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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- transformers |
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license: cc-by-4.0 |
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language: te |
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widget: |
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- source_sentence: "ఒక మహిళ ఉల్లిపాయను కత్తిస్తోంది" |
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sentences: |
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- "ఒక స్త్రీ ఉల్లిపాయలు కోస్తోంది" |
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- "ఒక స్త్రీ బంగాళాదుంపను తొక్కడం" |
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- "ఒక పిల్లి ఇంటి చుట్టూ నడుస్తోంది" |
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example_title: "Example 1" |
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- source_sentence: "పిల్లల బృందం జంపింగ్ పోటీని నిర్వహిస్తోంది" |
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sentences: |
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- "పిల్లల గుంపు సరదాగా గడుపుతోంది" |
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- "పిల్లలు పార్కులో ఆడుకోవడానికి ఇష్టపడతారు" |
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- "ముగ్గురు అబ్బాయిలు నడుస్తున్నారు" |
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example_title: "Example 2" |
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- source_sentence: "మీ రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తుంది" |
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sentences: |
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- "రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తోంది" |
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- "మేము మీ అన్ని ప్రశ్నలకు సమాధానమిచ్చాము" |
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- "నేను ఈ ప్రశ్నకు సమాధానం ఇస్తాను" |
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example_title: "Example 3" |
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--- |
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# TeluguSBERT |
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This is a TeluguBERT model (l3cube-pune/telugu-bert) trained on the NLI dataset. <br> |
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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 capabilities is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> indic-sentence-bert-nli </a> <br> |
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A better sentence similarity model (fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/telugu-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/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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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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<a href='https://arxiv.org/abs/2211.11187'> monolingual Indic SBERT paper </a> <br> |
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<a href='https://arxiv.org/abs/2304.11434'> multilingual Indic SBERT paper </a> |
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Other Monolingual Indic sentence BERT models are listed below: <br> |
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<a href='https://huggingface.co/l3cube-pune/marathi-sentence-bert-nli'> Marathi SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/hindi-sentence-bert-nli'> Hindi SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/kannada-sentence-bert-nli'> Kannada SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/telugu-sentence-bert-nli'> Telugu SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-bert-nli'> Malayalam SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/tamil-sentence-bert-nli'> Tamil SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-bert-nli'> Gujarati SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/odia-sentence-bert-nli'> Oriya SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/bengali-sentence-bert-nli'> Bengali SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-bert-nli'> Punjabi SBERT</a> <br> |
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<a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> Indic SBERT (multilingual)</a> <br> |
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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 Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/hindi-sentence-similarity-sbert'> Hindi Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/kannada-sentence-similarity-sbert'> Kannada Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/telugu-sentence-similarity-sbert'> Telugu Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-similarity-sbert'> Malayalam Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/tamil-sentence-similarity-sbert'> Tamil Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-similarity-sbert'> Gujarati Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/odia-sentence-similarity-sbert'> Oriya Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/bengali-sentence-similarity-sbert'> Bengali Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-similarity-sbert'> Punjabi Similarity </a> <br> |
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<a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> Indic Similarity (multilingual)</a> <br> |
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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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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('{MODEL_NAME}') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Usage (HuggingFace Transformers) |
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Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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def cls_pooling(model_output, attention_mask): |
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return model_output[0][:,0] |
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# Sentences we want sentence embeddings for |
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sentences = ['This is an example sentence', 'Each sentence is converted'] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') |
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model = AutoModel.from_pretrained('{MODEL_NAME}') |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, cls pooling. |
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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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