|
--- |
|
language: |
|
- en |
|
tags: |
|
- grammatical error correction |
|
- text2text |
|
- t5 |
|
license: apache-2.0 |
|
datasets: |
|
- clang-8 |
|
- conll-14 |
|
- con-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). |
|
|
|
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. |
|
``` |
