File size: 1,443 Bytes
ea8bfa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | # CLARA Scripts
CLI scripts converted from `notebooks/CLARA_HFM.ipynb` for training and evaluating HFM.
## Available Scripts
### 1) `scripts/train_hfm.py`
Train CLARA on HFM and save best checkpoint.
```bash
python3 scripts/train_hfm.py \
--data-root data/HFM \
--text-dir data/HFM/text \
--output-dir outputs/hfm \
--checkpoint-name clara_hfm.pt \
--batch-size 32 \
--max-epochs 50
```
### 2) `scripts/evaluate_hfm.py`
Evaluate one checkpoint on HFM test set with all variants from notebook:
- `Raw`
- `Bias+Temp`
- `Top-2 Flip`
- `Affine Neutral`
```bash
python3 scripts/evaluate_hfm.py \
--checkpoint outputs/hfm/clara_hfm.pt \
--data-root data/HFM \
--text-dir data/HFM/text \
--output-dir results/hfm
```
### 3) `scripts/run_hfm_all.py`
Run train then evaluate in one command.
```bash
python3 scripts/run_hfm_all.py \
--data-root data/HFM \
--text-dir data/HFM/text \
--train-output-dir outputs/hfm \
--results-dir results/hfm
```
## HFM Data Preparation
If your HFM comes as 3 zip files in `data/`, extract with:
```bash
cd data
unzip -n HFM-20260303T020235Z-1-001.zip
unzip -n HFM-20260303T020235Z-1-002.zip
unzip -n HFM-20260303T020235Z-1-003.zip
```
Expected structure:
```text
data/HFM/
├── image/
├── train/image/
├── val/image/ (or valid/image/)
├── test/image/
└── text/
├── train.txt
├── val.txt (or valid.txt)
└── test.txt
```
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