Update README.md

README.md

@@ -13,4 +13,55 @@ tags:
 - health
 - medical
 - nlp
----
+---
+
+MT5-small is finetuned with large corups of Nepali Health Question-Answering Dataset.
+
+### Training Procedure
+
+The model was trained for 30 epochs with the following training parameters:
+
+- Learning Rate: 2e-4
+- Batch Size: 2
+- Gradient Accumulation Steps: 8
+- FP16 (mixed-precision training): Disabled
+- Optimizer: AdamW with weight decay
+
+The training loss consistently decreased, indicating successful learning.
+
+## Use Case
+
+```python
+
+  !pip install transformers sentencepiece
+
+  from transformers import MT5ForConditionalGeneration, AutoTokenizer 
+  # Load the trained model
+  model = MT5ForConditionalGeneration.from_pretrained("Chhabi/mt5-small-finetuned-Nepali-Health-50k-2")
+  
+  # Load the tokenizer for generating new output
+  tokenizer = AutoTokenizer.from_pretrained("Chhabi/mt5-small-finetuned-Nepali-Health-50k-2",use_fast=True)
+
+
+    
+  query = "म धेरै थकित महसुस गर्छु र मेरो नाक बगिरहेको छ। साथै, मलाई घाँटी दुखेको छ र अलि टाउको दुखेको छ। मलाई के भइरहेको छ?"
+  input_text = f"answer: {query}"
+  inputs = tokenizer(input_text,return_tensors='pt',max_length=256,truncation=True).to("cuda")
+  print(inputs)
+  generated_text = model.generate(**inputs,max_length=512,min_length=256,length_penalty=3.0,num_beams=10,top_p=0.95,top_k=100,do_sample=True,temperature=0.7,num_return_sequences=3,no_repeat_ngram_size=4)
+  print(generated_text)
+  # generated_text
+  generated_response = tokenizer.batch_decode(generated_text,skip_special_tokens=True)[0]
+  tokens = generated_response.split(" ")
+  filtered_tokens = [token for token in tokens if not token.startswith("<extra_id_")]
+  print(' '.join(filtered_tokens))
+
+```
+## Evaluation
+### BLEU score:
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a9a2e403835e13f9786936/X9NK63aj1EKeBH-d_NUG6.png)
+
+
+