modeling¶
-
class
ErnieDualEncoder
(query_model_name_or_path, title_model_name_or_path=None, share_parameters=False, dropout=None, use_cross_batch=False)[source]¶ Bases:
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.
Example
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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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)[source]¶ Defines the computation performed at every call. Should be overridden by all subclasses.
- Parameters
*inputs (tuple) – unpacked tuple arguments
**kwargs (dict) – unpacked dict arguments
-
-
class
ErnieCrossEncoder
(pretrain_model_name_or_path, num_classes=2, dropout=None)[source]¶ Bases:
paddle.fluid.dygraph.layers.Layer
Example
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 similarity probability of text pair. embedding = model.matching(**inputs)