This is the IndicBART model fine-tuned on the PMI and PIB dataset for XX to En translation. 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("ai4bharat/IndicBART-XXEN", do_lower_case=False, use_fast=False, keep_accents=True)

# Or use tokenizer = AlbertTokenizer.from_pretrained("ai4bharat/IndicBART-XXEN", do_lower_case=False, use_fast=False, keep_accents=True)

model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/IndicBART-XXEN")

# Or use model = MBartForConditionalGeneration.from_pretrained("ai4bharat/IndicBART-XXEN")

# 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-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

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:])

# 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() # 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("<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
```
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
3. Note that the tokenizer I have used is based on sentencepiece and not BPE. Therefore I use the AlbertTokenizer class and not the MBartTokenizer class.
{}
transformers pytorch mbart text2text-generation autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 

This is the IndicBART model fine-tuned on the PMI and PIB dataset for XX to En translation. For detailed documentation look here: URL and URL

Usage:


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 URL
3. Note that the tokenizer I have used is based on sentencepiece and not BPE. Therefore I use the AlbertTokenizer class and not the MBartTokenizer class.
TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us 
