Instructions to use Ashishkr/paraphrase_diversity_ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ashishkr/paraphrase_diversity_ranker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Ashishkr/paraphrase_diversity_ranker") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (#2)
Browse files- Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (377d66512a6c9463b288c0d0bdbe1649b9cfaec0)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
README.md
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license: apache-2.0
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inference: false
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We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to paraphrase generation is "OverGenerate and Rank" , Its important to have a robust model of scoring/ ranking paraphrases. NLG Metrics like bleu ,BleuRT, gleu , Meteor have not proved very effective in scoring paraphrases.
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- salesken
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license: apache-2.0
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inference: false
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to paraphrase generation is "OverGenerate and Rank" , Its important to have a robust model of scoring/ ranking paraphrases. NLG Metrics like bleu ,BleuRT, gleu , Meteor have not proved very effective in scoring paraphrases.
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