111 lines
5.3 KiB
Python
111 lines
5.3 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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from typing import Any, Dict
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import numpy as np
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from modelscope.metainfo import TaskModels
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from modelscope.models.builder import MODELS
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from modelscope.models.nlp.task_models.task_model import EncoderModel
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from modelscope.outputs import FeatureExtractionOutput, OutputKeys
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from modelscope.utils.constant import Tasks
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__all__ = ['ModelForFeatureExtraction']
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@MODELS.register_module(
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Tasks.feature_extraction, module_name=TaskModels.feature_extraction)
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class ModelForFeatureExtraction(EncoderModel):
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task = Tasks.feature_extraction
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# There is no head for feature extraction method
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head_type = None
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def forward(self,
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input_ids=None,
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attention_mask=None,
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token_type_ids=None,
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position_ids=None,
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head_mask=None,
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inputs_embeds=None,
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labels=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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**kwargs):
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r"""
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Args:
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input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary.
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Indices can be obtained using :class:`~modelscope.models.nlp.structbert.SbertTokenizer`. See
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:meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` for
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details.
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attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
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1]``:
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- 0 corresponds to a `sentence A` token,
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- 1 corresponds to a `sentence B` token.
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position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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config.max_position_embeddings - 1]``.
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head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
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Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
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Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
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vectors than the model's internal embedding lookup matrix.
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output_attentions (:obj:`bool`, `optional`):
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Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
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tensors for more detail.
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output_hidden_states (:obj:`bool`, `optional`):
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Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
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more detail.
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return_dict (:obj:`bool`, `optional`):
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Whether or not to return a :class:`~transformers.ModelOutput` instead of a plain tuple.
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labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
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Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
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config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
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If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
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Returns:
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Returns `modelscope.outputs.AttentionTextClassificationModelOutput`
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Examples:
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>>> from modelscope.models import Model
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>>> from modelscope.preprocessors import Preprocessor
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>>> model = Model.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base')
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>>> preprocessor = Preprocessor.from_pretrained('damo/nlp_structbert_sentence-similarity_chinese-base')
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>>> print(model(**preprocessor(('This is a test', 'This is also a test'))))
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"""
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# backbone do not need labels, only head need for loss compute
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feature = self.extract_feature(
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input_ids=input_ids,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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head_mask=head_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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return FeatureExtractionOutput(
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text_embedding=feature.last_hidden_state)
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