| 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 = AutoTokenizer.from_pretrained("prajdabre/IndicBART", do_lower_case=False, use_fast=False, keep_accents=True) | |
| # Or use tokenizer = AlbertTokenizer.from_pretrained("prajdabre/IndicBART", do_lower_case=False, use_fast=False, keep_accents=True) | |
| model = AutoModelForSeq2SeqLM.from_pretrained("prajdabre/IndicBART") | |
| # Or use model = MBartForConditionalGeneration.from_pretrained("prajdabre/IndicBART") | |
| # Some initial mapping | |
| bos_id = tokenizer._convert_token_to_id_with_added_voc("<s>") | |
| eos_id = tokenizer._convert_token_to_id_with_added_voc("</s>") | |
| 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 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 # tensor([[ 466, 1981, 80, 25573, 64001, 64004]]) | |
| out = tokenizer("<2hi> मैं एक लड़का हूँ </s>", add_special_tokens=False, return_tensors="pt", padding=True).input_ids # tensor([[64006, 942, 43, 32720, 8384, 64001]]) | |
| 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=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 | |
| 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=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>")) | |
| 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. 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 |