Hindi Sentence Bert Nli

🚀 HindSBERT

HindSBERT is a model based on the HindBERT architecture (l3cube - pune/hindi - bert - v2), trained on the NLI dataset. It's part of the project MahaNLP, available at https://github.com/l3cube - pune/MarathiNLP. A multilingual version of this model, supporting major Indic languages and cross - lingual capabilities, can be found here indic - sentence - bert - nli . A better - performing sentence similarity model (a fine - tuned version of this model) is shared at https://huggingface.co/l3cube - pune/hindi - sentence - similarity - sbert.

Model Information

Widget Examples

Example 1

• Source Sentence: "एक आदमी एक रस्सी पर चढ़ रहा है"
• Comparison Sentences:
  • "एक आदमी एक रस्सी पर चढ़ता है"
  • "एक आदमी एक दीवार पर चढ़ रहा है"
  • "एक आदमी बांसुरी बजाता है"

Example 2

• Source Sentence: "कुछ लोग गा रहे हैं"
• Comparison Sentences:
  • "लोगों का एक समूह गाता है"
  • "बिल्ली दूध पी रही है"
  • "दो आदमी लड़ रहे हैं"

Example 3

• Source Sentence: "फेडरर ने 7वां विंबलडन खिताब जीत लिया है"
• Comparison Sentences:
  • "फेडरर अपने करियर में कुल 20 ग्रैंडस्लैम खिताब जीत चुके है "
  • "फेडरर ने सितंबर में अपने निवृत्ति की घोषणा की"
  • "एक आदमी कुछ खाना पकाने का तेल एक बर्तन में डालता है"

Related Papers

• More details on the dataset, models, and baseline results can be found in our paper

@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 Related Models

Monolingual Indic Sentence BERT Models

• [Marathi SBERT](https://huggingface.co/l3cube - pune/marathi - sentence - bert - nli)
• [Hindi SBERT](https://huggingface.co/l3cube - pune/hindi - sentence - bert - nli)
• [Kannada SBERT](https://huggingface.co/l3cube - pune/kannada - sentence - bert - nli)
• [Telugu SBERT](https://huggingface.co/l3cube - pune/telugu - sentence - bert - nli)
• [Malayalam SBERT](https://huggingface.co/l3cube - pune/malayalam - sentence - bert - nli)
• [Tamil SBERT](https://huggingface.co/l3cube - pune/tamil - sentence - bert - nli)
• [Gujarati SBERT](https://huggingface.co/l3cube - pune/gujarati - sentence - bert - nli)
• [Oriya SBERT](https://huggingface.co/l3cube - pune/odia - sentence - bert - nli)
• [Bengali SBERT](https://huggingface.co/l3cube - pune/bengali - sentence - bert - nli)
• [Punjabi SBERT](https://huggingface.co/l3cube - pune/punjabi - sentence - bert - nli)
• [Indic SBERT (multilingual)](https://huggingface.co/l3cube - pune/indic - sentence - bert - nli)

Monolingual Similarity Models

• [Marathi Similarity](https://huggingface.co/l3cube - pune/marathi - sentence - similarity - sbert)
• [Hindi Similarity](https://huggingface.co/l3cube - pune/hindi - sentence - similarity - sbert)
• [Kannada Similarity](https://huggingface.co/l3cube - pune/kannada - sentence - similarity - sbert)
• [Telugu Similarity](https://huggingface.co/l3cube - pune/telugu - sentence - similarity - sbert)
• [Malayalam Similarity](https://huggingface.co/l3cube - pune/malayalam - sentence - similarity - sbert)
• [Tamil Similarity](https://huggingface.co/l3cube - pune/tamil - sentence - similarity - sbert)
• [Gujarati Similarity](https://huggingface.co/l3cube - pune/gujarati - sentence - similarity - sbert)
• [Oriya Similarity](https://huggingface.co/l3cube - pune/odia - sentence - similarity - sbert)
• [Bengali Similarity](https://huggingface.co/l3cube - pune/bengali - sentence - similarity - sbert)
• [Punjabi Similarity](https://huggingface.co/l3cube - pune/punjabi - sentence - similarity - sbert)
• [Indic Similarity (multilingual)](https://huggingface.co/l3cube - pune/indic - sentence - similarity - sbert)

🚀 Quick Start

This is a sentence - transformers model. It maps sentences and paragraphs to a 768 - dimensional dense vector space and can be used for tasks such as clustering or semantic search.

📦 Installation

If you have sentence - transformers installed, using this model is straightforward:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

Using 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)

Using HuggingFace Transformers

Without sentence - transformers, you can use the model as follows: First, pass your input through the transformer model, then apply the appropriate pooling operation on top of the contextualized word embeddings.

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)

📄 License

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