Token Classification
Transformers
PyTorch
English
roberta
keyphrase-extraction
Eval Results (legacy)
Instructions to use ml6team/keyphrase-extraction-kbir-semeval2017 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml6team/keyphrase-extraction-kbir-semeval2017 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ml6team/keyphrase-extraction-kbir-semeval2017")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ml6team/keyphrase-extraction-kbir-semeval2017") model = AutoModelForTokenClassification.from_pretrained("ml6team/keyphrase-extraction-kbir-semeval2017", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): 8a837e5
Update README.md
Browse files
README.md
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@@ -84,9 +84,9 @@ class KeyphraseExtractionPipeline(TokenClassificationPipeline):
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**kwargs
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)
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def postprocess(self,
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results = super().postprocess(
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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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**kwargs
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)
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def postprocess(self, all_outputs):
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results = super().postprocess(
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all_outputs=all_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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