AI Grammar Correction — Fine-tuned FLAN-T5

Model Description

This is a sequence-to-sequence transformer model fine-tuned for Grammatical Error Correction (GEC) based on google/flan-t5-base.

It automatically detects and corrects grammatical, syntactical, and stylistic errors in English sentences, returning fluent, natural corrections.

Task Details

• Task: Grammatical Error Correction (GEC)
• Base Architecture: google/flan-t5-base
• Input Prefix: "grammar: <input_text>"
• Max Sequence Length: 128 tokens

Dataset Specifications

Fine-tuned on a curated dataset of 100,000 sentence pairs:

• Train Set: 80,000 pairs (80%)
• Validation Set: 10,000 pairs (10%)
• Test Set: 10,000 pairs (10%)

Evaluation Results (10,000 Unseen Test Examples)

How to Use

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

MODEL_ID = "anuvid/ai-grammar-correction-model"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)

text = "She go to school yesterday."
inputs = tokenizer(f"grammar: {text}", return_tensors="pt")

outputs = model.generate(**inputs, max_length=128, num_beams=4, early_stopping=True)
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Original :", text)
print("Corrected:", corrected_text)
# Output: "She went to school yesterday."

Example Corrections