ionut-visan/Flan-T5-Large_Grammar_Ro

Flan-T5_Grammar (Romanian)

Flan-T5-Large is an instruction-tuned language model that treats all NLP tasks as text-to-text problems, excelling at grammar-related tasks like correction, rephrasing, and sentence completion through natural language prompts.

---

Dataset

I fine-tuned Flan-T5-Large on gec-ro-comments and gec-ro-cna datasets. The split was created by combining train (635 pairs), test (686 pairs), validation (666) from gec-ro-comments and train (1286) from gec-ro-cna to create the training set and test (1234) from gec-ro-cna for testing.

---

Configuration

• Model = “google/flan-t5-large”
• Learning rate = 5e-5
• Batch size = 4 (for both dataloaders)
• Optimizer = AdamW
• Epochs = 10
• Scheduler = Linear (with warmup = 0.1)

The condition for saving the model is that the test loss, wer, cer must be lower than the previously recorded best values.

---

Results

The fine-tuned model was saved at epoch 5 with Test Loss: 0.3151, WER: 0.0893, CER: 0.0304, BLEU: 0.8424, GLEU: 0.8405, ROUGE-1: 0.9294, ROUGE-2: 0.8723, ROUGE-L: 0.9279.

---

How to use

import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load model and tokenizer
model_name = "ionut-visan/Flan-T5-Large_Grammar_Ro"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
model.eval()

# Function to correct grammar
def correct_sentence(sentence):
    input_text = "grammar: " + sentence
    inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = model.generate(inputs, max_length=128, num_beams=4, early_stopping=True)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Interactive loop
print("Romanian Grammar Corrector (type 'exit' to quit)")
while True:
    user_input = input("\nEnter a sentence to correct: ")
    if user_input.lower() == "exit":
        print("Exiting. 👋")
        break
    corrected = correct_sentence(user_input)
    print("Corrected:", corrected)

---

Communication

For any questions regarding this model or to explore collaborations on ambitious AI/ML projects, please feel free to contact me at:

• [email protected]
• Ionuț Vișan's Linkedin