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	correctly interpret output of NER model
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					 1 changed files with 4 additions and 3 deletions
				
			
		|  | @ -220,6 +220,9 @@ def ner(model_id=None): | |||
| 
 | ||||
|         for pos, (token, word_pred) in enumerate(zip(tokens, word_predictions)): | ||||
| 
 | ||||
|             if word_pred == '[SEP]': | ||||
|                 word_pred = 'O' | ||||
| 
 | ||||
|             if not token.startswith('##') and token != '[UNK]': | ||||
|                 if len(word) > 0: | ||||
|                     output_sentence.append({'word': word, 'prediction': last_prediction}) | ||||
|  | @ -244,9 +247,6 @@ def ner(model_id=None): | |||
|             if not token.startswith('##') and word_pred == 'X': | ||||
|                 word_pred = 'O' | ||||
| 
 | ||||
|             if word_pred == '[SEP]': | ||||
|                 word_pred = 'O' | ||||
| 
 | ||||
|             token = token[2:] if token.startswith('##') else token | ||||
| 
 | ||||
|             word += token | ||||
|  | @ -267,6 +267,7 @@ def ner(model_id=None): | |||
|             logger.warning('Input and output different!!! \n\n\nInput: {}\n\nOutput: {}\n'. | ||||
|                            format("".join(input_sentence).replace(" ", ""), | ||||
|                                   "".join([pred['word'] for pred in output_sentence]))) | ||||
|     torch.cuda.empty_cache() | ||||
| 
 | ||||
|     return jsonify(output) | ||||
| 
 | ||||
|  |  | |||
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