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yangheng95 / LCF-ATEPC

codes for paper A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction
MIT License
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def get_ids_for_local_context_extractor #32

Closed Astudnew closed 3 years ago

Astudnew commented 3 years ago

Hi Thanks a lot for sharing this work I have Question, please I do not understand this function("def get_ids_for_local_context_extractor(self, text_indices): ") and how it works?? and why you use ==102 in this line and 5 in get labels function " sep_index = np.argmax((text_ids[text_i] == 102)"

"def get_ids_for_local_context_extractor(self, text_indices): # convert BERT-SPC input to BERT-BASE format text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == 102)) text_ids[text_i][sep_index + 1:] = 0 return torch.tensor(text_ids).to(self.args.device)"

yangheng95 commented 3 years ago

102 is the index of preserved token [SEP], the indices behind the first [SEP] are appended aspect indices and [SEP] indice.