Kannada Sentence Similarity Sbert

🚀 KannadaSBERT-STS

This is a KannadaSBERT model (l3cube-pune/kannada-sentence-bert-nli) fine - tuned on the STS dataset. It 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 sentence similarity is shared here.

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

🚀 Quick Start

✨ Features

• This is a fine - tuned KannadaSBERT model on the STS dataset.
• It is part of the MahaNLP project.
• There is a multilingual version supporting major Indic languages and cross - lingual sentence similarity.

📦 Installation

Using this model becomes easy when you have sentence - transformers installed:

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage (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)

Advanced 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)

📚 Documentation

• Model Details: This is a KannadaSBERT model fine - tuned on the STS dataset.
• Project Link: Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP.
• Multilingual Version: A multilingual version supporting major Indic languages and cross - lingual sentence similarity is available here.
• Paper Reference: More details can be found in our paper.

📄 License

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

BibTeX References

@article{deode2023l3cube,
  title={L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERT},
  author={Deode, Samruddhi and Gadre, Janhavi and Kajale, Aditi and Joshi, Ananya and Joshi, Raviraj},
  journal={arXiv preprint arXiv:2304.11434},
  year={2023}
}

@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 Related Models

• Monolingual Similarity Models:
  • Marathi Similarity
  • Hindi Similarity
  • Kannada Similarity
  • Telugu Similarity
  • Malayalam Similarity
  • Tamil Similarity
  • Gujarati Similarity
  • Oriya Similarity
  • Bengali Similarity
  • Punjabi Similarity
  • Indic Similarity (multilingual)
• Monolingual Indic Sentence BERT Models:
  • Marathi SBERT
  • Hindi SBERT
  • Kannada SBERT
  • Telugu SBERT
  • Malayalam SBERT
  • Tamil SBERT
  • Gujarati SBERT
  • Oriya SBERT
  • Bengali SBERT
  • Punjabi SBERT
  • Indic SBERT (multilingual)

Widget Examples

• Example 1:
  • Source Sentence: "ನಮ್ಮ ಪರಿಸರದ ಬಗ್ಗೆ ನಾವು ಕಾಳಜಿ ವಹಿಸಬೇಕು"
  • Comparison Sentences:
    • "ನಮ್ಮ ಪರಿಸರವನ್ನು ಸ್ವಚ್ಛವಾಗಿಟ್ಟುಕೊಳ್ಳೋಣ"
    • "ಜಾಗತಿಕ ತಾಪಮಾನವು ಗಂಭೀರ ಸಮಸ್ಯೆಯಾಗಿದೆ"
    • "ಹೆಚ್ಚು ಮರಗಳನ್ನು ನೆಡಿ"
• Example 2:
  • Source Sentence: "ಕೆಲವರು ಹಾಡುತ್ತಿದ್ದಾರೆ"
  • Comparison Sentences:
    • "ಜನರ ಗುಂಪು ಹಾಡುತ್ತಿದೆ"
    • "ಬೆಕ್ಕು ಹಾಲು ಕುಡಿಯುತ್ತಿದೆ"
    • "ಇಬ್ಬರು ಪುರುಷರು ಜಗಳವಾಡುತ್ತಿದ್ದಾರೆ"
• Example 3:
  • Source Sentence: "ಫೆಡರರ್ ವಿಂಬಲ್ಡನ್ ಪ್ರಶಸ್ತಿ ಗೆದ್ದಿದ್ದಾರೆ"
  • Comparison Sentences:
    • "ಫೆಡರರ್ ತಮ್ಮ ವೃತ್ತಿಜೀವನದಲ್ಲಿ ಒಟ್ಟು 20 ಗ್ರ್ಯಾನ್ ಸ್ಲಾಮ್ ಪ್ರಶಸ್ತಿಗಳನ್ನು ಗೆದ್ದಿದ್ದಾರೆ "
    • "ಫೆಡರರ್ ಸೆಪ್ಟೆಂಬರ್‌ನಲ್ಲಿ ನಿವೃತ್ತಿ ಘೋಷಿಸಿದರು"
    • "ಒಬ್ಬ ಮನುಷ್ಯ ಒಂದು ಪಾತ್ರೆಯಲ್ಲಿ ಸ್ವಲ್ಪ ಅಡುಗೆ ಎಣ್ಣೆಯನ್ನು ಸುರಿಯುತ್ತಾನೆ"