Create README.md

README.md

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+# This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small
+
+The present model supports 6 languages -
+1) English
+2) Hindi
+3) German
+4) Korean
+5) Japanese
+6) Portuguese
+
+Here is how to use this model
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/mt5-multilingual-sentiment")
+tokenizer = AutoTokenizer.from_pretrained("Chirayu/mt5-multilingual-sentiment")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+def get_subject(text, num_beams=2,max_length=512, repetition_penalty=2.5, length_penalty=1, early_stopping=True,top_p=.95, top_k=50, num_return_sequences=1):
+  
+  input_ids = tokenizer.encode(
+    text, return_tensors="pt", add_special_tokens=True
+  )
+  
+  input_ids = input_ids.to(device)
+  generated_ids = model.generate(
+      input_ids=input_ids,
+     
+      num_beams=num_beams,
+      max_length=max_length,
+      repetition_penalty=repetition_penalty,
+      length_penalty=length_penalty,
+      early_stopping=early_stopping,
+      top_p=top_p,
+      top_k=top_k,
+      num_return_sequences=num_return_sequences,
+  )
+  sentiment = [tokenizer.decode(generated_id,skip_special_tokens=True,clean_up_tokenization_spaces=True,) for generated_id in generated_ids]
+  return sentiment
+```