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| Metric | Mean (5-fold) | Best Fold (#2) |
|---|---|---|
| Test Accuracy | 60.54% | 72.49% |
| Test F1 | 0.5352 | 0.6852 |
| Fold | Test Accuracy | Test F1 | Note |
|---|---|---|---|
| 0 | 62.27% | 0.5580 | |
| 1 | 54.27% | 0.4427 | |
| 2 | 72.49% | 0.6852 | ⭐ Best |
| 3 | 56.86% | 0.4741 | |
| 4 | 56.80% | 0.5158 |
split_2 with 72.49% accuracyleave-x-out/
├── split_0/best_model.model
├── split_1/best_model.model
├── split_2/best_model.model
├── split_3/best_model.model
└── split_4/best_model.model
evaluation_results.json1from huggingface_hub import model_info
2
3info = model_info("bodyanats/booknlp-plus-speaker-attribution")
4folds = info.card_data["folds"]
5
6# Show all folds
7for fold_id, data in folds.items():
8 print(f"Fold {fold_id}: Acc={data['accuracy']:.2%}, F1={data['f1']:.4f}")
9
10# Get best fold
11best_fold = info.card_data["best_fold"]
12print(f"\nBest: Fold {best_fold} ({folds[str(best_fold)]['accuracy']:.2%})")1from huggingface_hub import model_info, hf_hub_download
2
3info = model_info("bodyanats/booknlp-plus-speaker-attribution")
4best_path = info.card_data["best_model_path"]
5model_path = hf_hub_download(repo_id="bodyanats/booknlp-plus-speaker-attribution", filename=best_path)1from huggingface_hub import model_info, hf_hub_download
2
3info = model_info("bodyanats/booknlp-plus-speaker-attribution")
4fold_id = "2" # Choose fold 0-4
5model_path = hf_hub_download(
6 repo_id="bodyanats/booknlp-plus-speaker-attribution",
7 filename=info.card_data["folds"][fold_id]["model_path"]
8)1from huggingface_hub import snapshot_download
2
3local_dir = snapshot_download(repo_id="bodyanats/booknlp-plus-speaker-attribution")