Token Classification
Transformers
PyTorch
English
roberta
keyphrase-extraction
Eval Results (legacy)
Instructions to use ml6team/keyphrase-extraction-kbir-inspec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ml6team/keyphrase-extraction-kbir-inspec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ml6team/keyphrase-extraction-kbir-inspec")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ml6team/keyphrase-extraction-kbir-inspec") model = AutoModelForTokenClassification.from_pretrained("ml6team/keyphrase-extraction-kbir-inspec", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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value: 0.588
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name: F1-score
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** Work in progress **
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# 🔑 Keyphrase Extraction model: KBIR-inspec
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Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
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Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
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value: 0.588
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name: F1-score
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---
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# 🔑 Keyphrase Extraction model: KBIR-inspec
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Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it.
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| 32 |
Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
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