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---
language:
- bn
- en
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# Bangla Sentence Transformer
Sentence Transformer is a cutting-edge natural language processing (NLP) model that is capable of encoding and transforming sentences into high-dimensional embeddings. With this technology, we can unlock powerful insights and applications in various fields like text classification, information retrieval, semantic search, and more.
This model is finetuned from ```stsb-xlm-r-multilingual```
It's now available on Hugging Face! 🎉🎉
## Install
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','আপনি কি এখানে কাছাকাছি থাকেন?', 'আশেপাশে কেউ আছেন?']
model = SentenceTransformer('shihab17/bangla-sentence-transformer')
embeddings = model.encode(sentences)
print(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 = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','আপনি কি এখানে কাছাকাছি থাকেন?', 'আশেপাশে কেউ আছেন?']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('shihab17/bangla-sentence-transformer')
model = AutoModel.from_pretrained('shihab17/bangla-sentence-transformer')
# 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)
```
## How to get sentence similarity
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import pytorch_cos_sim
transformer = SentenceTransformer('shihab17/bangla-sentence-transformer')
sentences = ['আমি আপেল খেতে পছন্দ করি। ', 'আমার একটি আপেল মোবাইল আছে।','আপনি কি এখানে কাছাকাছি থাকেন?', 'আশেপাশে কেউ আছেন?']
sentences_embeddings = transformer.encode(sentences)
for i in range(len(sentences)):
for j in range(i, len(sentences)):
sen_1 = sentences[i]
sen_2 = sentences[j]
sim_score = float(pytorch_cos_sim(sentences_embeddings[i], sentences_embeddings[j]))
print(sen_1, '----->', sen_2, sim_score)
```
## Best MSE: 7.57528096437454 |