| 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, AutoModelForSeq2SeqLM | |
| from transformers import AlbertTokenizer, AutoTokenizer | |
| tokenizer = AlbertTokenizer.from_pretrained("prajdabre/IndicBARTTokenizer", do_lower_case=False, use_fast=False, keep_accents=True) | |
| # Or use tokenizer = AutoTokenizer.from_pretrained("prajdabre/IndicBARTTokenizer", do_lower_case=False, use_fast=False, keep_accents=True) | |
| model = MBartForConditionalGeneration.from_pretrained("prajdabre/IndicBART") | |
| # Or use model = AutoModelForSeq2SeqLM.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.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]) | |
| # 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, decoder_start_token_id=tokenizer(["<2en>"], 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 |