
# `s-xlmr-bn`

This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like **clustering** or **semantic search**.

<!--- Describe your model here -->

## Model Details

- Model name: s-xlmr-bn
- Model version: 1.0
- Architecture: Sentence Transformer
- Language: Multilingual ( fine-tuned for Bengali Language)
- Base Models: 
    - [paraphrase-distilroberta-base-v2](https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v2) [Teacher Model]
    - [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) [Student Model]

## Training

The model was fine-tuned using  **Multilingual Knowledge Distillation** method. We took `paraphrase-distilroberta-base-v2` as the teacher model and  `xlm-roberta-large` as the student model.



![image](https://i.ibb.co/8Xrgnfr/sentence-transformer-model.png)

## Intended Use:

- **Primary Use Case:** Semantic similarity, clustering, and semantic searches
- **Potential Use Cases:** Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system

## Usage

### Using 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 = ["I sing in bengali", "আমি বাংলায় গান গাই"]

model = SentenceTransformer('afschowdhury/s-xlmr-bn')
embeddings = model.encode(sentences)
print(embeddings)
```

### Using 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 = ["I sing in bengali", "আমি বাংলায় গান গাই"]

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('afschowdhury/s-xlmr-bn')
model = AutoModel.from_pretrained('afschowdhury/s-xlmr-bn')

# 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)
```

## Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```


### Point of Contact
**Asif Faisal Chowdhury**  
E-mail: [afschowdhury@gmail.com](mailto:afschowdhury@gmail.com) | Linked-in: [afschowdhury](https://www.linkedin.com/in/afschowdhury)  
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "dpr", "bn", "multilingual"], "pipeline_tag": "sentence-similarity", "widget": [{"source_sentence": "\u0986\u09ae\u09bf \u09ac\u09be\u0982\u09b2\u09be\u09df \u0997\u09be\u09a8 \u0997\u09be\u0987", "sentences": ["I sing in Bangla", "I sing in Bengali", "I sing in English", "\u0986\u09ae\u09bf \u0997\u09be\u09a8 \u0997\u09be\u0987 \u09a8\u09be "], "example_title": "Singing"}]}
sentence-transformers pytorch xlm-roberta feature-extraction sentence-similarity transformers dpr bn multilingual endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #dpr #bn #multilingual #endpoints_compatible #region-us 


# 's-xlmr-bn'

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.



## Model Details

- Model name: s-xlmr-bn
- Model version: 1.0
- Architecture: Sentence Transformer
- Language: Multilingual ( fine-tuned for Bengali Language)
- Base Models: 
    - paraphrase-distilroberta-base-v2 [Teacher Model]
    - xlm-roberta-large [Student Model]

## Training

The model was fine-tuned using  Multilingual Knowledge Distillation method. We took 'paraphrase-distilroberta-base-v2' as the teacher model and  'xlm-roberta-large' as the student model.



!image

## Intended Use:

- Primary Use Case: Semantic similarity, clustering, and semantic searches
- Potential Use Cases: Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system

## Usage

### Using Sentence-Transformers

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



Then you can use the model like this:



### Using 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.



## Full Model Architecture




### Point of Contact
Asif Faisal Chowdhury  
E-mail: afschowdhury@URL | Linked-in: afschowdhury  
# 's-xlmr-bn'

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Model Details

- Model name: s-xlmr-bn
- Model version: 1.0
- Architecture: Sentence Transformer
- Language: Multilingual ( fine-tuned for Bengali Language)
- Base Models: 
    - paraphrase-distilroberta-base-v2 [Teacher Model]
    - xlm-roberta-large [Student Model] ## Training

The model was fine-tuned using  Multilingual Knowledge Distillation method. We took 'paraphrase-distilroberta-base-v2' as the teacher model and  'xlm-roberta-large' as the student model.



!image ## Intended Use:

- Primary Use Case: Semantic similarity, clustering, and semantic searches
- Potential Use Cases: Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system ## Usage ### Using Sentence-Transformers

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



Then you can use the model like this: ### Using 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. ## Full Model Architecture ### Point of Contact
Asif Faisal Chowdhury  
E-mail: afschowdhury@URL | Linked-in: afschowdhury
TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #dpr #bn #multilingual #endpoints_compatible #region-us 
 # 's-xlmr-bn'

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Model Details

- Model name: s-xlmr-bn
- Model version: 1.0
- Architecture: Sentence Transformer
- Language: Multilingual ( fine-tuned for Bengali Language)
- Base Models: 
    - paraphrase-distilroberta-base-v2 [Teacher Model]
    - xlm-roberta-large [Student Model] ## Training

The model was fine-tuned using  Multilingual Knowledge Distillation method. We took 'paraphrase-distilroberta-base-v2' as the teacher model and  'xlm-roberta-large' as the student model.



!image ## Intended Use:

- Primary Use Case: Semantic similarity, clustering, and semantic searches
- Potential Use Cases: Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system ## Usage ### Using Sentence-Transformers

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



Then you can use the model like this: ### Using 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. ## Full Model Architecture ### Point of Contact
Asif Faisal Chowdhury  
E-mail: afschowdhury@URL | Linked-in: afschowdhury
TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #dpr #bn #multilingual #endpoints_compatible #region-us 
# 's-xlmr-bn'

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Model Details

- Model name: s-xlmr-bn
- Model version: 1.0
- Architecture: Sentence Transformer
- Language: Multilingual ( fine-tuned for Bengali Language)
- Base Models: 
    - paraphrase-distilroberta-base-v2 [Teacher Model]
    - xlm-roberta-large [Student Model]## Training

The model was fine-tuned using  Multilingual Knowledge Distillation method. We took 'paraphrase-distilroberta-base-v2' as the teacher model and  'xlm-roberta-large' as the student model.



!image## Intended Use:

- Primary Use Case: Semantic similarity, clustering, and semantic searches
- Potential Use Cases: Document retrieval, information retrieval, recommendation systems, chatbot systems , FAQ system## Usage### Using Sentence-Transformers

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



Then you can use the model like this:### Using 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.## Full Model Architecture### Point of Contact
Asif Faisal Chowdhury  
E-mail: afschowdhury@URL | Linked-in: afschowdhury