Malayalam Sentence Bert Nli

🚀 MalayalamSBERT

MalayalamSBERT is a MalayalamBERT model (l3cube-pune/malayalam-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 available here indic-sentence-bert-nli . A better sentence similarity model (fine - tuned version of this model) can be found at: https://huggingface.co/l3cube-pune/malayalam-sentence-similarity-sbert.

Metadata

Widget Examples

• Example 1
  • Source Sentence: "കുട്ടികൾ പാർക്കിൽ കളിക്കാൻ ഇഷ്ടപ്പെടുന്നു"
  • Comparison Sentences:
    • "କେବଳ ପିଲାମାନଙ୍କୁ ପାର୍କରେ ଖେଳିବାକୁ ଅନୁମତି ଦିଆଯାଇଛି"
    • "പാർക്കിൽ കുട്ടികൾക്ക് മാത്രമേ കളിക്കാൻ അനുവാദമുള്ളൂ"
    • "കുട്ടികൾ പന്തുമായി കളിക്കാൻ ഇഷ്ടപ്പെടുന്നു"
• Example 2
  • Source Sentence: "പെയിന്റിംഗ് എന്റെ ഹോബിയാണ് "
  • Comparison Sentences:
    • "നൃത്തം എന്റെ ഹോബിയാണ്"
    • "എനിക്ക് ധാരാളം ഹോബികൾ ഉണ്ട് "
    • "പെയിന്റിംഗും നൃത്തവും ഞാൻ ആസ്വദിക്കുന്നു"
• Example 3
  • Source Sentence: "2 മണിക്കൂറിനുള്ളിൽ നിങ്ങൾക്ക് നഗരത്തിലേക്ക് പോകാം"
  • Comparison Sentences:
    • "2 മണിക്കൂർ കൊണ്ട് നഗരത്തിലെത്താം"
    • "യാത്രാ ദൈർഘ്യം 2 മണിക്കൂർ മാത്രം"
    • "പുതിയ സ്ഥലങ്ങളിലേക്കുള്ള യാത്ര എനിക്ക് ഇഷ്ടമാണ്"

Related Papers

More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2304.11434).

@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 Models

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)

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)

🚀 Quick Start

📦 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)

📄 License

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