README.md

---
library_name: transformers
license: mit
datasets:
- jhu-clsp/jfleg
language:
- en
base_model:
- google-t5/t5-base
pipeline_tag: text2text-generation
---

# 📚 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](https://github.com/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:
```python
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
```python
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

| Step | Training Loss | Validation Loss |
|------|---------------|-----------------|
| 1    | 0.9282        | 0.6091          |
| 2    | 0.6182        | 0.5561          |
| 3    | 0.6279        | 0.5345          |
| 4    | 0.6345        | 0.5147          |
| 5    | 0.5636        | 0.5076          |
| 6    | 0.6009        | 0.4928          |
| 7    | 0.5469        | 0.4950          |
| 8    | 0.5797        | 0.4834          |
| 9    | 0.5619        | 0.4818          |
| 10   | 0.6342        | 0.4788          |
| 11   | 0.5481        | 0.4786          |

### 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. 📬