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--- |
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license: apache-2.0 |
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base_model: google/mt5-large |
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tags: |
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- thai |
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- grammatical-error-correction |
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- mt5 |
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- fine-tuned |
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- l2-learners |
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- generated_from_keras_callback |
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model-index: |
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- name: pakawadeep/ctfl-gec-th |
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results: |
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- task: |
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name: Grammatical Error Correction |
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type: text2text-generation |
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dataset: |
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name: CTFL-GEC |
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type: custom |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.47 |
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- name: Recall |
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type: recall |
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value: 0.47 |
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- name: F1 |
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type: f1 |
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value: 0.47 |
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- name: F0.5 |
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type: f0.5 |
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value: 0.47 |
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- name: BLEU |
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type: bleu |
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value: 0.69 |
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- name: GLEU |
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type: gleu |
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value: 0.68 |
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- name: CHRF |
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type: chrf |
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value: 0.87 |
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language: |
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- th |
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--- |
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# pakawadeep/ctfl-gec-th |
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This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large), trained for **Grammatical Error Correction (GEC)** in **Thai** for **L2 learners**. It was developed as part of the research *"Grammatical Error Correction for L2 Learners of Thai Using Large Language Models"*, and represents the best-performing model in the study. |
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## Model description |
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This model is based on the mT5-large architecture and was fine-tuned on the CTFL-GEC dataset, which contains human-annotated grammatical error corrections from L2 Thai learners. To improve generalization, the dataset was augmented using the Self-Instruct method with 200% additional synthetic pairs. |
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The model is capable of correcting sentence-level grammatical errors typical of L2 Thai writing, including issues with word order, omissions, and incorrect particles. |
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## Intended uses & limitations |
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### Intended uses |
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- Grammatical error correction for Thai language learners |
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- Linguistic analysis of L2 learner errors |
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- Research in low-resource GEC methods |
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### Limitations |
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- May not generalize to informal or dialectal Thai |
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- Performance may degrade on sentence types or domains not represented in the training data |
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- Designed for Thai GEC only; not optimized for multilingual correction tasks |
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## Training and evaluation data |
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The model was fine-tuned on a combined dataset consisting of: |
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- **CTFL-GEC**: A manually annotated corpus of Thai learner writing (370 writing samples, 4,200+ sentences) |
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- **Self-Instruct augmentation (200%)**: Synthetic GEC pairs generated using LLM prompting |
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Evaluation was conducted on a held-out portion of the human-annotated dataset using common GEC metrics. |
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## Training procedure |
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### Training hyperparameters |
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- **Optimizer**: AdamWeightDecay |
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- **Learning rate**: 2e-5 |
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- **Beta1/Beta2**: 0.9 / 0.999 |
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- **Epsilon**: 1e-7 |
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- **Weight decay**: 0.01 |
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- **Precision**: float32 |
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### Framework versions |
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- Transformers 4.41.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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## Citation |
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If you use this model, please cite the associated thesis: |
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``` |
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Pakawadee P. Chookwan, "Grammatical Error Correction for L2 Learners of Thai Using Large Language Models", 2025. |
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``` |
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