| | --- |
| | language: |
| | - en |
| | tags: |
| | - grammatical error correction |
| | - text2text |
| | - t5 |
| | license: apache-2.0 |
| | datasets: |
| | - clang-8 |
| | - conll-14 |
| | - conll-13 |
| | metrics: |
| | - f0.5 |
| | --- |
| | |
| | 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). |
| | We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70). |
| | |
| | To effectively use the "Hosted inference API", write "gec: [YOUR SENTENCE HERE]". |
| | |
| | In order to use the model, look at the following snippet: |
| | ```python |
| | from transformers import T5ForConditionalGeneration, T5Tokenizer |
| | |
| | model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small") |
| | tokenizer = T5Tokenizer.from_pretrained('t5-small') |
| |
|
| | sentence = "I like to swimming" |
| | tokenized_sentence = tokenizer('gec: ' + sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt') |
| | corrected_sentence = tokenizer.decode( |
| | model.generate( |
| | input_ids = tokenized_sentence.input_ids, |
| | attention_mask = tokenized_sentence.attention_mask, |
| | max_length=128, |
| | num_beams=5, |
| | early_stopping=True, |
| | )[0], |
| | skip_special_tokens=True, |
| | clean_up_tokenization_spaces=True |
| | ) |
| | print(corrected_sentence) # -> I like swimming. |
| | ``` |
