Update README.md

README.md

@@ -21,9 +21,9 @@ pad_id = tokenizer._convert_token_to_id_with_added_voc("<pad>")
 # To get lang_id use any of ['<2as>', '<2bn>', '<2en>', '<2gu>', '<2hi>', '<2kn>', '<2ml>', '<2mr>', '<2or>', '<2pa>', '<2ta>', '<2te>']
 
 # First tokenize the input and outputs. The format below is how IndicBART-XXEN was trained so the input should be "Sentence </s> <2xx>" where xx is the language code. Similarly, the output should be "<2yy> Sentence </s>". 
-inp = tokenizer("मैं  एक लड़का हूँ </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[  466,  1981,    80, 25573, 64001, 64004]])
+inp = tokenizer("मैं  एक लड़का हूँ </s> <2hi>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
 
-out = tokenizer("<2en> I am a boy </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[64006,   942,    43, 32720,  8384, 64001]])
+out = tokenizer("<2en> I am a boy </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
 
 model_outputs=model(input_ids=inp, decoder_input_ids=out[:,0:-1], labels=out[:,1:])
 
@@ -37,7 +37,7 @@ model_outputs.logits
 
 model.eval() # Set dropouts to zero
 
-model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2hi>"))
+model_output=model.generate(inp, use_cache=True, num_beams=4, max_length=20, min_length=1, early_stopping=True, pad_token_id=pad_id, bos_token_id=bos_id, eos_token_id=eos_id, decoder_start_token_id=tokenizer._convert_token_to_id_with_added_voc("<2en>"))
 
 
 # Decode to get output strings