Create README.md

README.md

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+This is the IndicBART model. For detailed documentation look here: https://indicnlp.ai4bharat.org/indic-bart/ and https://github.com/AI4Bharat/indic-bart/
+
+Usage:
+
+from transformers import MBartForConditionalGeneration
+from transformers import AlbertTokenizer
+
+tokenizer = AlbertTokenizer.from_pretrained("prajdabre/IndicBARTTokenizer", do_lower_case=False, use_fast=False, keep_accents=True)
+
+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
+out = tokenizer("<2hi> मैं  एक लड़का हूँ </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:])
+
+# For loss
+model_outputs.loss ## This is not label smoothed.
+
+# For logits
+model_outputs.logits
+
+# For generation. Pardon the messiness. Note the decoder_start_token_id.
+
+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, eos_token_id=tokenizer(["</s>"], add_special_tokens=False).input_ids[0][0], decoder_start_token_id=tokenizer(["<2en>"], add_special_tokens=False).input_ids[0][0], bos_token_id=tokenizer(["<s>"], add_special_tokens=False).input_ids[0][0])
+
+
+# Decode to get output strings
+
+decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(decoded_output) # I am a boy
+
+# What if we mask?
+
+inp = tokenizer("I am [MASK] </s> <2en>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids
+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, eos_token_id=tokenizer(["</s>"], add_special_tokens=False).input_ids[0][0], decoder_start_token_id=tokenizer(["<2en>"], add_special_tokens=False).input_ids[0][0], bos_token_id=tokenizer(["<s>"], add_special_tokens=False).input_ids[0][0])
+decoded_output=tokenizer.decode(model_output[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
+print(decoded_output) # I am happy
+
+
+Notes:
+1. This is compatible with the latest version of transformers but was developed with version 4.3.2 so consider using 4.3.2 if possible.
+2. The tokenizer repo is kept separate from the model repo because unlike mBART-25 and mBART-50 which use a BPE model (MBartTokenizer class) whereas we use the sentencepiece model (AlbertTokenizer class).
+3. Currently, keeping the tokenizer and model files in the same repo complicates things so keeping them separate is a temporary solution. This will be fixed in future versions.
+4. While I have only shown how to let logits and loss and how to generate outputs, you can do pretty much everything the MBartForConditionalGeneration class can do as in https://huggingface.co/docs/transformers/model_doc/mbart#transformers.MBartForConditionalGeneration