modeling¶
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class
ErnieDualEncoder
(query_model_name_or_path=None, title_model_name_or_path=None, share_parameters=False, output_emb_size=None, dropout=None, reinitialize=False, use_cross_batch=False)[源代码]¶ 基类:
paddle.fluid.dygraph.layers.Layer
This class encapsulates two ErnieEncoder models into one model, so query embedding and title embedding could be obtained using one model. And this class allows two ErnieEncoder models to be trained at the same time.
示例
import paddle from paddlenlp.transformers import ErnieDualEncoder, ErnieTokenizer model = ErnieDualEncoder("rocketqa-zh-dureader-query-encoder", "rocketqa-zh-dureader-para-encoder") tokenizer = ErnieTokenizer.from_pretrained("rocketqa-zh-dureader-query-encoder") inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} # Get query embedding query_embedding = model.get_pooled_embedding(**inputs) # Get title embedding title_embedding = model.get_pooled_embedding(**inputs, is_query=False)
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get_pooled_embedding
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, is_query=True)[源代码]¶ Get the first feature of each sequence for classification
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forward
(query_input_ids, pos_title_input_ids, neg_title_input_ids, is_prediction=False, query_token_type_ids=None, query_position_ids=None, query_attention_mask=None, pos_title_token_type_ids=None, pos_title_position_ids=None, pos_title_attention_mask=None, neg_title_token_type_ids=None, neg_title_position_ids=None, neg_title_attention_mask=None)[源代码]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
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class
ErnieCrossEncoder
(pretrain_model_name_or_path, num_classes=2, reinitialize=False, dropout=None)[源代码]¶ 基类:
paddle.fluid.dygraph.layers.Layer
示例
import paddle from paddlenlp.transformers import ErnieCrossEncoder, ErnieTokenizer model = ErnieCrossEncoder("rocketqa-zh-dureader-cross-encoder") tokenizer = ErnieTokenizer.from_pretrained("rocketqa-zh-dureader-cross-encoder") inputs = tokenizer("你们好", text_pair="你好") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} # Get embedding of text pair. embedding = model.matching(**inputs)
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matching
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None, return_prob_distributation=False)[源代码]¶ Use the pooled_output as the feature for pointwise prediction, eg. RocketQAv1
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matching_v2
(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]¶ Use the cls token embedding as the feature for listwise prediction, eg. RocketQAv2
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class
ErnieEncoder
(config: ErnieConfig, output_emb_size: int | None = None)[源代码]¶ 基类:
paddlenlp.transformers.ernie.modeling.ErniePretrainedModel