flair.embeddings.transformer

flair.embeddings.transformer.pad_sequence_embeddings(all_hidden_states)View on GitHub

Return type:

Tensor

flair.embeddings.transformer.truncate_hidden_states(hidden_states, input_ids)View on GitHub

Return type:

Tensor

flair.embeddings.transformer.combine_strided_tensors(hidden_states, overflow_to_sample_mapping, half_stride, max_length, default_value)View on GitHub

Return type:

Tensor

flair.embeddings.transformer.fill_masked_elements(all_token_embeddings, sentence_hidden_states, mask, word_ids, lengths)View on GitHub

flair.embeddings.transformer.insert_missing_embeddings(token_embeddings, word_id, length)View on GitHub

Return type:

Tensor

flair.embeddings.transformer.fill_mean_token_embeddings(all_token_embeddings, sentence_hidden_states, word_ids, token_lengths)View on GitHub

flair.embeddings.transformer.document_mean_pooling(sentence_hidden_states, sentence_lengths)View on GitHub

flair.embeddings.transformer.document_max_pooling(sentence_hidden_states, sentence_lengths)View on GitHub

flair.embeddings.transformer.remove_special_markup(text)View on GitHub

class flair.embeddings.transformer.TransformerBaseEmbeddings(name, tokenizer, embedding_length, context_length, context_dropout, respect_document_boundaries, stride, allow_long_sentences, fine_tune, truncate, use_lang_emb, cls_pooling, is_document_embedding=False, is_token_embedding=False, force_device=None, force_max_length=False, feature_extractor=None, needs_manual_ocr=None, use_context_separator=True)View on GitHub

Bases: Embeddings[Sentence]

Base class for all TransformerEmbeddings.

This base class handles the tokenizer and the input preparation, however it won’t implement the actual model. This can be further extended to implement the model in either a pytorch, jit or onnx way of working.

to_args()View on GitHub

classmethod from_params(params)View on GitHub

to_params()View on GitHub

classmethod create_from_state(**state)View on GitHub

property embedding_length: int

Returns the length of the embedding vector.

property embedding_type: str

prepare_tensors(sentences, device=None)View on GitHub

class flair.embeddings.transformer.TransformerOnnxEmbeddings(onnx_model, providers=[], session_options=None, **kwargs)View on GitHub

Bases: TransformerBaseEmbeddings

to_params()View on GitHub

classmethod from_params(params)View on GitHub

Return type:

TransformerOnnxEmbeddings

create_session()View on GitHub

remove_session()View on GitHub

optimize_model(optimize_model_path, use_external_data_format=False, **kwargs)View on GitHub

Wrapper for onnxruntime.transformers.optimizer.optimize_model.

quantize_model(quantize_model_path, use_external_data_format=False, **kwargs)View on GitHub

classmethod collect_dynamic_axes(embedding, tensors)View on GitHub

classmethod export_from_embedding(path, embedding, example_sentences, opset_version=14, providers=None, session_options=None)View on GitHub

embeddings_name: str = 'TransformerOnnxEmbeddings'

class flair.embeddings.transformer.TransformerJitEmbeddings(jit_model, param_names, **kwargs)View on GitHub

Bases: TransformerBaseEmbeddings

to_params()View on GitHub

classmethod from_params(params)View on GitHub

Return type:

Embeddings

classmethod create_from_embedding(module, embedding, param_names)View on GitHub

classmethod parameter_to_list(embedding, wrapper, sentences)View on GitHub

Return type:

tuple[list[str], list[Tensor]]

embeddings_name: str = 'TransformerJitEmbeddings'

class flair.embeddings.transformer.TransformerJitWordEmbeddings(**kwargs)View on GitHub

Bases: TokenEmbeddings, TransformerJitEmbeddings

embeddings_name: str = 'TransformerJitWordEmbeddings'

class flair.embeddings.transformer.TransformerJitDocumentEmbeddings(**kwargs)View on GitHub

Bases: DocumentEmbeddings, TransformerJitEmbeddings

embeddings_name: str = 'TransformerJitDocumentEmbeddings'

class flair.embeddings.transformer.TransformerOnnxWordEmbeddings(**kwargs)View on GitHub

Bases: TokenEmbeddings, TransformerOnnxEmbeddings

embeddings_name: str = 'TransformerOnnxWordEmbeddings'

class flair.embeddings.transformer.TransformerOnnxDocumentEmbeddings(**kwargs)View on GitHub

Bases: DocumentEmbeddings, TransformerOnnxEmbeddings

embeddings_name: str = 'TransformerOnnxDocumentEmbeddings'

class flair.embeddings.transformer.TransformerEmbeddings(model='bert-base-uncased', fine_tune=True, layers='-1', layer_mean=True, subtoken_pooling='first', cls_pooling='cls', is_token_embedding=True, is_document_embedding=True, allow_long_sentences=False, use_context=False, respect_document_boundaries=True, context_dropout=0.5, saved_config=None, tokenizer_data=None, feature_extractor_data=None, name=None, force_max_length=False, needs_manual_ocr=None, use_context_separator=True, transformers_tokenizer_kwargs={}, transformers_config_kwargs={}, transformers_model_kwargs={}, peft_config=None, peft_gradient_checkpointing_kwargs={}, **kwargs)View on GitHub

Bases: TransformerBaseEmbeddings

onnx_clsView on GitHub

alias of TransformerOnnxEmbeddings

__init__(model='bert-base-uncased', fine_tune=True, layers='-1', layer_mean=True, subtoken_pooling='first', cls_pooling='cls', is_token_embedding=True, is_document_embedding=True, allow_long_sentences=False, use_context=False, respect_document_boundaries=True, context_dropout=0.5, saved_config=None, tokenizer_data=None, feature_extractor_data=None, name=None, force_max_length=False, needs_manual_ocr=None, use_context_separator=True, transformers_tokenizer_kwargs={}, transformers_config_kwargs={}, transformers_model_kwargs={}, peft_config=None, peft_gradient_checkpointing_kwargs={}, **kwargs)View on GitHub

Instantiate transformers embeddings.

Allows using transformers as TokenEmbeddings and DocumentEmbeddings or both.

Parameters:

• model (str) – name of transformer model (see huggingface hub for options)

• fine_tune (bool) – If True, the weights of the transformers embedding will be updated during training.

• layers (str) – Specify which layers should be extracted for the embeddings. Expects either “all” to extract all layers or a comma separated list of indices (e.g. “-1,-2,-3,-4” for the last 4 layers)

• layer_mean (bool) – If True, the extracted layers will be averaged. Otherwise, they will be concatenated.

• subtoken_pooling (Literal['first', 'last', 'first_last', 'mean']) – Specify how multiple sub-tokens will be aggregated for a token-embedding.

• cls_pooling (Literal['cls', 'max', 'mean']) – Specify how the document-embeddings will be extracted.

• is_token_embedding (bool) – If True, this embeddings can be handled as token-embeddings.

• is_document_embedding (bool) – If True, this embeddings can be handled document-embeddings.

• allow_long_sentences (bool) – If True, too long sentences will be patched and strided and afterwards combined.

• use_context (Union[bool, int]) – If True, predicting multiple sentences at once, will use the previous and next sentences for context.

• respect_document_boundaries (bool) – If True, the context calculation will stop if a sentence represents a context boundary.

• context_dropout (float) – Integer percentage (0-100) to specify how often the context won’t be used during training.

• saved_config (Optional[PretrainedConfig]) – Pretrained config used when loading embeddings. Always use None.

• tokenizer_data (Optional[BytesIO]) – Tokenizer data used when loading embeddings. Always use None.

• feature_extractor_data (Optional[BytesIO]) – Feature extractor data used when loading embeddings. Always use None.

• name (Optional[str]) – The name for the embeddings. Per default the name will be used from the used transformers model.

• force_max_length (bool) – If True, the tokenizer will always pad the sequences to maximum length.

• needs_manual_ocr (Optional[bool]) – If True, bounding boxes will be calculated manually. This is used for models like layoutlm where the tokenizer doesn’t compute the bounding boxes itself.

• use_context_separator (bool) – If True, the embedding will hold an additional token to allow the model to distingulish between context and prediction.

• transformers_tokenizer_kwargs (dict[str, Any]) – Further values forwarded to the initialization of the transformers tokenizer

• transformers_config_kwargs (dict[str, Any]) – Further values forwarded to the initialization of the transformers config

• transformers_model_kwargs (dict[str, Any]) – Further values forwarded to the initialization of the transformers model

• peft_config – If set, the model will be trained using adapters and optionally QLoRA. Should be of type “PeftConfig” or subtype

• peft_gradient_checkpointing_kwargs (Optional[dict[str, Any]]) – Further values used when preparing the model for kbit training. Only used if peft_config is set.

• **kwargs – Further values forwarded to the transformers config

embeddings_name: str = 'TransformerEmbeddings'

property embedding_length: int

Returns the length of the embedding vector.

property embedding_type: str

classmethod from_params(params)View on GitHub

to_params()View on GitHub

forward(input_ids, sub_token_lengths=None, token_lengths=None, attention_mask=None, overflow_to_sample_mapping=None, word_ids=None, langs=None, bbox=None, pixel_values=None)View on GitHub

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

export_onnx(path, example_sentences, **kwargs)View on GitHub

Export TransformerEmbeddings to OnnxFormat.

Parameters:

• path (Union[str, Path]) – the path to save the embeddings. Notice that the embeddings are stored as external file, hence it matters if the path is an absolue path or a relative one.

• example_sentences (list[Sentence]) – a list of sentences that will be used for tracing. It is recommended to take 2-4 sentences with some variation.

• **kwargs – the parameters passed to TransformerOnnxEmbeddings.export_from_embedding()

Return type:

TransformerOnnxEmbeddings