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--- |
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language: |
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- en |
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tags: |
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- grammatical error correction |
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- text2text |
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- t5 |
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license: apache-2.0 |
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datasets: |
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- clang-8 |
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- conll-14 |
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- conll-13 |
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metrics: |
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- f0.5 |
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--- |
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This model is an implementation of the paper [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC). |
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We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70). |
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In order to use the model, look at the following snippet: |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small") |
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tokenizer = T5Tokenizer.from_pretrained('t5-small') |
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sentence = "I like to swimming" |
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tokenized_sentence = tokenizer('gec: ' + sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt') |
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corrected_sentence = tokenizer.decode( |
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model.generate( |
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input_ids = tokenized_sentence.input_ids, |
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attention_mask = tokenized_sentence.attention_mask, |
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max_length=128, |
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num_beams=5, |
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early_stopping=True, |
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)[0], |
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skip_special_tokens=True, |
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clean_up_tokenization_spaces=True |
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) |
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print(corrected_sentence) # -> I like swimming. |
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``` |
