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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: cc-by-4.0
language: hi
widget:
- source_sentence: "एक आदमी एक रस्सी पर चढ़ रहा है"
sentences:
- "एक आदमी एक रस्सी पर चढ़ता है"
- "एक आदमी एक दीवार पर चढ़ रहा है"
- "एक आदमी बांसुरी बजाता है"
example_title: "Example 1"
- source_sentence: "कुछ लोग गा रहे हैं"
sentences:
- "लोगों का एक समूह गाता है"
- "बिल्ली दूध पी रही है"
- "दो आदमी लड़ रहे हैं"
example_title: "Example 2"
- source_sentence: "फेडरर ने 7वां विंबलडन खिताब जीत लिया है"
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"
example_title: "Example 3"
---
# HindSBERT
This is a HindBERT model (l3cube-pune/hindi-bert-v2) trained on the NLI dataset. <br>
Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br>
A better sentence similarity model (fine-tuned version of this model) is shared here : https://huggingface.co/l3cube-pune/hindi-sentence-similarity-sbert <br>
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2211.11187)
```
@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}
}
```
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## 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)
```
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