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Update README.md

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@@ -80,18 +80,18 @@ def post_process_re_output(re_output, re_input, ner_output):
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  return template
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  >>> input = "Hugging face is a French company in New york."
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- >>> output = ner_pip(input)
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- >>> re_input = process_ner_output(output, input)
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  >>> re_output = []
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  >>> for idx in range(len(re_input)):
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- >>> tmp_re_output = re_pip(re_input[idx]["re_input"])
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  >>> re_output.append(tmp_re_output)
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- >>> re_ner_output = post_process_re_output(re_output)
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  >>> print("Sentence: ",re_ner_output["input"])
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  >>> print("Entity: ", re_ner_output["entity"])
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  >>> print("Relation: ", re_ner_output["relation"])
 
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  return template
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  >>> input = "Hugging face is a French company in New york."
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+ >>> output = ner_pip(input) # inference NER tags
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+ >>> re_input = process_ner_output(output, input) # prepare a pair of entity and predict relation type
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  >>> re_output = []
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  >>> for idx in range(len(re_input)):
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+ >>> tmp_re_output = re_pip(re_input[idx]["re_input"]) # for each pair of entity, predict relation
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  >>> re_output.append(tmp_re_output)
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+ >>> re_ner_output = post_process_re_output(re_output) # post process NER and relation predictions
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  >>> print("Sentence: ",re_ner_output["input"])
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  >>> print("Entity: ", re_ner_output["entity"])
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  >>> print("Relation: ", re_ner_output["relation"])