Gec Spanish BARTO SYNTHETIC

🚀 Spanish Grammatical Error Correction Model

This model is designed for grammatical error correction of Spanish text. It addresses the issue of correcting errors in Spanish sentences and provides a reliable solution for enhancing text quality.

🚀 Quick Start

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 generation is available in the Github repo for this project: https://github.com/SkitCon/synth_gec_es

✨ Features

• High BLEU Score: Achieves a BLEU score of 0.851 on the COWS - L2H dataset.
• Sentence - Level Correction: Fine - tuned for sentence - level grammatical error correction.

📦 Installation

Since this model uses the transformers library, you can install it using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

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)

📚 Documentation

Model Information

BLEU Score

The model achieves a BLEU score of 0.851 on the COWS - L2H dataset, indicating its effectiveness in grammatical error correction.

Synthetic Data Generation

The synthetic data was generated based on a rule - based algorithm from well - formed Spanish sentences. The code for synthetic generation is available in the Github repo for this project: https://github.com/SkitCon/synth_gec_es

📄 License

No license information is provided in the original document.