models
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menu:
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- ['Tok2Vec', 'tok2vec']
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- ['Transformers', 'transformers']
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- ['Parser & NER', 'parser']
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- ['Text Classification', 'textcat']
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- ['Entity Linking', 'entitylinker']
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---
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TODO: intro and how architectures work, link to
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[`registry`](/api/top-level#registry),
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[custom models](/usage/training#custom-models) usage etc.
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## Tok2Vec architectures {#tok2vec source="spacy/ml/models/tok2vec.py"}
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### spacy.HashEmbedCNN.v1 {#HashEmbedCNN}
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### spacy.HashCharEmbedCNN.v1 {#HashCharEmbedCNN}
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### spacy.HashCharEmbedBiLSTM.v1 {#HashCharEmbedBiLSTM}
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## Transformer architectures {#transformers source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/architectures.py"}
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The following architectures are provided by the package
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
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[usage documentation](/usage/transformers) for how to integrate the
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architectures into your training config.
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### spacy-transformers.TransformerModel.v1 {#TransformerModel}
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<!-- TODO: description -->
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy-transformers.TransformerModel.v1"
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> name = "roberta-base"
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> tokenizer_config = {"use_fast": true}
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>
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> [model.get_spans]
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> @span_getters = "strided_spans.v1"
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> window = 128
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> stride = 96
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> ```
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| Name | Type | Description |
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| ------------------ | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `name` | str | Any model name that can be loaded by [`transformers.AutoModel`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModel). |
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| `get_spans` | `Callable` | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. |
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| `tokenizer_config` | `Dict[str, Any]` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). |
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### spacy-transformers.Tok2VecListener.v1 {#Tok2VecListener}
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<!-- TODO: description -->
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy-transformers.Tok2VecListener.v1"
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> grad_factor = 1.0
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>
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> [model.pooling]
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> @layers = "reduce_mean.v1"
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> ```
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| Name | Type | Description |
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| ------------- | ------------------------- | ---------------------------------------------------------------------------------------------- |
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| `grad_factor` | float | Factor for weighting the gradient if multiple components listen to the same transformer model. |
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| `pooling` | `Model[Ragged, Floats2d]` | Pooling layer to determine how the vector for each spaCy token will be computed. |
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## Parser & NER architectures {#parser source="spacy/ml/models/parser.py"}
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### spacy.TransitionBasedParser.v1 {#TransitionBasedParser}
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TransitionBasedParser.v1"
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> nr_feature_tokens = 6
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> hidden_width = 64
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> maxout_pieces = 2
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>
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> [model.tok2vec]
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> # ...
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> ```
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| Name | Type | Description |
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| ------------------- | ------------------------------------------ | ----------- |
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| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | |
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| `nr_feature_tokens` | int | |
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| `hidden_width` | int | |
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| `maxout_pieces` | int | |
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| `use_upper` | bool | |
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| `nO` | int | |
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## Text classification architectures {#textcat source="spacy/ml/models/textcat.py"}
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### spacy.TextCatEnsemble.v1 {#TextCatEnsemble}
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### spacy.TextCatBOW.v1 {#TextCatBOW}
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### spacy.TextCatCNN.v1 {#TextCatCNN}
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### spacy.TextCatLowData.v1 {#TextCatLowData}
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## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}
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### spacy.EntityLinker.v1 {#EntityLinker}
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