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website/docs/api/curatedtransformer.mdx
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@ -1,7 +1,7 @@
---
title: CuratedTransformer
teaser:
Pipeline component for multi-task learning with curated transformer models
Pipeline component for multi-task learning with Curated Transformer models
tag: class
source: github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_transformers/pipeline/transformer.py
version: 3.7
@ -33,9 +33,9 @@ If you want to use another type of model, use
[spacy-transformers](/api/spacy-transformers), which allows you to use all
Hugging Face transformer models with spaCy.
You will usually connect downstream components to a shared curated transformer
using one of the curated transformer listener layers. This works similarly to
spaCy's [Tok2Vec](/api/tok2vec), and the
You will usually connect downstream components to a shared Curated Transformer
pipe using one of the Curated Transformer listener layers. This works similarly
to spaCy's [Tok2Vec](/api/tok2vec), and the
[Tok2VecListener](/api/architectures/#Tok2VecListener) sublayer. The component
assigns the output of the transformer to the `Doc`'s extension attributes. To
access the values, you can use the custom
@ -50,9 +50,9 @@ The component sets the following
| Location | Value |
| ---------------- | -------------------------------------------------------------------------- |
| `Doc._.trf_data` | Curated transformer outputs for the `Doc` object. ~~DocTransformerOutput~~ |
| `Doc._.trf_data` | Curated Transformer outputs for the `Doc` object. ~~DocTransformerOutput~~ |
## Config and implementation {id="config"}
## Config and Implementation {id="config"}
The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the
@ -380,7 +380,7 @@ Load the pipe from a bytestring. Modifies the object in place and returns it.
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
| **RETURNS** | The `CuratedTransformer` object. ~~CuratedTransformer~~ |
## Serialization fields {id="serialization-fields"}
## Serialization Fields {id="serialization-fields"}
During serialization, spaCy will export several data fields used to restore
different aspects of the object. If needed, you can exclude them from
@ -445,7 +445,7 @@ Return the number of layer outputs stored in the `DocTransformerOutput` instance
| ----------- | -------------------------- |
| **RETURNS** | Numbef of outputs. ~~int~~ |
## Span getters {id="span_getters",source="github.com/explosion/spacy-transformers/blob/master/spacy_curated_transformers/span_getters.py"}
## Span Getters {id="span_getters",source="github.com/explosion/spacy-transformers/blob/master/spacy_curated_transformers/span_getters.py"}
Span getters are functions that take a batch of [`Doc`](/api/doc) objects and
return a lists of [`Span`](/api/span) objects for each doc to be processed by
@ -565,27 +565,6 @@ Construct a callback that initializes a WordPiece piece encoder model.
| ------ | ------------------------------------------------ |
| `path` | Path to the serialized WordPiece model. ~~Path~~ |
## Model Loaders
### HFTransformerEncoderLoader.v1 {id="hf_trfencoder_loader",tag="registered_function"}
Construct a callback that initializes a supported transformer model with weights
from a corresponding HuggingFace model.
| Name | Description |
| ---------- | ------------------------------------------ |
| `name` | Name of the HuggingFace model. ~~str~~ |
| `revision` | Name of the model revision/branch. ~~str~~ |
### PyTorchCheckpointLoader.v1 {id="pytorch_checkpoint_loader",tag="registered_function"}
Construct a callback that initializes a supported transformer model with weights
from a PyTorch checkpoint.
| Name | Description |
| ------ | ---------------------------------------- |
| `path` | Path to the PyTorch checkpoint. ~~Path~~ |
## Callbacks
### gradual_transformer_unfreezing.v1 {id="gradual_transformer_unfreezing",tag="registered_function"}