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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: bn |
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widget: |
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- source_sentence: "লোকটি কুড়াল দিয়ে একটি গাছ কেটে ফেলল" |
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sentences: |
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- "একজন লোক কুড়াল দিয়ে একটি গাছের নিচে চপ করে" |
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- "একজন লোক গিটার বাজছে" |
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- "একজন মহিলা ঘোড়ায় চড়ে" |
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example_title: "Example 1" |
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- source_sentence: "একটি গোলাপী সাইকেল একটি বিল্ডিংয়ের সামনে রয়েছে" |
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sentences: |
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- "কিছু ধ্বংসাবশেষের সামনে একটি সাইকেল" |
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- "গোলাপী দুটি ছোট মেয়ে নাচছে" |
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- "ভেড়া গাছের লাইনের সামনে মাঠে চারণ করছে" |
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example_title: "Example 2" |
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- source_sentence: "আলোর গতি সসীম হওয়ার গতি আমাদের মহাবিশ্বের অন্যতম মৌলিক" |
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sentences: |
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- "আলোর গতি কত?" |
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- "আলোর গতি সসীম" |
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- "আলো মহাবিশ্বের দ্রুততম জিনিস" |
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example_title: "Example 3" |
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--- |
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# BengaliSBERT-STS |
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This is a BengaliSBERT model (l3cube-pune/bengali-sentence-bert-nli) fine-tuned on the STS 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 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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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 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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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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## 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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#Mean Pooling - Take attention mask into account for correct averaging |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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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, mean pooling. |
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sentence_embeddings = mean_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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