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@ -492,138 +492,128 @@ how to integrate the architectures into your training config.
Construct an ALBERT transformer model.
| Name | Description |
| ------------------------------ | --------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `embedding_width` | Width of the embedding representations. ~~int~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
| `hidden_dropout_prob` | embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_groups` | Number of layer groups whose constituents share parameters. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
| Name | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `embedding_width` | Width of the embedding representations. ~~int~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_groups` | Number of layer groups whose constituents share parameters. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
### spacy-curated-transformers.BertTransformer.v1
Construct a BERT transformer model.
| Name | Description |
| ------------------------------ | --------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
| `hidden_dropout_prob` | embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
| Name | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
### spacy-curated-transformers.CamembertTransformer.v1
Construct a CamemBERT transformer model.
| Name | Description |
| ------------------------------ | --------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
| `hidden_dropout_prob` | embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
| Name | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
### spacy-curated-transformers.RobertaTransformer.v1
Construct a RoBERTa transformer model.
| Name | Description |
| ------------------------------ | --------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
| `hidden_dropout_prob` | embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
| Name | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
### spacy-curated-transformers.XlmrTransformer.v1
Construct a XLM-RoBERTa transformer model.
| Name | Description |
| ------------------------------ | --------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and ~~float~~ |
| `hidden_dropout_prob` | embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the ~~int~~ |
| `intermediate_width` | point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
| Name | Description |
| ------------------------------ | ---------------------------------------------------------------------------------------- |
| `vocab_size` | Vocabulary size. ~~int~~ |
| `with_spans` | Callback that constructs a span generator model. ~~Callable~~ |
| `piece_encoder` | The piece encoder to segment input tokens. ~~Model~~ |
| `attention_probs_dropout_prob` | Dropout probabilty of the self-attention layers. ~~float~~ |
| `hidden_act` | Activation used by the point-wise feed-forward layers. ~~str~~ |
| `hidden_dropout_prob` | Dropout probabilty of the point-wise feed-forward and embedding layers. ~~float~~ |
| `hidden_width` | Width of the final representations. ~~int~~ |
| `intermediate_width` | Width of the intermediate projection layer in the point-wise feed-forward layer. ~~int~~ |
| `layer_norm_eps` | Epsilon for layer normalization. ~~float~~ |
| `max_position_embeddings` | Maximum length of position embeddings. ~~int~~ |
| `model_max_length` | Maximum length of model inputs. ~~int~~ |
| `num_attention_heads` | Number of self-attention heads. ~~int~~ |
| `num_hidden_layers` | Number of hidden layers. ~~int~~ |
| `padding_idx` | Index of the padding meta-token. ~~int~~ |
| `type_vocab_size` | Type vocabulary size. ~~int~~ |
| `mixed_precision` | Use mixed-precision training. ~~bool~~ |
| `grad_scaler_config` | Configuration passed to the PyTorch gradient scaler. ~~dict~~ |
| **CREATES** | The model using the architecture ~~Model[TransformerInT, TransformerOutT]~~ |
### spacy-curated-transformers.ScalarWeight.v1