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--- |
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tags: |
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- sentence-transformers |
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- embeddings |
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- multilingual |
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- NLP |
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- Indic-languages |
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- semantic-search |
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- similarity |
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library_name: sentence-transformers |
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license: other |
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license_name: krutrim-community-license-agreement-version-1.0 |
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license_link: LICENSE.md |
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--- |
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# Vyakyarth: A Multilingual Sentence Embedding Model for Indic Languages |
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[](https://huggingface.co/krutrim-ai-labs/vyakyarth) [](https://github.com/ola-krutrim/Vyakyarth) [](https://cloud.olakrutrim.com/console/inference-service?section=models&modelName=Krutrim&artifactName=Vyakyarth&artifactType=model) [](https://ai-labs.olakrutrim.com/models/Vyakyarth-1-Indic-Embedding) |
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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[](https://www.youtube.com/watch?v=N1f8IlZCUi4) |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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from sklearn.metrics.pairwise import cosine_similarity |
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import numpy as np |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("krutrim-ai-labs/vyakyarth") |
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# Run inference |
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sentences = ["मैं अपने दोस्त से मिला", "I met my friend", "I love you" |
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] |
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embeddings = np.array(model.encode(sentences)) |
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print(cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]) |
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# Score : 0.9861017 |
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print(cosine_similarity([embeddings[0]], [embeddings[2]])[0][0]) |
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# Score 0.26329127 |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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### Evaluation/Benchmarking |
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### Dataset Name : Flores Cross Lingual Sentence Retrieval Task of IndicXtreme Benchmark |
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| **Language** | **MuRIL** | **IndicBERT** | **Vyakyarth** | **jina-embeddings-v3** | |
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|--------------|------|----------|----------|----------------| |
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| **Bengali** | 77.0 | 91.0 | **98.7** | 97.4 | |
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| **Gujarati** | 67.0 | 92.4 | **98.7** | 97.3 | |
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| **Hindi** | 84.2 | 90.5 | **99.9** | 98.8 | |
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| **Kannada** | 88.4 | 89.1 | **99.2** | 96.8 | |
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| **Malayalam**| 82.2 | 89.2 | **98.7** | 96.3 | |
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| **Marathi** | 83.9 | 92.5 | **98.8** | 97.1 | |
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| **Sanskrit** | 36.4 | 30.4 | **90.1** | 84.1 | |
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| **Tamil** | 79.4 | 90.0 | **97.9** | 95.8 | |
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| **Telugu** | 43.5 | 88.6 | **97.5** | 97.3 | |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## License |
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This code repository and the model weights are licensed under the [Krutrim Community License.](LICENSE.md) |
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## 7. Citation |
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``` |
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@inproceedings{ |
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author={Pushkar Singh, Sandeep Kumar Pandey, Rajkiran Panuganti}, |
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title={Vyakyarth: A Multilingual Sentence Embedding Model for Indic Languages}, |
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year = {2024}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/ola-krutrim/Vyakyarth}} |
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} |
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``` |
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## Contact |
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Contributions are welcome! If you have any improvements or suggestions, feel free to submit a pull request on GitHub. |
