Gujarati Sentence Similarity Sbert

🚀 GujaratiSBERT-STS

This is a GujaratiSBERT model (l3cube-pune/gujarati-sentence-bert-nli) fine - tuned on the STS dataset. It is released as a part of project MahaNLP: MahaNLP GitHub. 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.

Model Information

Widget Examples

The widget provides several examples to demonstrate the sentence similarity functionality:

• Example 1:
  • Source Sentence: "એક માણસ દોરડા પર ચઢી રહ્યો છે"
  • Comparison Sentences:
    • "એક માણસ દોરડા પર ચઢે છે"
    • "એક માણસ દિવાલ પર ચઢી રહ્યો છે"
    • "એક માણસ વાંસળી વગાડી રહ્યો છે"
• Example 2:
  • Source Sentence: "કેટલાક લોકો ગાતા હોય છે"
  • Comparison Sentences:
    • "લોકોનું એક જૂથ ગાય છે"
    • "એક બિલાડી દૂધ પી રહી છે"
    • "બે માણસો લડી રહ્યા છે"
• Example 3:
  • Source Sentence: "હું પહેલીવાર વિમાનમાં બેઠો"
  • Comparison Sentences:
    • "તે મારી પ્રથમ વિમાનની મુસાફરી હતી"
    • "હું પહેલીવાર ટ્રેનમાં બેઠો"
    • "મને નવી જગ્યાઓ પર ફરવાનું પસંદ છે"

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

Related 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)

Related 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)

📦 Installation

Using this model becomes easy when you have sentence - transformers installed. You can install it with the following command:

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 as follows: 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)

📄 License

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