metadata

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-STS

This is a HindSBERT model ( l3cube-pune/hindi-sentence-bert-nli ) fine-tuned on the STS dataset.
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] (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


#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)