metadata
language:
- en
pipeline_tag: text2text-generation
metrics:
- f1
tags:
- grammatical error correction
- GEC
- english
This is a fine-tuned version of Multilingual Bart trained (610M) on English in particular on the public dataset FCE for Grammatical Error Correction.
To initialize the model:
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained("MRNH/mbart-english-grammar-corrector")
To generate text using the model:
tokenizer = MBart50TokenizerFast.from_pretrained("MRNH/mbart-english-grammar-corrector", src_lang="en_XX", tgt_lang="en_XX")
input = tokenizer("I was here yesterday to studying",
text_target="I was here yesterday to study", return_tensors='pt')
output = model.generate(input["input_ids"],attention_mask=input["attention_mask"],
forced_bos_token_id=tokenizer_it.lang_code_to_id["en_XX"])
Training of the model is performed using the following loss computation based on the hidden state output h:
h.logits, h.loss = model(input_ids=input["input_ids"],
attention_mask=input["attention_mask"],
labels=input["labels"])