Telugu Sentence Bert Nli

🚀 TeluguSBERT

This is a TeluguSBERT model, which is a TeluguBERT model (l3cube-pune/telugu-bert) trained on the NLI 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 capabilities is shared here indic - sentence - bert - nli.

A better sentence similarity model (fine - tuned version of this model) is shared here: telugu - sentence - similarity - sbert.

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

✨ Features

• Pipeline Tag: Sentence - similarity
• Tags: sentence - transformers, feature - extraction, sentence - similarity, transformers
• License: cc - by - 4.0
• Language: Telugu
• Widgets:
  • Example 1:
    • Source Sentence: "ఒక మహిళ ఉల్లిపాయను కత్తిస్తోంది"
    • Sentences to Compare:
      • "ఒక స్త్రీ ఉల్లిపాయలు కోస్తోంది"
      • "ఒక స్త్రీ బంగాళాదుంపను తొక్కడం"
      • "ఒక పిల్లి ఇంటి చుట్టూ నడుస్తోంది"
  • Example 2:
    • Source Sentence: "పిల్లల బృందం జంపింగ్ పోటీని నిర్వహిస్తోంది"
    • Sentences to Compare:
      • "పిల్లల గుంపు సరదాగా గడుపుతోంది"
      • "పిల్లలు పార్కులో ఆడుకోవడానికి ఇష్టపడతారు"
      • "ముగ్గురు అబ్బాయిలు నడుస్తున్నారు"
  • Example 3:
    • Source Sentence: "మీ రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తుంది"
    • Sentences to Compare:
      • "రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తోంది"
      • "మేము మీ అన్ని ప్రశ్నలకు సమాధానమిచ్చాము"
      • "నేను ఈ ప్రశ్నకు సమాధానం ఇస్తాను"

📦 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


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)

📄 License

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

📚 Documentation

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 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)

• 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)