---
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
This is a BengaliBERT model (l3cube-pune/bengali-bert) trained on the NLI 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 capabilities is shared here indic-sentence-bert-nli
A better sentence similarity model (fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/bengali-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 Indic sentence BERT 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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```