metadata
language:
- en
pipeline_tag: text2text-generation
metrics:
- f1
tags:
- grammatical error correction
- GEC
- english
This is a fine-tuned version of LLAMA2 trained (7b) on spider, sql-create-context.
To initialize the model:
#from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
#model = MBartForConditionalGeneration.from_pretrained("MRNH/mbart-english-grammar-corrector")
Use the tokenizer:
#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')
To generate text using the model:
#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"])