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Parent(s):
c32e389
Update README.md
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README.md
CHANGED
@@ -87,7 +87,7 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
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def postprocess(self, model_outputs):
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results = super().postprocess(
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model_outputs=model_outputs,
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-
aggregation_strategy=AggregationStrategy.
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)
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return np.unique([result.get("word").strip() for result in results])
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@@ -118,7 +118,8 @@ the semantic meaning of a text even better than these classical methods.
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Classical methods look at the frequency, occurrence and order of words
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in the text, whereas these neural approaches can capture long-term
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semantic dependencies and context of words in a text.
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""".replace("
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keyphrases = extractor(text)
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def postprocess(self, model_outputs):
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results = super().postprocess(
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model_outputs=model_outputs,
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aggregation_strategy=AggregationStrategy.SIMPLE,
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)
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return np.unique([result.get("word").strip() for result in results])
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Classical methods look at the frequency, occurrence and order of words
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in the text, whereas these neural approaches can capture long-term
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semantic dependencies and context of words in a text.
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""".replace("
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", " ")
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keyphrases = extractor(text)
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