Text Classification
Transformers
Safetensors
English
deberta-v2
image-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)# 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) - Notebooks
- Google Colab
- Kaggle
IELTS Writing Task 2 Essay Scorer (DeBERTa-v3-base)
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.
π Quick Links & Resources
- Dataset: View Clean Dataset
- Training Logs: View training_history.csv
- Training Notebook: src/deberta-v3-base-ielts-aes.ipynb
- Inference Notebook: src/IELTS_DeBERTa_Inference.ipynb
Model Capabilities
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):
- TA (Task Achievement)
- CC (Coherence and Cohesion)
- LR (Lexical Resource)
- GRA (Grammatical Range and Accuracy)
- OverallBand (Calculated average rounded to the nearest 0.5)
Training Curves
License
This project is licensed under the MIT License.
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