Bengali Sentence Similarity Sbert

🚀 BengaliSBERT-STS

This is a BengaliSBERT model (l3cube-pune/bengali-sentence-bert-nli) fine - tuned on the STS dataset. It is 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.

🚀 Quick Start

Model Information

Widget Examples

Example 1

• Source Sentence: "লোকটি কুড়াল দিয়ে একটি গাছ কেটে ফেলল"
• Comparison Sentences:
  • "একজন লোক কুড়াল দিয়ে একটি গাছের নিচে চপ করে"
  • "একজন লোক গিটার বাজছে"
  • "একজন মহিলা ঘোড়ায় চড়ে"

Example 2

• Source Sentence: "একটি গোলাপী সাইকেল একটি বিল্ডিংয়ের সামনে রয়েছে"
• Comparison Sentences:
  • "কিছু ধ্বংসাবশেষের সামনে একটি সাইকেল"
  • "গোলাপী দুটি ছোট মেয়ে নাচছে"
  • "ভেড়া গাছের লাইনের সামনে মাঠে চারণ করছে"

Example 3

• Source Sentence: "আলোর গতি সসীম হওয়ার গতি আমাদের মহাবিশ্বের অন্যতম মৌলিক"
• Comparison Sentences:
  • "আলোর গতি কত?"
  • "আলোর গতি সসীম"
  • "আলো মহাবিশ্বের দ্রুততম জিনিস"

✨ Features

• Fine - tuned on the STS dataset for better sentence similarity performance.
• Part of the MahaNLP project, contributing to the NLP research in Indic languages.
• A multilingual version is available for cross - lingual sentence similarity tasks.

📦 Installation

Using this model becomes easy when you have sentence - transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (Sentence - Transformers)

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)

Advanced Usage (HuggingFace Transformers)

Without sentence - transformers, 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.

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)

📚 Documentation

Related Papers

• BibTeX Citations

@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}
}

• Paper Links
  • monolingual Indic SBERT paper
  • multilingual Indic SBERT paper

Other Related Models

Monolingual Similarity Models

• Marathi Similarity
• Hindi Similarity
• Kannada Similarity
• Telugu Similarity
• Malayalam Similarity
• Tamil Similarity
• Gujarati Similarity
• Oriya Similarity
• Bengali Similarity
• Punjabi Similarity
• Indic Similarity (multilingual)

Monolingual Indic Sentence BERT Models

• Marathi SBERT
• Hindi SBERT
• Kannada SBERT
• Telugu SBERT
• Malayalam SBERT
• Tamil SBERT
• Gujarati SBERT
• Oriya SBERT
• Bengali SBERT
• Punjabi SBERT
• Indic SBERT (multilingual)

📄 License

This model is released under the cc - by - 4.0 license.