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DistilBERT Subject Classifier
A lightweight, single‐label text‐classification model built on DistilBERT to assign one of 19 high‑level academic or professional subjects to an input text.
Model Overview
- Architecture:
DistilBertForSequenceClassification - Hidden Size: 768
- Intermediate (FFN) Size: 3072
- Heads / Layers: 12 heads × 6 layers
- Dropout:
- Attention dropout: 0.1
- Sequence‐classification dropout: 0.2
- Activation: GELU
- Max Sequence Length: 512 tokens
Label Mapping
| ID | Label | ID | Label |
|---|---|---|---|
| 0 | english | 10 | math |
| 1 | music | 11 | philosophy |
| 2 | environmental science | 12 | psychology |
| 3 | history | 13 | engineering |
| 4 | art and design | 14 | biology |
| 5 | political science | 15 | sociology |
| 6 | computer science | 16 | chemistry |
| 7 | business/management | 17 | economics |
| 8 | medicine/health sciences | 18 | physics |
| 9 | law |
Configuration Highlights
{
"model_type": "distilbert",
"initializer_range": 0.02,
"problem_type": "single_label_classification",
"torch_dtype": "float32",
"transformers_version": "4.49.0"
}
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