Telugu Sentence Similarity Sbert

🚀 TeluguSBERT-STS

This is a TeluguSBERT model (l3cube-pune/telugu-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.

📄 License

The model is released under the CC BY 4.0 license.

📚 Documentation

BibTeX Citations

@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}
}

Related Papers

• Monolingual Indic SBERT paper
• Multilingual Indic SBERT paper

Other 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)

Other 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)

🚀 Quick Start

📦 Installation

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

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage with 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 with 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)

📋 Model Details

🎛️ Widget Examples

The model comes with some widget examples to demonstrate its usage:

• Example 1:
  • Source Sentence: "ఒక మహిళ ఉల్లిపాయను కత్తిస్తోంది"
  • Comparison Sentences:
    • "ఒక స్త్రీ ఉల్లిపాయలు కోస్తోంది"
    • "ఒక స్త్రీ బంగాళాదుంపను తొక్కడం"
    • "ఒక పిల్లి ఇంటి చుట్టూ నడుస్తోంది"
• Example 2:
  • Source Sentence: "పిల్లల బృందం జంపింగ్ పోటీని నిర్వహిస్తోంది"
  • Comparison Sentences:
    • "పిల్లల గుంపు సరదాగా గడుపుతోంది"
    • "పిల్లలు పార్కులో ఆడుకోవడానికి ఇష్టపడతారు"
    • "ముగ్గురు అబ్బాయిలు నడుస్తున్నారు"
• Example 3:
  • Source Sentence: "మీ రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తుంది"
  • Comparison Sentences:
    • "రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తోంది"
    • "మేము మీ అన్ని ప్రశ్నలకు సమాధానమిచ్చాము"
    • "నేను ఈ ప్రశ్నకు సమాధానం ఇస్తాను"