Text Classification
Transformers
Safetensors
English
deberta-v2
feature-extraction
ielts
automated-essay-scoring
deberta-v3
regression
nlp
custom_code
text-embeddings-inference
Instructions to use star092304/ielts-writing-task2-debertav3base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use star092304/ielts-writing-task2-debertav3base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="star092304/ielts-writing-task2-debertav3base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("star092304/ielts-writing-task2-debertav3base", trust_remote_code=True) model = AutoModel.from_pretrained("star092304/ielts-writing-task2-debertav3base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md
Browse files
README.md
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pipeline_tag: text-classification
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pipeline_tag: text-classification
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---
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# IELTS Writing Task 2 Essay Scorer (DeBERTa-v3-base)
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This repository contains a custom **Prompt-Aware IELTS Essay Scorer** fine-tuned on DeBERTa-v3-base. It simultaneously predicts the 4 analytical criteria of IELTS Writing Task 2 criteria and computes the overall band score.
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## 🔗 Quick Links & Resources
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* **Dataset:** [View Clean Dataset](https://huggingface.co/star092304/ielts-writing-task2-debertav3base/tree/main/data)
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* **Training Logs:** [View training_history.csv](https://huggingface.co/star092304/ielts-writing-task2-debertav3base/blob/main/training_history.csv)
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* **Training Notebook:** [src/deberta-v3-base-ielts-aes.ipynb](https://huggingface.co/star092304/ielts-writing-task2-debertav3base/blob/main/src/deberta-v3-base-ielts-aes.ipynb)
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* **Inference Notebook:** [src/IELTS_DeBERTa_Inference.ipynb](https://huggingface.co/star092304/ielts-writing-task2-debertav3base/blob/main/src/IELTS_DeBERTa_Inference.ipynb)
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---
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## Model Capabilities
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The model takes both the **Writing Prompt** and the **Student's Essay** as inputs, focuses specifically on the essay body using an attention pooling layer, and grades the text across 4 core traits (scaled 0-9):
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* **TA** (Task Achievement)
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* **CC** (Coherence and Cohesion)
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* **LR** (Lexical Resource)
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* **GRA** (Grammatical Range and Accuracy)
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* **OverallBand** (Calculated average rounded to the nearest 0.5)
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### Training Curves
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---
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## License
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This project is licensed under the MIT License.
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