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---
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
This is a KannadaBERT model (l3cube-pune/kannada-bert) trained on the NLI dataset. <br>
Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br>
A multilingual version of this model supporting major Indic languages and cross-lingual capabilities is shared here <a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> indic-sentence-bert-nli </a> <br>
A better sentence similarity model (fine-tuned version of this model) is shared here: https://huggingface.co/l3cube-pune/kannada-sentence-similarity-sbert <br>
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}
}
```
<a href='https://arxiv.org/abs/2211.11187'> monolingual Indic SBERT paper </a> <br>
<a href='https://arxiv.org/abs/2304.11434'> multilingual Indic SBERT paper </a>
Other Monolingual Indic sentence BERT models are listed below: <br>
<a href='https://huggingface.co/l3cube-pune/marathi-sentence-bert-nli'> Marathi SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/hindi-sentence-bert-nli'> Hindi SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/kannada-sentence-bert-nli'> Kannada SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/telugu-sentence-bert-nli'> Telugu SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-bert-nli'> Malayalam SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/tamil-sentence-bert-nli'> Tamil SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-bert-nli'> Gujarati SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/odia-sentence-bert-nli'> Oriya SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/bengali-sentence-bert-nli'> Bengali SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-bert-nli'> Punjabi SBERT</a> <br>
<a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'> Indic SBERT (multilingual)</a> <br>
Other Monolingual similarity models are listed below: <br>
<a href='https://huggingface.co/l3cube-pune/marathi-sentence-similarity-sbert'> Marathi Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/hindi-sentence-similarity-sbert'> Hindi Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/kannada-sentence-similarity-sbert'> Kannada Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/telugu-sentence-similarity-sbert'> Telugu Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/malayalam-sentence-similarity-sbert'> Malayalam Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/tamil-sentence-similarity-sbert'> Tamil Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/gujarati-sentence-similarity-sbert'> Gujarati Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/odia-sentence-similarity-sbert'> Oriya Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/bengali-sentence-similarity-sbert'> Bengali Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/punjabi-sentence-similarity-sbert'> Punjabi Similarity </a> <br>
<a href='https://huggingface.co/l3cube-pune/indic-sentence-similarity-sbert'> Indic Similarity (multilingual)</a> <br>
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
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](https://www.SBERT.net), 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.
```python
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)
``` |