Update README.md

README.md

@@ -3,12 +3,16 @@ This is the IndicBART model. For detailed documentation look here: https://indic
 Usage:
 
 ```
-from transformers import MBartForConditionalGeneration
-from transformers import AlbertTokenizer
+from transformers import MBartForConditionalGeneration, AutoModelForSeq2SeqLM
+from transformers import AlbertTokenizer, AutoTokenizer
 
 tokenizer = AlbertTokenizer.from_pretrained("prajdabre/IndicBARTTokenizer", do_lower_case=False, use_fast=False, keep_accents=True)
 
-model = MBartForConditionalGeneration.from_pretrained("prajdabre/IndicBART")
+# Or use tokenizer = AutoTokenizer.from_pretrained("prajdabre/IndicBARTTokenizer", do_lower_case=False, use_fast=False, keep_accents=True)
+
+model = AutoModelForSeq2SeqLM.from_pretrained("prajdabre/IndicBART")
+
+# Or use model = MBartForConditionalGeneration.from_pretrained("prajdabre/IndicBART")
 
 # First tokenize the input and outputs. The format below is how IndicBART 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("I am a boy <\/s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
@@ -25,6 +29,8 @@ model_outputs.logits
 
 # For generation. Pardon the messiness. Note the decoder_start_token_id.
 
+model.eval() # Det 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=tokenizer.pad_token_id, decoder_start_token_id=tokenizer(["<2en>"], add_special_tokens=False).input_ids[0][0])