diff --git a/spacy/errors.py b/spacy/errors.py index f4fd3731f..9cbc4ef1b 100644 --- a/spacy/errors.py +++ b/spacy/errors.py @@ -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 " diff --git a/spacy/ml/models/textcat.py b/spacy/ml/models/textcat.py index d4aed2839..181bbcf4c 100644 --- a/spacy/ml/models/textcat.py +++ b/spacy/ml/models/textcat.py @@ -94,7 +94,7 @@ def build_text_classifier( # TODO: move to legacy @registry.architectures.register("spacy.TextCatEnsemble.v1") -def build_text_classifier( +def build_text_classifier_v1( width: int, embed_size: int, pretrained_vectors: Optional[bool], diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md index 517ab9e7a..479e56f88 100644 --- a/website/docs/api/architectures.md +++ b/website/docs/api/architectures.md @@ -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"]`. -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 | | -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |