Punjabi Sentence Bert Nli

🚀 PunjabiSBERT

PunjabiSBERT is a model designed for sentence similarity tasks. It is based on a PunjabiBERT model (l3cube - pune/punjabi - bert) trained on the NLI dataset. This model is part of the project MahaNLP: [https://github.com/l3cube - pune/MarathiNLP](https://github.com/l3cube - pune/MarathiNLP). There is also a multilingual version supporting major Indic languages and cross - lingual capabilities available at [indic - sentence - bert - nli](https://huggingface.co/l3cube - pune/indic - sentence - bert - nli). Additionally, a better fine - tuned sentence similarity model can be found at [https://huggingface.co/l3cube - pune/punjabi - sentence - similarity - sbert](https://huggingface.co/l3cube - pune/punjabi - sentence - similarity - sbert).

Model Information

Widget Examples

• Example 1
  • Source Sentence: "ਪੇਂਟਿੰਗ ਮੇਰਾ ਸ਼ੌਕ ਹੈ"
  • Comparison Sentences:
    • "ਨੱਚਣਾ ਮੇਰਾ ਸ਼ੌਕ ਹੈ"
    • "ਮੇਰੇ ਬਹੁਤ ਸਾਰੇ ਸ਼ੌਕ ਹਨ"
    • "ਮੈਨੂੰ ਪੇਂਟਿੰਗ ਅਤੇ ਡਾਂਸ ਦੋਵਾਂ ਦਾ ਆਨੰਦ ਆਉਂਦਾ ਹੈ"
• Example 2
  • Source Sentence: "ਕੁਝ ਲੋਕ ਗਾ ਰਹੇ ਹਨ"
  • Comparison Sentences:
    • "ਲੋਕਾਂ ਦਾ ਇੱਕ ਸਮੂਹ ਗਾ ਰਿਹਾ ਹੈ"
    • "ਇੱਕ ਬਿੱਲੀ ਦੁੱਧ ਪੀ ਰਹੀ ਹੈ"
    • "ਦੋ ਆਦਮੀ ਲੜ ਰਹੇ ਹਨ"
• Example 3
  • Source Sentence: "ਮੇਰੇ ਘਰ ਵਿੱਚ ਤੁਹਾਡਾ ਸੁਆਗਤ ਹੈ"
  • Comparison Sentences:
    • "ਮੈਂ ਤੁਹਾਡੇ ਘਰ ਵਿੱਚ ਤੁਹਾਡਾ ਸੁਆਗਤ ਕਰਾਂਗਾ "
    • "ਮੇਰਾ ਘਰ ਕਾਫੀ ਵੱਡਾ ਹੈ"
    • "ਅੱਜ ਮੇਰੇ ਘਰ ਵਿੱਚ ਰਹੋ"

Related Papers

• You can find more details on the dataset, models, and baseline results in our paper.

  @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}
  }
  

• monolingual Indic SBERT paper
• multilingual Indic SBERT paper

Other Monolingual Indic Sentence BERT Models

• [Marathi SBERT](https://huggingface.co/l3cube - pune/marathi - sentence - bert - nli)
• [Hindi SBERT](https://huggingface.co/l3cube - pune/hindi - sentence - bert - nli)
• [Kannada SBERT](https://huggingface.co/l3cube - pune/kannada - sentence - bert - nli)
• [Telugu SBERT](https://huggingface.co/l3cube - pune/telugu - sentence - bert - nli)
• [Malayalam SBERT](https://huggingface.co/l3cube - pune/malayalam - sentence - bert - nli)
• [Tamil SBERT](https://huggingface.co/l3cube - pune/tamil - sentence - bert - nli)
• [Gujarati SBERT](https://huggingface.co/l3cube - pune/gujarati - sentence - bert - nli)
• [Oriya SBERT](https://huggingface.co/l3cube - pune/odia - sentence - bert - nli)
• [Bengali SBERT](https://huggingface.co/l3cube - pune/bengali - sentence - bert - nli)
• [Punjabi SBERT](https://huggingface.co/l3cube - pune/punjabi - sentence - bert - nli)
• [Indic SBERT (multilingual)](https://huggingface.co/l3cube - pune/indic - sentence - bert - nli)

Other Monolingual Similarity Models

• [Marathi Similarity](https://huggingface.co/l3cube - pune/marathi - sentence - similarity - sbert)
• [Hindi Similarity](https://huggingface.co/l3cube - pune/hindi - sentence - similarity - sbert)
• [Kannada Similarity](https://huggingface.co/l3cube - pune/kannada - sentence - similarity - sbert)
• [Telugu Similarity](https://huggingface.co/l3cube - pune/telugu - sentence - similarity - sbert)
• [Malayalam Similarity](https://huggingface.co/l3cube - pune/malayalam - sentence - similarity - sbert)
• [Tamil Similarity](https://huggingface.co/l3cube - pune/tamil - sentence - similarity - sbert)
• [Gujarati Similarity](https://huggingface.co/l3cube - pune/gujarati - sentence - similarity - sbert)
• [Oriya Similarity](https://huggingface.co/l3cube - pune/odia - sentence - similarity - sbert)
• [Bengali Similarity](https://huggingface.co/l3cube - pune/bengali - sentence - similarity - sbert)
• [Punjabi Similarity](https://huggingface.co/l3cube - pune/punjabi - sentence - similarity - sbert)
• [Indic Similarity (multilingual)](https://huggingface.co/l3cube - pune/indic - sentence - similarity - sbert)

🚀 Quick Start

Prerequisites

You need to install sentence - transformers to use this model easily. You can install it using 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)

If you don't have sentence - transformers installed, you can still use the model. First, pass your input through the transformer model, then 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.