Create README.md

README.md

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+---
+language:
+- bn
+- en
+pipeline_tag: translation
+---
+# 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 finetune from ```stsb-xlm-r-multilingual``` 
+ it's now available on Hugging Face! 🎉🎉
+
+## Install
+
+```
+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