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Update docs [ci skip]

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Ines Montani 2020-07-29 19:41:34 +02:00
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45
website/docs/api/architectures.md
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@ -24,10 +24,55 @@ TODO: intro and how architectures work, link to
## Transformer architectures {#transformers source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/architectures.py"} ## Transformer architectures {#transformers source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/architectures.py"}
The following architectures are provided by the package
[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
[usage documentation](/usage/transformers) for how to integrate the
architectures into your training config.
### spacy-transformers.TransformerModel.v1 {#TransformerModel} ### spacy-transformers.TransformerModel.v1 {#TransformerModel}
<!-- TODO: description -->
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy-transformers.TransformerModel.v1"
> name = "roberta-base"
> tokenizer_config = {"use_fast": true}
>
> [model.get_spans]
> @span_getters = "strided_spans.v1"
> window = 128
> stride = 96
> ```
| Name | Type | Description |
| ------------------ | ---------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `name` | str | Any model name that can be loaded by [`transformers.AutoModel`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModel). |
| `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. |
| `tokenizer_config` | `Dict[str, Any]` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). |
### spacy-transformers.Tok2VecListener.v1 {#Tok2VecListener} ### spacy-transformers.Tok2VecListener.v1 {#Tok2VecListener}
<!-- TODO: description -->
> #### Example Config
>
> ```ini
> [model]
> @architectures = "spacy-transformers.Tok2VecListener.v1"
> grad_factor = 1.0
>
> [model.pooling]
> @layers = "reduce_mean.v1"
> ```
| Name | Type | Description |
| ------------- | ------------------------- | ---------------------------------------------------------------------------------------------- |
| `grad_factor` | float | Factor for weighting the gradient if multiple components listen to the same transformer model. |
| `pooling` | `Model[Ragged, Floats2d]` | Pooling layer to determine how the vector for each spaCy token will be computed. |
## Parser & NER architectures {#parser source="spacy/ml/models/parser.py"} ## Parser & NER architectures {#parser source="spacy/ml/models/parser.py"}
### spacy.TransitionBasedParser.v1 {#TransitionBasedParser} ### spacy.TransitionBasedParser.v1 {#TransitionBasedParser}

14
website/docs/api/transformer.md
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@ -366,13 +366,13 @@ Transformer tokens and outputs for one `Doc` object.
<!-- TODO: --> <!-- TODO: -->
| Name | Type | Description | | Name | Type | Description |
| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- | | ---------- | -------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------- |
| `spans` | `List[List[Span]]` | <!-- TODO: --> | | `spans` | `List[List[Span]]` | <!-- TODO: --> |
| `tokens` | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html?highlight=batchencoding#transformers.BatchEncoding) | <!-- TODO: --> | | `tokens` | [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) | <!-- TODO: --> |
| `tensors` | `List[torch.Tensor]` | <!-- TODO: --> | | `tensors` | `List[torch.Tensor]` | <!-- TODO: --> |
| `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | <!-- TODO: --> | | `align` | [`Ragged`](https://thinc.ai/docs/api-types#ragged) | <!-- TODO: --> |
| `doc_data` | `List[TransformerData]` | <!-- TODO: also mention it's property --> | | `doc_data` | `List[TransformerData]` | <!-- TODO: also mention it's property --> |
### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"} ### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"}

22
website/docs/usage/processing-pipelines.md
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@ -220,15 +220,19 @@ available pipeline components and component functions.
> ruler = nlp.add_pipe("entity_ruler") > ruler = nlp.add_pipe("entity_ruler")
> ``` > ```
| String name | Component | Description | | String name | Component | Description |
| --------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------- | | --------------- | ----------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `tagger` | [`Tagger`](/api/tagger) | Assign part-of-speech-tags. | | `tagger` | [`Tagger`](/api/tagger) | Assign part-of-speech-tags. |
| `parser` | [`DependencyParser`](/api/dependencyparser) | Assign dependency labels. | | `parser` | [`DependencyParser`](/api/dependencyparser) | Assign dependency labels. |
| `ner` | [`EntityRecognizer`](/api/entityrecognizer) | Assign named entities. | | `ner` | [`EntityRecognizer`](/api/entityrecognizer) | Assign named entities. |
| `entity_linker` | [`EntityLinker`](/api/entitylinker) | Assign knowledge base IDs to named entities. Should be added after the entity recognizer. | | `entity_linker` | [`EntityLinker`](/api/entitylinker) | Assign knowledge base IDs to named entities. Should be added after the entity recognizer. |
| `textcat` | [`TextCategorizer`](/api/textcategorizer) | Assign text categories. | | `entity_ruler` | [`EntityRuler`](/api/entityruler) | Assign named entities based on pattern rules and dictionaries. |
| `entity_ruler` | [`EntityRuler`](/api/entityruler) | Assign named entities based on pattern rules. | | `textcat` | [`TextCategorizer`](/api/textcategorizer) | Assign text categories. |
| `sentencizer` | [`Sentencizer`](/api/sentencizer) | Add rule-based sentence segmentation without the dependency parse. | | `morphologizer` | [`Morphologizer`](/api/morphologizer) | Assign morphological features and coarse-grained POS tags. |
| `senter` | [`SentenceRecognizer`](/api/sentencerecognizer) | Assign sentence boundaries. |
| `sentencizer` | [`Sentencizer`](/api/sentencizer) | Add rule-based sentence segmentation without the dependency parse. |
| `tok2vec` | [`Tok2Vec`](/api/tok2vec) | <!-- TODO: --> |
| `transformer` | [`Transformer`](/api/transformer) | Assign the tokens and outputs of a transformer model. |
<!-- TODO: update with more components --> <!-- TODO: update with more components -->

12
website/docs/usage/transformers.md
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@ -101,7 +101,9 @@ evaluate, package and visualize your model.
The `[components]` section in the [`config.cfg`](#TODO:) describes the pipeline The `[components]` section in the [`config.cfg`](#TODO:) describes the pipeline
components and the settings used to construct them, including their model components and the settings used to construct them, including their model
implementation. Here's a config snippet for the implementation. Here's a config snippet for the
[`Transformer`](/api/transformer) component, along with matching Python code: [`Transformer`](/api/transformer) component, along with matching Python code. In
this case, the `[components.transformer]` block describes the `transformer`
component:
> #### Python equivalent > #### Python equivalent
> >
@ -257,10 +259,10 @@ grad_factor = 1.0
``` ```
The [Tok2VecListener](/api/architectures#Tok2VecListener) layer expects a The [Tok2VecListener](/api/architectures#Tok2VecListener) layer expects a
[pooling layer](https://thinc.ai/docs/api-layers#reduction-ops), which needs to [pooling layer](https://thinc.ai/docs/api-layers#reduction-ops) as the argument
be of type `Model[Ragged, Floats2d]`. This layer determines how the vector for `pooling`, which needs to be of type `Model[Ragged, Floats2d]`. This layer
each spaCy token will be computed from the zero or more source rows the token is determines how the vector for each spaCy token will be computed from the zero or
aligned against. Here we use the more source rows the token is aligned against. Here we use the
[`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean) layer, which [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean) layer, which
averages the wordpiece rows. We could instead use `reduce_last`, averages the wordpiece rows. We could instead use `reduce_last`,
[`reduce_max`](https://thinc.ai/docs/api-layers#reduce_max), or a custom [`reduce_max`](https://thinc.ai/docs/api-layers#reduce_max), or a custom

26
website/src/widgets/quickstart-install.js
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@ -36,13 +36,18 @@ const DATA = [
], ],
}, },
{ {
id: 'data', id: 'addition',
title: 'Additional data', title: 'Additions',
multiple: true, multiple: true,
options: [ options: [
{
id: 'transformers',
title: 'Transformers',
help: 'Use transformers like BERT to train your spaCy models',
},
{ {
id: 'lookups', id: 'lookups',
title: 'Lemmatization', title: 'Lemmatizer data',
help: 'Install additional lookup tables and rules for lemmatization', help: 'Install additional lookup tables and rules for lemmatization',
}, },
], ],
@ -86,13 +91,22 @@ const QuickstartInstall = ({ id, title }) => (
set PYTHONPATH=C:\path\to\spaCy set PYTHONPATH=C:\path\to\spaCy
</QS> </QS>
<QS package="source">pip install -r requirements.txt</QS> <QS package="source">pip install -r requirements.txt</QS>
<QS data="lookups" package="pip"> <QS addition="transformers" package="pip">
pip install -U spacy-lookups-transformers
</QS>
<QS addition="transformers" package="source">
pip install -U spacy-transformers
</QS>
<QS addition="transformers" package="conda">
conda install -c conda-forge spacy-transformers
</QS>
<QS addition="lookups" package="pip">
pip install -U spacy-lookups-data pip install -U spacy-lookups-data
</QS> </QS>
<QS data="lookups" package="source"> <QS addition="lookups" package="source">
pip install -U spacy-lookups-data pip install -U spacy-lookups-data
</QS> </QS>
<QS data="lookups" package="conda"> <QS addition="lookups" package="conda">
conda install -c conda-forge spacy-lookups-data conda install -c conda-forge spacy-lookups-data
</QS> </QS>
<QS package="source">python setup.py build_ext --inplace</QS> <QS package="source">python setup.py build_ext --inplace</QS>