mirror of https://github.com/explosion/spaCy.git
add docs for upstream argument of TransformerListener
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@ -125,8 +125,9 @@ class Warnings:
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class Errors:
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E001 = ("No component '{name}' found in pipeline. Available names: {opts}")
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E002 = ("Can't find factory for '{name}' for language {lang} ({lang_code}). "
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"This usually happens when spaCy calls `nlp.{method}` with custom "
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"This usually happens when spaCy calls `nlp.{method}` with a custom "
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"component name that's not registered on the current language class. "
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"If you're using a Transformer, make sure to install 'spacy-transformers'. "
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"If you're using a custom component, make sure you've added the "
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"decorator `@Language.component` (for function components) or "
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"`@Language.factory` (for class components).\n\nAvailable "
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@ -94,7 +94,7 @@ def build_text_classifier(
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# TODO: move to legacy
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@registry.architectures.register("spacy.TextCatEnsemble.v1")
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def build_text_classifier(
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def build_text_classifier_v1(
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width: int,
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embed_size: int,
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pretrained_vectors: Optional[bool],
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@ -143,10 +143,10 @@ argument that connects to the shared `tok2vec` component in the pipeline.
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Construct an embedding layer that separately embeds a number of lexical
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attributes using hash embedding, concatenates the results, and passes it through
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a feed-forward subnetwork to build a mixed representation. The features used
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can be configured with the `attrs` argument. The suggested attributes are
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`NORM`, `PREFIX`, `SUFFIX` and `SHAPE`. This lets the model take into account
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some subword information, without construction a fully character-based
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a feed-forward subnetwork to build a mixed representation. The features used can
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be configured with the `attrs` argument. The suggested attributes are `NORM`,
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`PREFIX`, `SUFFIX` and `SHAPE`. This lets the model take into account some
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subword information, without construction a fully character-based
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representation. If pretrained vectors are available, they can be included in the
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representation as well, with the vectors table will be kept static (i.e. it's
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not updated).
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@ -393,11 +393,12 @@ operate over wordpieces, which usually don't align one-to-one against spaCy
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tokens. The layer therefore requires a reduction operation in order to calculate
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a single token vector given zero or more wordpiece vectors.
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| Name | Description |
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| ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `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]~~ |
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| `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~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
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| Name | Description |
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| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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| `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~~ |
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| `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~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy-transformers.Tok2VecTransformer.v1 {#Tok2VecTransformer}
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@ -563,7 +564,8 @@ from the linear model, where it is stored in `model.attrs["multi_label"]`.
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<Accordion title="spacy.TextCatEnsemble.v1 definition" spaced>
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The v1 was functionally similar, but used an internal `tok2vec` instead of taking it as argument.
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The v1 was functionally similar, but used an internal `tok2vec` instead of
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taking it as argument.
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| Name | Description |
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| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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