pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: cc-by-4.0
language: te
widget:
- source_sentence: ఒక మహిళ ఉల్లిపాయను కత్తిస్తోంది
sentences:
- ఒక స్త్రీ ఉల్లిపాయలు కోస్తోంది
- ఒక స్త్రీ బంగాళాదుంపను తొక్కడం
- ఒక పిల్లి ఇంటి చుట్టూ నడుస్తోంది
example_title: Example 1
- source_sentence: పిల్లల బృందం జంపింగ్ పోటీని నిర్వహిస్తోంది
sentences:
- పిల్లల గుంపు సరదాగా గడుపుతోంది
- పిల్లలు పార్కులో ఆడుకోవడానికి ఇష్టపడతారు
- ముగ్గురు అబ్బాయిలు నడుస్తున్నారు
example_title: Example 2
- source_sentence: మీ రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తుంది
sentences:
- రెండు ప్రశ్నలకు అవుననే సమాధానం వస్తోంది
- మేము మీ అన్ని ప్రశ్నలకు సమాధానమిచ్చాము
- నేను ఈ ప్రశ్నకు సమాధానం ఇస్తాను
example_title: Example 3
TeluguSBERT
This is a TeluguBERT model (l3cube-pune/telugu-bert) trained on the NLI dataset.
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: https://huggingface.co/l3cube-pune/telugu-sentence-similarity-sbert
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 sentence BERT models are listed below:
Marathi SBERT
Hindi SBERT
Kannada SBERT
Telugu SBERT
Malayalam SBERT
Tamil SBERT
Gujarati SBERT
Oriya SBERT
Bengali SBERT
Punjabi SBERT
Indic SBERT (multilingual)
Other Monolingual Indic sentence BERT models are listed below:
Marathi SBERT
Hindi SBERT
Kannada SBERT
Telugu SBERT
Malayalam SBERT
Tamil SBERT
Gujarati SBERT
Oriya SBERT
Bengali SBERT
Punjabi SBERT
Indic SBERT (multilingual)
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then 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)
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)