mirror of https://github.com/explosion/spaCy.git
Update docs for spacy-transformers v1.1 data classes (#9361)
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Adriane Boyd
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@ -397,12 +397,13 @@ are wrapped into the
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by this class. Instances of this class are typically assigned to the
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[`Doc._.trf_data`](/api/transformer#assigned-attributes) extension attribute.
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| Name | Description |
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| --------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `tokens` | A slice of the tokens data produced by the tokenizer. This may have several fields, including the token IDs, the texts and the attention mask. See the [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) object for details. ~~dict~~ |
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| `tensors` | The activations for the `Doc` from the transformer. Usually the last tensor that is 3-dimensional will be the most important, as that will provide the final hidden state. Generally activations that are 2-dimensional will be attention weights. Details of this variable will differ depending on the underlying transformer model. ~~List[FloatsXd]~~ |
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| `align` | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
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| `width` | The width of the last hidden layer. ~~int~~ |
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `tokens` | A slice of the tokens data produced by the tokenizer. This may have several fields, including the token IDs, the texts and the attention mask. See the [`transformers.BatchEncoding`](https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding) object for details. ~~dict~~ |
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| `model_output` | The model output from the transformer model, determined by the model and transformer config. New in `spacy-transformers` v1.1.0. ~~transformers.file_utils.ModelOutput~~ |
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| `tensors` | The `model_output` in the earlier `transformers` tuple format converted using [`ModelOutput.to_tuple()`](https://huggingface.co/transformers/main_classes/output.html#transformers.file_utils.ModelOutput.to_tuple). Returns `Tuple` instead of `List` as of `spacy-transformers` v1.1.0. ~~Tuple[Union[FloatsXd, List[FloatsXd]]]~~ |
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| `align` | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
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| `width` | The width of the last hidden layer. ~~int~~ |
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### TransformerData.empty {#transformerdata-emoty tag="classmethod"}
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@ -412,19 +413,32 @@ Create an empty `TransformerData` container.
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| ----------- | ---------------------------------- |
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| **RETURNS** | The container. ~~TransformerData~~ |
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<Accordion title="Previous versions of TransformerData" spaced>
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In `spacy-transformers` v1.0, the model output is stored in
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`TransformerData.tensors` as `List[Union[FloatsXd]]` and only includes the
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activations for the `Doc` from the transformer. Usually the last tensor that is
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3-dimensional will be the most important, as that will provide the final hidden
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state. Generally activations that are 2-dimensional will be attention weights.
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Details of this variable will differ depending on the underlying transformer
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model.
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</Accordion>
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## FullTransformerBatch {#fulltransformerbatch tag="dataclass"}
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Holds a batch of input and output objects for a transformer model. The data can
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then be split to a list of [`TransformerData`](/api/transformer#transformerdata)
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objects to associate the outputs to each [`Doc`](/api/doc) in the batch.
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| Name | Description |
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| ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `spans` | The batch of input spans. The outer list refers to the Doc objects in the batch, and the inner list are the spans for that `Doc`. Note that spans are allowed to overlap or exclude tokens, but each `Span` can only refer to one `Doc` (by definition). This means that within a `Doc`, the regions of the output tensors that correspond to each `Span` may overlap or have gaps, but for each `Doc`, there is a non-overlapping contiguous slice of the outputs. ~~List[List[Span]]~~ |
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| `tokens` | The output of the tokenizer. ~~transformers.BatchEncoding~~ |
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| `tensors` | The output of the transformer model. ~~List[torch.Tensor]~~ |
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| `align` | Alignment from the spaCy tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
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| `doc_data` | The outputs, split per `Doc` object. ~~List[TransformerData]~~ |
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `spans` | The batch of input spans. The outer list refers to the Doc objects in the batch, and the inner list are the spans for that `Doc`. Note that spans are allowed to overlap or exclude tokens, but each `Span` can only refer to one `Doc` (by definition). This means that within a `Doc`, the regions of the output tensors that correspond to each `Span` may overlap or have gaps, but for each `Doc`, there is a non-overlapping contiguous slice of the outputs. ~~List[List[Span]]~~ |
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| `tokens` | The output of the tokenizer. ~~transformers.BatchEncoding~~ |
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| `model_output` | The model output from the transformer model, determined by the model and transformer config. New in `spacy-transformers` v1.1.0. ~~transformers.file_utils.ModelOutput~~ |
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| `tensors` | The `model_output` in the earlier `transformers` tuple format converted using [`ModelOutput.to_tuple()`](https://huggingface.co/transformers/main_classes/output.html#transformers.file_utils.ModelOutput.to_tuple). Returns `Tuple` instead of `List` as of `spacy-transformers` v1.1.0. ~~Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]~~ |
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| `align` | Alignment from the spaCy tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
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| `doc_data` | The outputs, split per `Doc` object. ~~List[TransformerData]~~ |
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### FullTransformerBatch.unsplit_by_doc {#fulltransformerbatch-unsplit_by_doc tag="method"}
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@ -447,6 +461,13 @@ Split a `TransformerData` object that represents a batch into a list with one
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| ----------- | ------------------------------------------ |
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| **RETURNS** | The split batch. ~~List[TransformerData]~~ |
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<Accordion title="Previous versions of FullTransformerBatch" spaced>
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In `spacy-transformers` v1.0, the model output is stored in
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`FullTransformerBatch.tensors` as `List[torch.Tensor]`.
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</Accordion>
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## Span getters {#span_getters source="github.com/explosion/spacy-transformers/blob/master/spacy_transformers/span_getters.py"}
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Span getters are functions that take a batch of [`Doc`](/api/doc) objects and
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@ -43,6 +43,7 @@
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"cymem.Pool": "https://github.com/explosion/cymem",
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"preshed.BloomFilter": "https://github.com/explosion/preshed",
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"transformers.BatchEncoding": "https://huggingface.co/transformers/main_classes/tokenizer.html#transformers.BatchEncoding",
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"transformers.file_utils.ModelOutput": "https://huggingface.co/transformers/main_classes/output.html#modeloutput",
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"torch.Tensor": "https://pytorch.org/docs/stable/tensors.html",
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"numpy.ndarray": "https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html",
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"Match": "https://docs.python.org/3/library/re.html#match-objects",
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