--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: cc-by-4.0 language: bn widget: - source_sentence: "লোকটি কুড়াল দিয়ে একটি গাছ কেটে ফেলল" sentences: - "একজন লোক কুড়াল দিয়ে একটি গাছের নিচে চপ করে" - "একজন লোক গিটার বাজছে" - "একজন মহিলা ঘোড়ায় চড়ে" example_title: "Example 1" - source_sentence: "একটি গোলাপী সাইকেল একটি বিল্ডিংয়ের সামনে রয়েছে" sentences: - "কিছু ধ্বংসাবশেষের সামনে একটি সাইকেল" - "গোলাপী দুটি ছোট মেয়ে নাচছে" - "ভেড়া গাছের লাইনের সামনে মাঠে চারণ করছে" example_title: "Example 2" - source_sentence: "আলোর গতি সসীম হওয়ার গতি আমাদের মহাবিশ্বের অন্যতম মৌলিক" sentences: - "আলোর গতি কত?" - "আলোর গতি সসীম" - "আলো মহাবিশ্বের দ্রুততম জিনিস" example_title: "Example 3" --- # BengaliSBERT-STS This is a BengaliSBERT model (l3cube-pune/bengali-sentence-bert-nli) fine-tuned on the STS dataset.
Released as a part of project MahaNLP : https://github.com/l3cube-pune/MarathiNLP
A multilingual version of this model supporting major Indic languages and cross-lingual sentence similarity is shared here indic-sentence-similarity-sbert
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2304.11434) ``` @article{deode2023l3cube, title={L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT}, author={Deode, Samruddhi and Gadre, Janhavi and Kajale, Aditi and Joshi, Ananya and Joshi, Raviraj}, journal={arXiv preprint arXiv:2304.11434}, year={2023} } ``` ``` @article{joshi2022l3cubemahasbert, title={L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi}, author={Joshi, Ananya and Kajale, Aditi and Gadre, Janhavi and Deode, Samruddhi and Joshi, Raviraj}, journal={arXiv preprint arXiv:2211.11187}, year={2022} } ``` Other Monolingual similarity models are listed below:
Marathi
Hindi
Kannada
Telugu
Malayalam
Tamil
Gujarati
Oriya
Bengali
Punjabi
monolingual paper
multilingual paper ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) 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. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ```