samadpls/t5-base-grammar-checker

📚 Model Card for Grammar Correction Model

This is a grammar correction model based on the Google T5 architecture, fine-tuned on the JHU-CLSP/JFLEG dataset for text correction tasks. ✍️

Model Details

This model is designed to correct grammatical errors in English sentences. It was fine-tuned using the JFLEG dataset, which provides examples of grammatically correct sentences.

• —Follow the Developer: Abdul Samad Siddiqui (@samadpls) 👨‍💻

Uses

This model can be directly used to correct grammar and spelling mistakes in sentences. ✅

Example Usage

Here's a basic code snippet to demonstrate how to use the model:

import requests

API_URL = "https://api-inference.huggingface.co/models/samadpls/t5-base-grammar-checker"
HEADERS = {"Authorization": "Bearer YOUR_HF_API_KEY"}

def query(payload):
    response = requests.post(API_URL, headers=HEADERS, json=payload)
    return response.json()

data = query({"inputs": "grammar: This sentences, has bads grammar and spelling!"})
print(data)

OR

from transformers import T5ForConditionalGeneration, T5Tokenizer

# Load the model and tokenizer
model_name = "samadpls/t5-base-grammar-checker"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Example input
example_1 = "grammar: This sentences, has bads grammar and spelling!"

# Tokenize and generate corrected output
inputs = tokenizer.encode(example_1, return_tensors="pt")
outputs = model.generate(inputs)
corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Corrected Sentence:", corrected_sentence)

Training Details

The model was trained on the JHU CLSP JFLEG dataset, which includes various examples of sentences with grammatical errors and their corrections. 📖

Training Procedure

• —Training Hardware: Personal laptop with NVIDIA GeForce MX230 GDDR5 and 16GB RAM 💻
• —Training Time: Approximately 1 hour ⏳
• —Hyperparameters: No specific hyperparameters were set for training.

Training Logs

Final Training Metrics

• —Training Runtime: 1508.2528 seconds ⏱️
• —Training Samples per Second: 1.799
• —Training Steps per Second: 0.225
• —Final Training Loss: 0.5925
• —Final Epoch: 1.0

Model Card Contact

For inquiries, please contact Abdul Samad Siddiqui via GitHub. 📬