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@@ -36,8 +36,16 @@ This is a TamilSBERT model (l3cube-pune/tamil-sentence-bert-nli) fine-tuned on t
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  Released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP <br>
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  A multilingual version of this model supporting major Indic languages and cross-lingual sentence similarity is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> indic-sentence-similarity-sbert </a> <br>
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- More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187)
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  ```
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  @article{joshi2022l3cubemahasbert,
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  title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi},
@@ -47,6 +55,20 @@ More details on the dataset, models, and baseline results can be found in our [p
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  }
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  ```
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  ## Usage (Sentence-Transformers)
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
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  Released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP <br>
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  A multilingual version of this model supporting major Indic languages and cross-lingual sentence similarity is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> indic-sentence-similarity-sbert </a> <br>
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+ More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2304.11434)
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+ ```
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+ @article{deode2023l3cube,
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+ title={L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT},
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+ author={Deode, Samruddhi and Gadre, Janhavi and Kajale, Aditi and Joshi, Ananya and Joshi, Raviraj},
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+ journal={arXiv preprint arXiv:2304.11434},
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+ year={2023}
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+ }
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+ ```
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  ```
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  @article{joshi2022l3cubemahasbert,
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  title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi},
 
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  }
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  ```
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+ Other Monolingual similarity models are listed below: <br>
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+ <a href='https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert'> Marathi </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/hindi-sentence-similarity-sbert'> Hindi </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/kannada-sentence-similarity-sbert'> Kannada </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/telugu-sentence-similarity-sbert'> Telugu </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/malayalam-sentence-similarity-sbert'> Malayalam </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/tamil-sentence-similarity-sbert'> Tamil </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/gujarati-sentence-similarity-sbert'> Gujarati </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/odia-sentence-similarity-sbert'> Oriya </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/bengali-sentence-similarity-sbert'> Bengali </a> <br>
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+ <a href='https://huggingface.co/l3cube-pune/punjabi-sentence-similarity-sbert'> Punjabi </a> <br>
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+ <a href='https://arxiv.org/abs/2211.11187'> monolingual paper </a> <br>
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+ <a href='https://arxiv.org/abs/2304.11434'> multilingual paper </a>
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+
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  ## Usage (Sentence-Transformers)
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: