
# IndicSBERT-STS

This is a IndicSBERT model (<a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'>l3cube-pune/indic-sentence-bert-nli</a>) trained on the STS dataset of ten major Indian Languages. <br>
The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
The model also has cross-lingual capabilities. <br>
Released as a part of project MahaNLP: https://github.com/l3cube-pune/MarathiNLP <br>

Generic Indic Sentence BERT model is shared here : <a href='https://huggingface.co/l3cube-pune/indic-sentence-bert-nli'>l3cube-pune/indic-sentence-bert-nli</a> <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}
}
```

<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 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>

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>


## 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


#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)
```

{"language": ["multilingual", "en", "hi", "mr", "kn", "ta", "te", "ml", "gu", "or", "pa", "bn"], "license": "cc-by-4.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "\u0926\u093f\u0935\u093e\u0933\u0940 \u0906\u092a\u0923 \u092e\u094b\u0920\u094d\u092f\u093e \u0909\u0924\u094d\u0938\u093e\u0939\u093e\u0928\u0947 \u0938\u093e\u091c\u0930\u0940 \u0915\u0930\u0924\u094b", "sentences": ["\u0926\u093f\u0935\u093e\u0933\u0940 \u0906\u092a\u0923 \u0906\u0928\u0902\u0926\u093e\u0928\u0947 \u0938\u093e\u091c\u0930\u0940 \u0915\u0930\u0924\u094b", "\u0926\u093f\u0935\u093e\u0933\u0940 \u0939\u093e \u0926\u093f\u0935\u094d\u092f\u093e\u0902\u091a\u093e \u0938\u0923 \u0906\u0939\u0947"], "example_title": "Monolingual- Marathi"}, {"source_sentence": "\u0939\u092e \u0926\u0940\u092a\u093e\u0935\u0932\u0940 \u0909\u0924\u094d\u0938\u093e\u0939 \u0915\u0947 \u0938\u093e\u0925 \u092e\u0928\u093e\u0924\u0947 \u0939\u0948\u0902", "sentences": ["\u0939\u092e \u0926\u0940\u092a\u093e\u0935\u0932\u0940 \u0916\u0941\u0936\u093f\u092f\u094b\u0902 \u0938\u0947 \u092e\u0928\u093e\u0924\u0947 \u0939\u0948\u0902", "\u0926\u093f\u0935\u093e\u0932\u0940 \u0930\u094b\u0936\u0928\u0940 \u0915\u093e \u0924\u094d\u092f\u094b\u0939\u093e\u0930 \u0939\u0948"], "example_title": "Monolingual- Hindi"}, {"source_sentence": "\u0a85\u0aae\u0ac7 \u0a89\u0aa4\u0acd\u0ab8\u0abe\u0ab9\u0aa5\u0ac0 \u0aa6\u0abf\u0ab5\u0abe\u0ab3\u0ac0 \u0a89\u0a9c\u0ab5\u0ac0\u0a8f \u0a9b\u0ac0\u0a8f", "sentences": ["\u0aa6\u0abf\u0ab5\u0abe\u0ab3\u0ac0 \u0a86\u0aaa\u0aa3\u0ac7 \u0a96\u0ac1\u0ab6\u0ac0\u0a93\u0aa5\u0ac0 \u0a89\u0a9c\u0ab5\u0ac0\u0a8f \u0a9b\u0ac0\u0a8f", "\u0aa6\u0abf\u0ab5\u0abe\u0ab3\u0ac0 \u0a8f \u0ab0\u0acb\u0ab6\u0aa8\u0ac0\u0aa8\u0acb \u0aa4\u0ab9\u0ac7\u0ab5\u0abe\u0ab0 \u0a9b\u0ac7"], "example_title": "Monolingual- Gujarati"}, {"source_sentence": "\u0906\u092e\u094d\u0939\u093e\u0932\u093e \u092d\u093e\u0930\u0924\u0940\u092f \u0905\u0938\u0932\u094d\u092f\u093e\u091a\u093e \u0905\u092d\u093f\u092e\u093e\u0928 \u0906\u0939\u0947", "sentences": ["\u0939\u092e\u0947\u0902 \u092d\u093e\u0930\u0924\u0940\u092f \u0939\u094b\u0928\u0947 \u092a\u0930 \u0917\u0930\u094d\u0935 \u0939\u0948", "\u0c2d\u0c3e\u0c30\u0c24\u0c40\u0c2f\u0c41\u0c32\u0c2e\u0c48\u0c28\u0c02\u0c26\u0c41\u0c15\u0c41 \u0c17\u0c30\u0c4d\u0c35\u0c3f\u0c38\u0c4d\u0c24\u0c41\u0c28\u0c4d\u0c28\u0c3e\u0c02", "\u0a85\u0aae\u0aa8\u0ac7 \u0aad\u0abe\u0ab0\u0aa4\u0ac0\u0aaf \u0ab9\u0acb\u0ab5\u0abe\u0aa8\u0acb \u0a97\u0ab0\u0acd\u0ab5 \u0a9b\u0ac7"], "example_title": "Cross-lingual 1"}, {"source_sentence": "\u0a2c\u0a3e\u0a30\u0a3f\u0a38\u0a3c \u0a24\u0a4b\u0a02 \u0a2c\u0a3e\u0a05\u0a26 \u0a2c\u0a17\u0a40\u0a1a\u0a3e \u0a38\u0a41\u0a70\u0a26\u0a30 \u0a26\u0a3f\u0a16\u0a3e\u0a08 \u0a26\u0a3f\u0a70\u0a26\u0a3e \u0a39\u0a48", "sentences": ["\u0d2e\u0d34\u0d2f\u0d4d\u0d15\u0d4d\u0d15\u0d4d \u0d36\u0d47\u0d37\u0d02 \u0d2a\u0d42\u0d28\u0d4d\u0d24\u0d4b\u0d1f\u0d4d\u0d1f\u0d02 \u0d2e\u0d28\u0d4b\u0d39\u0d30\u0d2e\u0d3e\u0d2f\u0d3f \u0d15\u0d3e\u0d23\u0d2a\u0d4d\u0d2a\u0d46\u0d1f\u0d41\u0d28\u0d4d\u0d28\u0d41", "\u0b2c\u0b30\u0b4d\u0b37\u0b3e \u0b2a\u0b30\u0b47 \u0b2c\u0b17\u0b3f\u0b1a\u0b3e \u0b38\u0b41\u0b28\u0b4d\u0b26\u0b30 \u0b26\u0b47\u0b16\u0b3e\u0b2f\u0b3e\u0b0f |", "\u092c\u093e\u0930\u093f\u0936 \u0915\u0947 \u092c\u093e\u0926 \u092c\u0917\u0940\u091a\u093e \u0938\u0941\u0902\u0926\u0930 \u0926\u093f\u0916\u0924\u093e \u0939\u0948"], "example_title": "Cross-lingual 2"}]}
sentence-transformers pytorch bert feature-extraction sentence-similarity transformers multilingual en hi mr kn ta te ml gu or pa bn arxiv:2304.11434 arxiv:2211.11187 license:cc-by-4.0 endpoints_compatible has_space region:us
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #multilingual #en #hi #mr #kn #ta #te #ml #gu #or #pa #bn #arxiv-2304.11434 #arxiv-2211.11187 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 


# IndicSBERT-STS

This is a IndicSBERT model (<a href='URL trained on the STS dataset of ten major Indian Languages. <br>
The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
The model also has cross-lingual capabilities. <br>
Released as a part of project MahaNLP: URL <br>

Generic Indic Sentence BERT model is shared here : <a href='URL <br>
More details on the dataset, models, and baseline results can be found in our [paper] (URL



<a href='URL monolingual Indic SBERT paper </a> <br>
<a href='URL multilingual Indic SBERT paper </a>

Other Monolingual similarity models are listed below: <br>
<a href='URL Marathi Similarity </a> <br>
<a href='URL Hindi Similarity </a> <br>
<a href='URL Kannada Similarity </a> <br>
<a href='URL Telugu Similarity </a> <br>
<a href='URL Malayalam Similarity </a> <br>
<a href='URL Tamil Similarity </a> <br>
<a href='URL Gujarati Similarity </a> <br>
<a href='URL Oriya Similarity </a> <br>
<a href='URL Bengali Similarity </a> <br>
<a href='URL Punjabi Similarity </a> <br>
<a href='URL Indic Similarity (multilingual)</a> <br>

Other Monolingual Indic sentence BERT models are listed below: <br>
<a href='URL Marathi SBERT</a> <br>
<a href='URL Hindi SBERT</a> <br>
<a href='URL Kannada SBERT</a> <br>
<a href='URL Telugu SBERT</a> <br>
<a href='URL Malayalam SBERT</a> <br>
<a href='URL Tamil SBERT</a> <br>
<a href='URL Gujarati SBERT</a> <br>
<a href='URL Oriya SBERT</a> <br>
<a href='URL Bengali SBERT</a> <br>
<a href='URL Punjabi SBERT</a> <br>
<a href='URL Indic SBERT (multilingual)</a> <br>


## Usage (Sentence-Transformers)

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



Then you can use the model like this:





## 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.



# IndicSBERT-STS

This is a IndicSBERT model (<a href='URL trained on the STS dataset of ten major Indian Languages. <br>
The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
The model also has cross-lingual capabilities. <br>
Released as a part of project MahaNLP: URL <br>

Generic Indic Sentence BERT model is shared here : <a href='URL <br>
More details on the dataset, models, and baseline results can be found in our [paper] (URL



<a href='URL monolingual Indic SBERT paper </a> <br>
<a href='URL multilingual Indic SBERT paper </a>

Other Monolingual similarity models are listed below: <br>
<a href='URL Marathi Similarity </a> <br>
<a href='URL Hindi Similarity </a> <br>
<a href='URL Kannada Similarity </a> <br>
<a href='URL Telugu Similarity </a> <br>
<a href='URL Malayalam Similarity </a> <br>
<a href='URL Tamil Similarity </a> <br>
<a href='URL Gujarati Similarity </a> <br>
<a href='URL Oriya Similarity </a> <br>
<a href='URL Bengali Similarity </a> <br>
<a href='URL Punjabi Similarity </a> <br>
<a href='URL Indic Similarity (multilingual)</a> <br>

Other Monolingual Indic sentence BERT models are listed below: <br>
<a href='URL Marathi SBERT</a> <br>
<a href='URL Hindi SBERT</a> <br>
<a href='URL Kannada SBERT</a> <br>
<a href='URL Telugu SBERT</a> <br>
<a href='URL Malayalam SBERT</a> <br>
<a href='URL Tamil SBERT</a> <br>
<a href='URL Gujarati SBERT</a> <br>
<a href='URL Oriya SBERT</a> <br>
<a href='URL Bengali SBERT</a> <br>
<a href='URL Punjabi SBERT</a> <br>
<a href='URL Indic SBERT (multilingual)</a> <br> ## Usage (Sentence-Transformers)

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



Then you can use the model like this: ## 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.
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #multilingual #en #hi #mr #kn #ta #te #ml #gu #or #pa #bn #arxiv-2304.11434 #arxiv-2211.11187 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 
 # IndicSBERT-STS

This is a IndicSBERT model (<a href='URL trained on the STS dataset of ten major Indian Languages. <br>
The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
The model also has cross-lingual capabilities. <br>
Released as a part of project MahaNLP: URL <br>

Generic Indic Sentence BERT model is shared here : <a href='URL <br>
More details on the dataset, models, and baseline results can be found in our [paper] (URL



<a href='URL monolingual Indic SBERT paper </a> <br>
<a href='URL multilingual Indic SBERT paper </a>

Other Monolingual similarity models are listed below: <br>
<a href='URL Marathi Similarity </a> <br>
<a href='URL Hindi Similarity </a> <br>
<a href='URL Kannada Similarity </a> <br>
<a href='URL Telugu Similarity </a> <br>
<a href='URL Malayalam Similarity </a> <br>
<a href='URL Tamil Similarity </a> <br>
<a href='URL Gujarati Similarity </a> <br>
<a href='URL Oriya Similarity </a> <br>
<a href='URL Bengali Similarity </a> <br>
<a href='URL Punjabi Similarity </a> <br>
<a href='URL Indic Similarity (multilingual)</a> <br>

Other Monolingual Indic sentence BERT models are listed below: <br>
<a href='URL Marathi SBERT</a> <br>
<a href='URL Hindi SBERT</a> <br>
<a href='URL Kannada SBERT</a> <br>
<a href='URL Telugu SBERT</a> <br>
<a href='URL Malayalam SBERT</a> <br>
<a href='URL Tamil SBERT</a> <br>
<a href='URL Gujarati SBERT</a> <br>
<a href='URL Oriya SBERT</a> <br>
<a href='URL Bengali SBERT</a> <br>
<a href='URL Punjabi SBERT</a> <br>
<a href='URL Indic SBERT (multilingual)</a> <br> ## Usage (Sentence-Transformers)

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



Then you can use the model like this: ## 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.
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #multilingual #en #hi #mr #kn #ta #te #ml #gu #or #pa #bn #arxiv-2304.11434 #arxiv-2211.11187 #license-cc-by-4.0 #endpoints_compatible #has_space #region-us 
# IndicSBERT-STS

This is a IndicSBERT model (<a href='URL trained on the STS dataset of ten major Indian Languages. <br>
The single model works for English, Hindi, Marathi, Kannada, Tamil, Telugu, Gujarati, Oriya, Punjabi, Malayalam, and Bengali.
The model also has cross-lingual capabilities. <br>
Released as a part of project MahaNLP: URL <br>

Generic Indic Sentence BERT model is shared here : <a href='URL <br>
More details on the dataset, models, and baseline results can be found in our [paper] (URL



<a href='URL monolingual Indic SBERT paper </a> <br>
<a href='URL multilingual Indic SBERT paper </a>

Other Monolingual similarity models are listed below: <br>
<a href='URL Marathi Similarity </a> <br>
<a href='URL Hindi Similarity </a> <br>
<a href='URL Kannada Similarity </a> <br>
<a href='URL Telugu Similarity </a> <br>
<a href='URL Malayalam Similarity </a> <br>
<a href='URL Tamil Similarity </a> <br>
<a href='URL Gujarati Similarity </a> <br>
<a href='URL Oriya Similarity </a> <br>
<a href='URL Bengali Similarity </a> <br>
<a href='URL Punjabi Similarity </a> <br>
<a href='URL Indic Similarity (multilingual)</a> <br>

Other Monolingual Indic sentence BERT models are listed below: <br>
<a href='URL Marathi SBERT</a> <br>
<a href='URL Hindi SBERT</a> <br>
<a href='URL Kannada SBERT</a> <br>
<a href='URL Telugu SBERT</a> <br>
<a href='URL Malayalam SBERT</a> <br>
<a href='URL Tamil SBERT</a> <br>
<a href='URL Gujarati SBERT</a> <br>
<a href='URL Oriya SBERT</a> <br>
<a href='URL Bengali SBERT</a> <br>
<a href='URL Punjabi SBERT</a> <br>
<a href='URL Indic SBERT (multilingual)</a> <br>## Usage (Sentence-Transformers)

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



Then you can use the model like this:## 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.