---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: cc-by-4.0
language: mr
widget:
- source_sentence: "शेतकऱ्यांचे डोळे आकाशाकडे लागले आहेत"
sentences:
- "आता शेतकऱ्यांचे डोळे आभाळाकडे लागले आहेत"
- "अन्नधान्य उत्पादनासाठी शेतकरी कष्ट करतात"
- "शहरात कचऱ्याचे ढीग दिसतात"
example_title: "Example 1"
- source_sentence: "घटनेची माहिती मिळताच पोलिसांचा ताफा तेथे पोहोचला"
sentences:
- "पोलिसांना घटनेची माहिती मिळताच त्यांचे पथक घटनास्थळी पोहोचले"
- "तेव्हा पोलिसांनी त्यांच्या तक्रारीची दखल घेतली नाही"
- "दिवसाचा उत्तरार्ध कुटुंबासोबत मौजमजेत घालवाल"
example_title: "Example 2"
- source_sentence: "पहिल्या पाच किलोमीटर अंतरासाठी पाच रुपये दर आकारण्यात येत आहे"
sentences:
- "पाच रुपयांत पाच किमी प्रवास करा"
- "दोन ठिकाणांमधले मोठे अंतर प्रवास करणे कंटाळवाणे आहे"
- "नुकत्याच झालेल्या पावसामुळे हिरवळ दिसत आहे"
example_title: "Example 3"
---
# MahaSBERT
A MahaBERT model (l3cube-pune/marathi-bert-v2) trained on the NLI dataset.
This 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 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/marathi-sentence-similarity-sbert
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}
}
```
Other Monolingual Indic sentence BERT models are listed below:
Marathi
Hindi
Kannada
Telugu
Malayalam
Tamil
Gujarati
Oriya
Bengali
Punjabi
monolingual paper
multilingual paper
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.
## 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)
```