T5-Small Grammar Correction
A fine-tuned t5-small model for correcting grammar errors in English text. Given a sentence, the model generates a grammatically correct version using a text-to-text approach.
Model Details
- Developed by: Harsha Vardhan N
- Model type: Sequence-to-Sequence Transformer
- Language(s): English
- License: Apache 2.0
- Finetuned from model: t5-small
Training Details
Training Data
The model was fine-tuned on the wiki_auto/auto_full_with_split dataset, a large-scale corpus designed for sentence-level grammatical and stylistic simplification. It contains aligned pairs of complex and simplified English sentences extracted from Wikipedia and Simple Wikipedia. For this task, the dataset was used to teach the model how to correct ungrammatical sentences into fluent and grammatically correct English.
Training Procedure
- Epochs: 3
- Training Duration: ~1 hour
- Optimizer: AdamW (via Hugging Face
Seq2SeqTrainer) - Learning Rate: 5e-5
- Batch Size: 8
- Environment: Google Colab GPU
Technical Specifications
Compute Infrastructure
Hardware
- GPU: Google Colab-provided GPU (likely Tesla T4)
Software
- Framework: Hugging Face Transformers, PyTorch
- Trainer Used: Seq2SeqTrainer
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Base model
google-t5/t5-small