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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- jhu-clsp/jfleg |
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language: |
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- en |
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base_model: |
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- google-t5/t5-base |
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pipeline_tag: text2text-generation |
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--- |
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# 📚 Model Card for Grammar Correction Model |
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This is a grammar correction model based on the Google T5 architecture, fine-tuned on the JHU-CLSP/JFLEG dataset for text correction tasks. ✍️ |
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## Model Details |
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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. |
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- **Follow the Developer:** Abdul Samad Siddiqui ([@samadpls](https://github.com/samadpls)) 👨💻 |
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## Uses |
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This model can be directly used to correct grammar and spelling mistakes in sentences. ✅ |
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### Example Usage |
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Here's a basic code snippet to demonstrate how to use the model: |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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# Load the model and tokenizer |
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model_name = "samadpls/t5-base-grammar-checker" |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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# Example input |
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example_1 = "grammar: This sentences, has bads grammar and spelling!" |
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# Tokenize and generate corrected output |
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inputs = tokenizer.encode(example_1, return_tensors="pt") |
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outputs = model.generate(inputs) |
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corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print("Corrected Sentence:", corrected_sentence) |
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``` |
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## Training Details |
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The model was trained on the JHU CLSP JFLEG dataset, which includes various examples of sentences with grammatical errors and their corrections. 📖 |
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### Training Procedure |
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- **Training Hardware:** Personal laptop with NVIDIA GeForce MX230 GDDR5 and 16GB RAM 💻 |
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- **Training Time:** Approximately 1 hour ⏳ |
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- **Hyperparameters:** No specific hyperparameters were set for training. |
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### Training Logs |
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| Step | Training Loss | Validation Loss | |
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|------|---------------|-----------------| |
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| 1 | 0.9282 | 0.6091 | |
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| 2 | 0.6182 | 0.5561 | |
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| 3 | 0.6279 | 0.5345 | |
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| 4 | 0.6345 | 0.5147 | |
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| 5 | 0.5636 | 0.5076 | |
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| 6 | 0.6009 | 0.4928 | |
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| 7 | 0.5469 | 0.4950 | |
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| 8 | 0.5797 | 0.4834 | |
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| 9 | 0.5619 | 0.4818 | |
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| 10 | 0.6342 | 0.4788 | |
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| 11 | 0.5481 | 0.4786 | |
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### Final Training Metrics |
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- **Training Runtime:** 1508.2528 seconds ⏱️ |
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- **Training Samples per Second:** 1.799 |
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- **Training Steps per Second:** 0.225 |
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- **Final Training Loss:** 0.5925 |
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- **Final Epoch:** 1.0 |
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## Model Card Contact |
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For inquiries, please contact Abdul Samad Siddiqui via GitHub. 📬 |
