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metadata
language:
  - de
pipeline_tag: text2text-generation
metrics:
  - f1
tags:
  - grammatical error correction
  - GEC
  - german

This is a fine-tuned version of Multilingual Bart (610M) trained on German in particular on the public dataset Falko-MERLIN for Grammatical Error Correction.

To initialize the model:

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
model = MBartForConditionalGeneration.from_pretrained("MRNH/mbart-german-grammar-corrector")

Use the tokenizer:

tokenizer = MBart50TokenizerFast.from_pretrained("MRNH/mbart-german-grammar-corrector", src_lang="de_DE", tgt_lang="de_DE")
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["de_DE"])

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"])