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initial documentation run
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@ -481,6 +481,318 @@ The other arguments are shared between all versions.
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</Accordion>
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## Curated transformer architectures {id="curated-trf",source="https://github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_transformers/models/architectures.py"}
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The following architectures are provided by the package
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[`spacy-curated-transformers`](https://github.com/explosion/spacy-curated-transformers). See the
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[usage documentation](/usage/embeddings-transformers#transformers) for how to
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integrate the architectures into your training config.
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<Infobox variant="warning">
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Note that in order to use these architectures in your config, you need to
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install the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers). See the
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[installation docs](/usage/embeddings-transformers#transformers-installation)
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for details and system requirements.
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</Infobox>
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### spacy-curated-transformers.AlbertTransformer.v1
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Construct an ALBERT transformer model.
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| Name | Description |
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|--------------------------------|-----------------------------------------------------------------------------|
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| `vocab_size` | Vocabulary size. ~~int~~ |
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| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
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| `with_spans` | piece_encoder (Model) ~~Callable~~ |
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| `with_spans` | The piece encoder to segment input tokens. ~~Callable~~ |
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| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
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| `embedding_width` | Width of the embedding representations. ~~int~~ |
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| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
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| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
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| `hidden_dropout_prob` | embedding layers. ~~float~~ |
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| `hidden_width` | Width of the final representations. ~~int~~ |
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| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
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| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
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| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
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| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
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| `model_max_length` | Maximum length of model inputs. ~~int~~ |
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| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
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| `num_hidden_groups` | Number of layer groups whose constituents share parameters. ~~int~~ |
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| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
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| `padding_idx` | Index of the padding meta-token. ~~int~~ |
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| `type_vocab_size` | Type vocabulary size. ~~int~~ |
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| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
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### spacy-curated-transformers.BertTransformer.v1
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Construct a BERT transformer model.
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| Name | Description |
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|--------------------------------|-----------------------------------------------------------------------------|
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| `vocab_size` | Vocabulary size. ~~int~~ |
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| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
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| `with_spans` | piece_encoder (Model) ~~Callable~~ |
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| `with_spans` | The piece encoder to segment input tokens. ~~Callable~~ |
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| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
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| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
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| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
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| `hidden_dropout_prob` | embedding layers. ~~float~~ |
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| `hidden_width` | Width of the final representations. ~~int~~ |
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| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
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| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
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| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
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| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
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| `model_max_length` | Maximum length of model inputs. ~~int~~ |
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| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
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| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
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| `padding_idx` | Index of the padding meta-token. ~~int~~ |
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| `type_vocab_size` | Type vocabulary size. ~~int~~ |
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| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
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### spacy-curated-transformers.CamembertTransformer.v1
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Construct a CamemBERT transformer model.
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| Name | Description |
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|--------------------------------|-----------------------------------------------------------------------------|
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| `vocab_size` | Vocabulary size. ~~int~~ |
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| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
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| `with_spans` | piece_encoder (Model) ~~Callable~~ |
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| `with_spans` | The piece encoder to segment input tokens. ~~Callable~~ |
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| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
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| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
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| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
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| `hidden_dropout_prob` | embedding layers. ~~float~~ |
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| `hidden_width` | Width of the final representations. ~~int~~ |
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| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
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| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
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| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
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| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
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| `model_max_length` | Maximum length of model inputs. ~~int~~ |
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| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
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| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
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| `padding_idx` | Index of the padding meta-token. ~~int~~ |
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| `type_vocab_size` | Type vocabulary size. ~~int~~ |
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| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
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### spacy-curated-transformers.RobertaTransformer.v1
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Construct a RoBERTa transformer model.
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| Name | Description |
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|--------------------------------|-----------------------------------------------------------------------------|
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| `vocab_size` | Vocabulary size. ~~int~~ |
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| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
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| `with_spans` | piece_encoder (Model) ~~Callable~~ |
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| `with_spans` | The piece encoder to segment input tokens. ~~Callable~~ |
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| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
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| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
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| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
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| `hidden_dropout_prob` | embedding layers. ~~float~~ |
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| `hidden_width` | Width of the final representations. ~~int~~ |
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| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
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| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
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| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
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| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
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| `model_max_length` | Maximum length of model inputs. ~~int~~ |
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| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
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| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
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| `padding_idx` | Index of the padding meta-token. ~~int~~ |
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| `type_vocab_size` | Type vocabulary size. ~~int~~ |
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| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
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### spacy-curated-transformers.XlmrTransformer.v1
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Construct a XLM-RoBERTa transformer model.
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| Name | Description |
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|--------------------------------|-----------------------------------------------------------------------------|
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| `vocab_size` | Vocabulary size. ~~int~~ |
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| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
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| `with_spans` | piece_encoder (Model) ~~Callable~~ |
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| `with_spans` | The piece encoder to segment input tokens. ~~Callable~~ |
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| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
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| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
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| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
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| `hidden_dropout_prob` | embedding layers. ~~float~~ |
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| `hidden_width` | Width of the final representations. ~~int~~ |
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| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
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| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
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| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
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| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
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| `model_max_length` | Maximum length of model inputs. ~~int~~ |
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| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
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| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
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| `padding_idx` | Index of the padding meta-token. ~~int~~ |
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| `type_vocab_size` | Type vocabulary size. ~~int~~ |
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| `torchscript` | Set to `True` when loading TorchScript models, `False` otherwise. ~~bool~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
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### spacy-curated-transformers.ScalarWeight.v1
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Construct a model that accepts a list of transformer layer outputs and returns a weighted representation of the same.
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| Name | Description |
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|----------------------|-------------------------------------------------------------------------------|
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| `num_layers` | Number of transformer hidden layers. ~~int~~ |
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| `dropout_prob` | Dropout probability. ~~float~~ |
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| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
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| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
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| **CREATES** | The model using the architecture ~~Model[ScalarWeightInT, ScalarWeightOutT]~~ |
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### spacy-curated-transformers.TransformerLayersListener.v1
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Construct a listener layer that communicates with one or more upstream Transformer
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components. This layer extracts the output of the last transformer layer and performs
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pooling over the individual pieces of each Doc token, returning their corresponding
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representations. The upstream name should either be the wildcard string '*', or the name of the Transformer component.
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In almost all cases, the wildcard string will suffice as there'll only be one
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upstream Transformer component. But in certain situations, e.g: you have disjoint
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datasets for certain tasks, or you'd like to use a pre-trained pipeline but a
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downstream task requires its own token representations, you could end up with
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more than one Transformer component in the pipeline.
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| Name | Description |
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|-----------------|------------------------------------------------------------------------------------------------------------------------|
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| `layers` | The the number of layers produced by the upstream transformer component, excluding the embedding layer. ~~int~~ |
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| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ |
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| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ |
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| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ |
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| `grad_factor` | Factor to multiply gradients with. ~~float~~ |
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| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy-curated-transformers.LastTransformerLayerListener.v1
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Construct a listener layer that communicates with one or more upstream Transformer
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components. This layer extracts the output of the last transformer layer and performs
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pooling over the individual pieces of each Doc token, returning their corresponding
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representations. The upstream name should either be the wildcard string '*', or the name of the Transformer component.
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In almost all cases, the wildcard string will suffice as there'll only be one
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upstream Transformer component. But in certain situations, e.g: you have disjoint
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datasets for certain tasks, or you'd like to use a pre-trained pipeline but a
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downstream task requires its own token representations, you could end up with
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more than one Transformer component in the pipeline.
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| Name | Description |
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|-----------------|------------------------------------------------------------------------------------------------------------------------|
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| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ |
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| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ |
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| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ |
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| `grad_factor` | Factor to multiply gradients with. ~~float~~ |
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| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy-curated-transformers.ScalarWeightingListener.v1
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Construct a listener layer that communicates with one or more upstream Transformer
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components. This layer calculates a weighted representation of all transformer layer
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outputs and performs pooling over the individual pieces of each Doc token, returning
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their corresponding representations.
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Requires its upstream Transformer components to return all layer outputs from
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their models. The upstream name should either be the wildcard string '*', or the name of the Transformer component.
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In almost all cases, the wildcard string will suffice as there'll only be one
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upstream Transformer component. But in certain situations, e.g: you have disjoint
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datasets for certain tasks, or you'd like to use a pre-trained pipeline but a
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downstream task requires its own token representations, you could end up with
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more than one Transformer component in the pipeline.
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| Name | Description |
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|-----------------|------------------------------------------------------------------------------------------------------------------------|
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| `width` | The width of the vectors produced by the upstream transformer component. ~~int~~ |
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| `weighting` | Model that is used to perform the weighting of the different layer outputs. ~~Model~~ |
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| `pooling` | Model that is used to perform pooling over the piece representations. ~~Model~~ |
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| `upstream_name` | A string to identify the 'upstream' Transformer component to communicate with. ~~str~~ |
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| `grad_factor` | Factor to multiply gradients with. ~~float~~ |
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| **CREATES** | A model that returns the relevant vectors from an upstream transformer component. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy-curated-transformers.BertWordpieceEncoder.v1
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Construct a WordPiece piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers. This encoder also splits each token
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on punctuation characters, as expected by most BERT models.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.ByteBpeEncoder.v1
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Construct a Byte-BPE piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.CamembertSentencepieceEncoder.v1
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Construct a SentencePiece piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers with CamemBERT post-processing applied.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.CharEncoder.v1
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Construct a character piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.SentencepieceEncoder.v1
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Construct a SentencePiece piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers with CamemBERT post-processing applied.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.WordpieceEncoder.v1
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Construct a WordPiece piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers. This encoder also splits each token
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on punctuation characters, as expected by most BERT models.
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This model must be separately initialized using an appropriate
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loader.
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### spacy-curated-transformers.XlmrSentencepieceEncoder.v1
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Construct a SentencePiece piece encoder model that accepts a list
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of token sequences or documents and returns a corresponding list
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of piece identifiers with XLM-RoBERTa post-processing applied.
|
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This model must be separately initialized using an appropriate
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loader.
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## Pretraining architectures {id="pretrain",source="spacy/ml/models/multi_task.py"}
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The spacy `pretrain` command lets you initialize a `Tok2Vec` layer in your
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@ -519,7 +831,7 @@ objective for a Tok2Vec layer. To use this objective, make sure that the
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vectors.
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| Name | Description |
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| --------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
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|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `maxout_pieces` | The number of maxout pieces to use. Recommended values are `2` or `3`. ~~int~~ |
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| `hidden_size` | Size of the hidden layer of the model. ~~int~~ |
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| `loss` | The loss function can be either "cosine" or "L2". We typically recommend to use "cosine". ~~~str~~ |
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|
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@ -1018,6 +1018,54 @@ $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
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| **PRINTS** | Debugging information. |
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### debug pieces {id="debug-pieces",version="3.6",tag="command"}
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Analyze word- or sentencepiece stats.
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```bash
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$ python -m spacy debug pieces [config_path] [code_path] [transformer_name]
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```
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| Name | Description |
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|--------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| `config_path` | Path to config file. ~~Union[Path, str] (positional)~~ |
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| `code_path` | Path to Python file with additional code (registered functions) to be imported. ~~Union[Path, str] (option)~~ |
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| `transformer_name` | Name of the transformer pipe to gather piece statistics for (default: first transformer pipe). ~~str (option)~~ |
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| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
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| **PRINTS** | Debugging information. |
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||||
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||||
|
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<Accordion title="Example outputs" spaced>
|
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|
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```bash
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$ python -m spacy debug pieces ./config.cfg
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```
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```
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========================= Training corpus statistics =========================
|
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Median token length: 1.0
|
||||
Mean token length: 1.54
|
||||
Token length range: [1, 13]
|
||||
|
||||
======================= Development corpus statistics =======================
|
||||
Median token length: 1.0
|
||||
Mean token length: 1.44
|
||||
Token length range: [1, 8]
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
## quantize {id="quantize",tag="command",version="3.6"}
|
||||
|
||||
Quantize a curated transformers model to reduce its size.
|
||||
|
||||
| Name | Description |
|
||||
|----------------|---------------------------------------------------------------------|
|
||||
| `model_path` | Model to quantize. ~~Path (positional)~~ |
|
||||
| `output_path` | Output directory to store quantized model in. ~~Path (positional)~~ |
|
||||
| `max_mse_loss` | Maximum MSE loss of quantized parameters. ~~float (option)~~ |
|
||||
| `skip_embeds` | Do not quantize embeddings. ~~bool (option)~~ |
|
||||
| `skip_linear` | Do not quantize linear layers. ~~bool (option)~~ |
|
||||
|
||||
## train {id="train",tag="command"}
|
||||
|
||||
Train a pipeline. Expects data in spaCy's
|
||||
|
|
|
|||
|
|
@ -483,7 +483,6 @@ Construct a callback that initializes a character piece encoder model.
|
|||
| `eos_piece` | Piece used as a end-of-sentence token. Defaults to `"[EOS]"`. ~~str~~ |
|
||||
| `unk_piece` | Piece used as a stand-in for unknown tokens. Defaults to `"[UNK]"`. ~~str~~ |
|
||||
| `normalize` | Unicode normalization form to use. Defaults to `"NFKC"`. ~~str~~ |
|
||||
| `vocab` | The shared vocabulary to use. ~~Optional[Vocab]~~ |
|
||||
|
||||
|
||||
### HFPieceEncoderLoader.v1 {id="hf_pieceencoder_loader",tag="registered_function"}
|
||||
|
|
@ -531,3 +530,17 @@ Construct a callback that initializes a supported transformer model with weights
|
|||
|--------|------------------------------------------|
|
||||
| `path` | Path to the PyTorch checkpoint. ~~Path~~ |
|
||||
|
||||
## Callbacks
|
||||
|
||||
### gradual_transformer_unfreezing.v1 {id="gradual_transformer_unfreezing",tag="registered_function"}
|
||||
|
||||
Construct a callback that can be used to gradually unfreeze the
|
||||
weights of one or more Transformer components during training. This
|
||||
can be used to prevent catastrophic forgetting during fine-tuning.
|
||||
|
||||
|
||||
| Name | Description |
|
||||
|----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| `target_pipes` | A dictionary whose keys and values correspond to the names of Transformer components and the training step at which they should be unfrozen respectively. ~~Dict[str, int]~~ |
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user