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  1. cct-paper-v7-sv1/README.md +0 -80
  2. cct-paper-v7-sv1/electra_cola/cls_begin_test.npz +0 -3
  3. cct-paper-v7-sv1/electra_cola/cls_begin_train.npz +0 -3
  4. cct-paper-v7-sv1/electra_cola/cls_begin_validation.npz +0 -3
  5. cct-paper-v7-sv1/electra_cola/cls_last_test.npz +0 -3
  6. cct-paper-v7-sv1/electra_cola/cls_last_train.npz +0 -3
  7. cct-paper-v7-sv1/electra_cola/cls_last_validation.npz +0 -3
  8. cct-paper-v7-sv1/electra_cola/cls_mid_test.npz +0 -3
  9. cct-paper-v7-sv1/electra_cola/cls_mid_train.npz +0 -3
  10. cct-paper-v7-sv1/electra_cola/cls_mid_validation.npz +0 -3
  11. cct-paper-v7-sv1/electra_cola/graph_test.npz +0 -3
  12. cct-paper-v7-sv1/electra_cola/graph_train.npz +0 -3
  13. cct-paper-v7-sv1/electra_cola/graph_validation.npz +0 -3
  14. cct-paper-v7-sv1/electra_cola/intra_attn_test.npz +0 -3
  15. cct-paper-v7-sv1/electra_cola/intra_attn_train.npz +0 -3
  16. cct-paper-v7-sv1/electra_cola/intra_attn_validation.npz +0 -3
  17. cct-paper-v7-sv1/electra_cola/punct_test.npz +0 -3
  18. cct-paper-v7-sv1/electra_cola/punct_train.npz +0 -3
  19. cct-paper-v7-sv1/electra_cola/punct_validation.npz +0 -3
  20. cct-paper-v7-sv1/electra_cola/ripser_test.npz +0 -3
  21. cct-paper-v7-sv1/electra_cola/ripser_train.npz +0 -3
  22. cct-paper-v7-sv1/electra_cola/ripser_validation.npz +0 -3
  23. cct-paper-v7-sv1/electra_cola/template_test.npz +0 -3
  24. cct-paper-v7-sv1/electra_cola/template_train.npz +0 -3
  25. cct-paper-v7-sv1/electra_cola/template_validation.npz +0 -3
  26. cct-paper-v7-sv1/electra_cola/toktopo_graph_test.npz +0 -3
  27. cct-paper-v7-sv1/electra_cola/toktopo_graph_train.npz +0 -3
  28. cct-paper-v7-sv1/electra_cola/toktopo_graph_validation.npz +0 -3
  29. cct-paper-v7-sv1/electra_cola/toktopo_pd_test.npz +0 -3
  30. cct-paper-v7-sv1/electra_cola/toktopo_pd_train.npz +0 -3
  31. cct-paper-v7-sv1/electra_cola/toktopo_pd_validation.npz +0 -3
  32. cct-paper-v7-sv1/electra_goemotions/cls_begin_test.npz +0 -3
  33. cct-paper-v7-sv1/electra_goemotions/cls_begin_train.npz +0 -3
  34. cct-paper-v7-sv1/electra_goemotions/cls_begin_validation.npz +0 -3
  35. cct-paper-v7-sv1/electra_goemotions/cls_last_test.npz +0 -3
  36. cct-paper-v7-sv1/electra_goemotions/cls_last_train.npz +0 -3
  37. cct-paper-v7-sv1/electra_goemotions/cls_last_validation.npz +0 -3
  38. cct-paper-v7-sv1/electra_goemotions/cls_mid_test.npz +0 -3
  39. cct-paper-v7-sv1/electra_goemotions/cls_mid_train.npz +0 -3
  40. cct-paper-v7-sv1/electra_goemotions/cls_mid_validation.npz +0 -3
  41. cct-paper-v7-sv1/electra_goemotions/graph_test.npz +0 -3
  42. cct-paper-v7-sv1/electra_goemotions/graph_train.npz +0 -3
  43. cct-paper-v7-sv1/electra_goemotions/graph_validation.npz +0 -3
  44. cct-paper-v7-sv1/electra_goemotions/intra_attn_test.npz +0 -3
  45. cct-paper-v7-sv1/electra_goemotions/intra_attn_train.npz +0 -3
  46. cct-paper-v7-sv1/electra_goemotions/intra_attn_validation.npz +0 -3
  47. cct-paper-v7-sv1/electra_goemotions/punct_test.npz +0 -3
  48. cct-paper-v7-sv1/electra_goemotions/punct_train.npz +0 -3
  49. cct-paper-v7-sv1/electra_goemotions/punct_validation.npz +0 -3
  50. cct-paper-v7-sv1/electra_goemotions/ripser_test.npz +0 -3
cct-paper-v7-sv1/README.md DELETED
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- ---
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- license: cc-by-4.0
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- tags:
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- - transformers
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- - attention-topology
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- - text-classification
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- - uncertainty-estimation
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- pretty_name: CCT paper — Single-v1 attention-topology features
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- ---
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-
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- # `cct-paper-v7-sv1` — Single-v1 attention-topology features
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-
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- Pre-extracted 14,604-scalar Single-v1 attention-topology / hidden-state
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- features for all 11 paper configurations from the EMNLP 2026 submission
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- *"Class-Centroid Trajectories for Admissible Misclassification Detection
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- in Fine-Tuned Transformer Classifiers."*
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-
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- Use these to **re-train the SV1 or SV1+CCT CatBoost detector from
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- scratch without re-running feature extraction**. See
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- [`cct-paper-v7-models`](../cct-paper-v7-models) for the underlying
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- encoder weights, and [`cct-paper-v7`](../cct-paper-v7) for the lean
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- verification pack (paper PDF, smoke tests, paper-result CSVs).
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-
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- ## Layout
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-
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- ```
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- cct-paper-v7-sv1/{cfg}/{family}_{split}.npz
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- ```
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-
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- Per cfg, 10 sub-families × 3 splits = 30 npz files:
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-
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- | Sub-family | Dim (roberta-base) | Description |
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- |---|---:|---|
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- | `ripser` | 2,016 | per-head H₀/H₁ barcodes of attention |
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- | `template` | 1,008 | per-head template-attention summaries |
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- | `graph` | 7,776 | per-head attention-graph topology |
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- | `toktopo_pd` | 120 | per-layer token-graph persistence diagrams |
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- | `toktopo_graph` | 84 | per-layer token-graph structure |
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- | `intra_attn` | 1,152 | intra-attention spectral statistics |
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- | `punct` | 148 | punctuation-token Frobenius distances |
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- | `cls_last` | 768 | raw `[CLS]` at layer 12 |
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- | `cls_mid` | 768 | raw `[CLS]` at layer 6 |
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- | `cls_begin` | 768 | raw `[CLS]` at layer 0 |
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- | **Total** | **14,604** | |
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-
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- Sizes vary with sample count: ~230 MB per electra cfg, ~250-500 MB per
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- roberta cfg (largest is roberta/yelp at 1.9 GB due to N=30k test).
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- Total ~5.2 GB.
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-
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- ## NPZ schema
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-
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- Each `{family}_{split}.npz`:
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-
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- | Key | Shape | Dtype | Description |
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- |---|---|---|---|
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- | `features` | `(N, D)` | `float32` | The D scalars from this family per sample |
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- | `columns` | `(D,)` | `<U…` | Feature names |
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- | `idx` | `(N,)` | `int64` | Row index in the split |
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-
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- ## How to retrain SV1+CCT from cached features
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-
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- ```python
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- import numpy as np
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-
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- CFG = "roberta_newsgroups"
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- SV1_FAMS = ["ripser","template","graph","toktopo_pd","toktopo_graph",
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- "intra_attn","punct","cls_last","cls_mid","cls_begin"]
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-
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- def load_sv1(cfg, split):
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- Xs = [np.load(f"cct-paper-v7-sv1/{cfg}/{f}_{split}.npz")["features"]
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- for f in SV1_FAMS]
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- return np.concatenate(Xs, axis=1).astype(np.float32)
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-
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- X_tr = load_sv1(CFG, "train")
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- # Combine with CCT-25 from cct-paper-v7/features/{cfg}/cct_train.npz
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- # to get the full SV1+CCT 14,629-scalar input.
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- ```
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-
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- The full SV1+CCT trainer (per-family CatBoost-importance budget cutter
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- → Protocol H CatBoost) is shipped in `cct-paper-v7/repo/cct_reproduce`.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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