--- 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} } ``` monolingual Indic SBERT paper
multilingual Indic SBERT paper Other Monolingual similarity models are listed below:
Marathi Similarity
Hindi Similarity
Kannada Similarity
Telugu Similarity
Malayalam Similarity
Tamil Similarity
Gujarati Similarity
Oriya Similarity
Bengali Similarity
Punjabi Similarity
Indic Similarity (multilingual)
Other Monolingual Indic sentence BERT models are listed below:
Marathi SBERT
Hindi SBERT
Kannada SBERT
Telugu SBERT
Malayalam SBERT
Tamil SBERT
Gujarati SBERT
Oriya SBERT
Bengali SBERT
Punjabi SBERT
Indic SBERT (multilingual)
## 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) ```