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@@ -17,6 +17,17 @@ This is released as a part of project MahaNLP: https://github.com/l3cube-pune/Ma
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  A better sentence similarity model(fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert <br>
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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  <!--- Describe your model here -->
@@ -74,7 +85,3 @@ sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
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  print("Sentence embeddings:")
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  print(sentence_embeddings)
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  ```
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-
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- ## Citing & Authors
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-
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- It will be updated soon, refer to the project page for now.
 
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  A better sentence similarity model(fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert <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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+ ```
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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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+ author={Joshi, Ananya and Kajale, Aditi and Gadre, Janhavi and Deode, Samruddhi and Joshi, Raviraj},
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+ journal={arXiv preprint arXiv:2211.11187},
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+ year={2022}
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+ }
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+
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+
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  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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  <!--- Describe your model here -->
 
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  print("Sentence embeddings:")
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  print(sentence_embeddings)
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  ```