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--- |
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datasets: |
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- grammarly/coedit |
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- Owishiboo/grammar-correction |
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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: text-generation |
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--- |
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# English Grammar Correction model |
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# Quick Start (Python) |
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### Installation |
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```bash |
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pip install transformers torch |
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``` |
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### Basic Usage |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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import torch |
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# Load model and tokenizer |
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model_name = "yoon-eunbin/t5-gec-model" |
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tokenizer = T5Tokenizer.from_pretrained(model_name) |
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model = T5ForConditionalGeneration.from_pretrained(model_name) |
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# Set device |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = model.to(device) |
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# Prepare input |
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text = "He has left the room when I came into the room." |
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64).to(device) |
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# Generate correction |
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outputs = model.generate(**inputs, max_length=64) |
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# Decode output |
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corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(f"Original: {text}") |
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print(f"Corrected: {corrected_text}") |
