pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: cc-by-4.0
language: kn
widget:
- source_sentence: ನಮ್ಮ ಪರಿಸರದ ಬಗ್ಗೆ ನಾವು ಕಾಳಜಿ ವಹಿಸಬೇಕು
sentences:
- ನಮ್ಮ ಪರಿಸರವನ್ನು ಸ್ವಚ್ಛವಾಗಿಟ್ಟುಕೊಳ್ಳೋಣ
- ಜಾಗತಿಕ ತಾಪಮಾನವು ಗಂಭೀರ ಸಮಸ್ಯೆಯಾಗಿದೆ
- ಹೆಚ್ಚು ಮರಗಳನ್ನು ನೆಡಿ
example_title: Example 1
- source_sentence: ಕೆಲವರು ಹಾಡುತ್ತಿದ್ದಾರೆ
sentences:
- ಜನರ ಗುಂಪು ಹಾಡುತ್ತಿದೆ
- ಬೆಕ್ಕು ಹಾಲು ಕುಡಿಯುತ್ತಿದೆ
- ಇಬ್ಬರು ಪುರುಷರು ಜಗಳವಾಡುತ್ತಿದ್ದಾರೆ
example_title: Example 2
- source_sentence: ಫೆಡರರ್ ವಿಂಬಲ್ಡನ್ ಪ್ರಶಸ್ತಿ ಗೆದ್ದಿದ್ದಾರೆ
sentences:
- >-
ಫೆಡರರ್ ತಮ್ಮ ವೃತ್ತಿಜೀವನದಲ್ಲಿ ಒಟ್ಟು 20 ಗ್ರ್ಯಾನ್ ಸ್ಲಾಮ್ ಪ್ರಶಸ್ತಿಗಳನ್ನು
ಗೆದ್ದಿದ್ದಾರೆ
- ಫೆಡರರ್ ಸೆಪ್ಟೆಂಬರ್ನಲ್ಲಿ ನಿವೃತ್ತಿ ಘೋಷಿಸಿದರು
- ಒಬ್ಬ ಮನುಷ್ಯ ಒಂದು ಪಾತ್ರೆಯಲ್ಲಿ ಸ್ವಲ್ಪ ಅಡುಗೆ ಎಣ್ಣೆಯನ್ನು ಸುರಿಯುತ್ತಾನೆ
example_title: Example 3
KannadaSBERT-STS
This is a KannadaSBERT model (l3cube-pune/kannada-sentence-bert-nli) fine-tuned on the STS 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 sentence similarity is shared here indic-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}
}
Other Monolingual similarity models are listed below:
Marathi
Hindi
Kannada
Telugu
Malayalam
Tamil
Gujarati
Oriya
Bengali
Punjabi
monolingual paper
multilingual paper
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
#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)