README.md

metadata

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 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}
}

This is a sentence-transformers 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 installed:

pip install -U sentence-transformers

Then you can use the model like this:

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, 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


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)