|
--- |
|
language: |
|
- es |
|
metrics: |
|
- bleu |
|
base_model: |
|
- vgaraujov/bart-base-spanish |
|
pipeline_tag: text2text-generation |
|
library_name: transformers |
|
tags: |
|
- gec |
|
- spanish |
|
- seq2seq |
|
- bart |
|
- cows-l2h |
|
--- |
|
|
|
This model has been trained on 80% of the COWS-L2H dataset and 80,984 SYNTHETICALLY-GENERATED errorful sentences for grammatical error correction of Spanish text. The corpus was sentencized, so the model has been fine-tuned for SENTENCE CORRECTION. This model will likely not perform well on an entire paragraph. To correct a paragraph, sentencize the text and run the model for each sentence. |
|
|
|
The synthetic data was generated based on a rule-based algorithm from well-formed Spanish sentences. The code for synthetic generaton is available in the Github repo for this project: https://github.com/SkitCon/synth_gec_es |
|
|
|
BLEU: 0.851 on COWS-L2H |
|
|
|
Example usage: |
|
|
|
```python |
|
from transformers import AutoTokenizer, BartForConditionalGeneration |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("SkitCon/gec-spanish-BARTO-SYNTHETIC") |
|
model = BartForConditionalGeneration.from_pretrained("SkitCon/gec-spanish-BARTO-SYNTHETIC") |
|
|
|
input_sentences = ["Yo va al tienda.", "Espero que tú ganas."] |
|
|
|
tokenized_text = tokenizer(input_sentences, max_length=128, padding="max_length", truncation=True, return_tensors="pt") |
|
|
|
input_ids = tokenized_text["input_ids"].squeeze() |
|
attention_mask = tokenized_text["attention_mask"].squeeze() |
|
|
|
outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask) |
|
|
|
for sentence in tokenizer.batch_decode(outputs, skip_special_tokens=True): |
|
print(sentence) |
|
``` |
