samadpls
/

t5-base-grammar-checker

@@ -1,199 +1,87 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 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.
+- **Developed by:** 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
+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. 📬