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