Marathi Sentence Similarity Sbert

🚀 MahaSBERT-STS

A MahaSBERT model (l3cube-pune/marathi-sentence-bert-nli) fine-tuned on STS dataset. This model maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for tasks like clustering or semantic search.

🚀 Quick Start

This is a sentence-transformers model. It can be used for tasks such as clustering or semantic search by mapping sentences and paragraphs into a 768-dimensional dense vector space.

📦 Installation

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

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

If you have sentence-transformers installed, you can use the model like this:

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

Without sentence-transformers, you can use the model as follows: First, pass your input through the transformer model, and then apply the appropriate 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)

📚 Documentation

This model is a fine - tuned version of the MahaSBERT model (l3cube - pune/marathi - sentence - bert - nli) on the STS dataset. It is released as part of the 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 indic - sentence - similarity - sbert .

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

@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)

📄 License

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

Widget Examples

The following are some widget examples for testing sentence similarity:

{
    "pipeline_tag": "sentence-similarity",
    "tags": [
        "sentence-transformers",
        "feature-extraction",
        "sentence-similarity",
        "transformers"
    ],
    "license": "cc-by-4.0",
    "language": "mr",
    "widget": [
        {
            "source_sentence": "शेतकऱ्यांचे डोळे आकाशाकडे लागले आहेत",
            "sentences": [
                "आता शेतकऱ्यांचे डोळे आभाळाकडे लागले आहेत",
                "अन्नधान्य उत्पादनासाठी शेतकरी कष्ट करतात",
                "शहरात कचऱ्याचे ढीग दिसतात"
            ],
            "example_title": "Example 1"
        },
        {
            "source_sentence": "घटनेची माहिती मिळताच पोलिसांचा ताफा तेथे पोहोचला",
            "sentences": [
                "पोलिसांना घटनेची माहिती मिळताच त्यांचे पथक घटनास्थळी पोहोचले",
                "तेव्हा पोलिसांनी त्यांच्या तक्रारीची दखल घेतली नाही",
                "दिवसाचा उत्तरार्ध कुटुंबासोबत मौजमजेत घालवाल"
            ],
            "example_title": "Example 2"
        },
        {
            "source_sentence": "पहिल्या पाच किलोमीटर अंतरासाठी पाच रुपये दर आकारण्यात येत आहे",
            "sentences": [
                "पाच रुपयांत पाच किमी प्रवास करा",
                "दोन ठिकाणांमधले मोठे अंतर प्रवास करणे कंटाळवाणे आहे",
                "नुकत्याच झालेल्या पावसामुळे हिरवळ दिसत आहे"
            ],
            "example_title": "Example 3"
        }
    ]
}