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Merge pull request #6409 from svlandeg/feature/trf-docs

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Ines Montani 2020-12-08 06:32:10 +01:00 committed by GitHub
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3
spacy/errors.py
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@ -125,8 +125,9 @@ class Warnings:
class Errors:
E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
"This usually happens when spaCy calls `nlp.{method}` with custom "
"This usually happens when spaCy calls `nlp.{method}` with a custom "
"component name that's not registered on the current language class. "
"If you're using a Transformer, make sure to install 'spacy-transformers'. "
"If you're using a custom component, make sure you've added the "
"decorator `@Language.component` (for function components) or "
"`@Language.factory` (for class components).\n\nAvailable "

22
website/docs/api/architectures.md
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@ -143,10 +143,10 @@ argument that connects to the shared `tok2vec` component in the pipeline.
Construct an embedding layer that separately embeds a number of lexical
attributes using hash embedding, concatenates the results, and passes it through
a feed-forward subnetwork to build a mixed representation. The features used
can be configured with the `attrs` argument. The suggested attributes are
`NORM`, `PREFIX`, `SUFFIX` and `SHAPE`. This lets the model take into account
some subword information, without construction a fully character-based
a feed-forward subnetwork to build a mixed representation. The features used can
be configured with the `attrs` argument. The suggested attributes are `NORM`,
`PREFIX`, `SUFFIX` and `SHAPE`. This lets the model take into account some
subword information, without construction a fully character-based
representation. If pretrained vectors are available, they can be included in the
representation as well, with the vectors table will be kept static (i.e. it's
not updated).
@ -393,11 +393,12 @@ operate over wordpieces, which usually don't align one-to-one against spaCy
tokens. The layer therefore requires a reduction operation in order to calculate
a single token vector given zero or more wordpiece vectors.
| Name | Description |
| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `pooling` | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. ~~Model[Ragged, Floats2d]~~ |
| `grad_factor` | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. ~~float~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
| Name | Description |
| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `pooling` | A reduction layer used to calculate the token vectors based on zero or more wordpiece vectors. If in doubt, mean pooling (see [`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean)) is usually a good choice. ~~Model[Ragged, Floats2d]~~ |
| `grad_factor` | Reweight gradients from the component before passing them upstream. You can set this to `0` to "freeze" the transformer weights with respect to the component, or use it to make some components more significant than others. Leaving it at `1.0` is usually fine. ~~float~~ |
| `upstream` | A string to identify the "upstream" `Transformer` component to communicate with. By default, the upstream name is the wildcard string `"*"`, but you could also specify the name of the `Transformer` component. You'll almost never have multiple upstream `Transformer` components, so the wildcard string will almost always be fine. ~~str~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
### spacy-transformers.Tok2VecTransformer.v1 {#Tok2VecTransformer}
@ -563,7 +564,8 @@ from the linear model, where it is stored in `model.attrs["multi_label"]`.
<Accordion title="spacy.TextCatEnsemble.v1 definition" spaced>
The v1 was functionally similar, but used an internal `tok2vec` instead of taking it as argument.
The v1 was functionally similar, but used an internal `tok2vec` instead of
taking it as argument.
| Name | Description |
| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |