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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)): |         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 not token.startswith('##') and token != '[UNK]': | ||||||
|                 if len(word) > 0: |                 if len(word) > 0: | ||||||
|                     output_sentence.append({'word': word, 'prediction': last_prediction}) |                     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': |             if not token.startswith('##') and word_pred == 'X': | ||||||
|                 word_pred = 'O' |                 word_pred = 'O' | ||||||
| 
 | 
 | ||||||
|             if word_pred == '[SEP]': |  | ||||||
|                 word_pred = 'O' |  | ||||||
| 
 |  | ||||||
|             token = token[2:] if token.startswith('##') else token |             token = token[2:] if token.startswith('##') else token | ||||||
| 
 | 
 | ||||||
|             word += token |             word += token | ||||||
|  | @ -267,6 +267,7 @@ def ner(model_id=None): | ||||||
|             logger.warning('Input and output different!!! \n\n\nInput: {}\n\nOutput: {}\n'. |             logger.warning('Input and output different!!! \n\n\nInput: {}\n\nOutput: {}\n'. | ||||||
|                            format("".join(input_sentence).replace(" ", ""), |                            format("".join(input_sentence).replace(" ", ""), | ||||||
|                                   "".join([pred['word'] for pred in output_sentence]))) |                                   "".join([pred['word'] for pred in output_sentence]))) | ||||||
|  |     torch.cuda.empty_cache() | ||||||
| 
 | 
 | ||||||
|     return jsonify(output) |     return jsonify(output) | ||||||
| 
 | 
 | ||||||
|  |  | ||||||
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