| --- |
| license: cc-by-4.0 |
| pretty_name: CCT — Class-Centroid Trajectories, pre-computed features |
| tags: |
| - uncertainty-estimation |
| - misclassification-detection |
| - transformers |
| - text-classification |
| --- |
| |
| # CCT — pre-computed features, predictions, results, and encoders |
|
|
| Companion data for *Class-Centroid Trajectories for Admissible |
| Misclassification Detection in Fine-Tuned Transformer Classifiers*. |
| The repository is laid out **exactly as the code expects**, so it drops |
| into the `cct-reproduce` package with a single command and no moving of |
| files: |
|
|
| ```bash |
| hf download zaaabik/223344 --repo-type dataset --local-dir cct-reproduce/data |
| # older CLI: huggingface-cli download zaaabik/223344 --repo-type dataset --local-dir cct-reproduce/data |
| pytest cct-reproduce/tests/ # 46 tests, reproduces the paper numbers |
| ``` |
|
|
| Download only what you need (each is self-contained): |
|
|
| ```bash |
| # CCT-25 + predictions only (~2 GB) — enough for every CCT table/figure |
| hf download zaaabik/223344 --repo-type dataset --local-dir cct-reproduce/data --include "features/*" "results/*" |
| # one configuration only |
| hf download zaaabik/223344 --repo-type dataset --local-dir cct-reproduce/data --include "features/roberta_cola/*" |
| ``` |
|
|
| ## Layout |
|
|
| | Path | Size | Contents | |
| |---|---|---| |
| | `features/{cfg}/` | ~2 GB | `cct_{split}.npz` (CCT-25), `cls_{begin,mid,last}_{split}.npz`, `predictions_{split}.parquet` | |
| | `sv1/{cfg}/` | ~7 GB | AttnTopo per-family features: `{ripser,template,graph,toktopo_pd,toktopo_graph,intra_attn,punct,cls_last,cls_mid,cls_begin}_{split}.npz` (14,608 columns in total) | |
| | `results/` | small | cached per-paper-table CSVs | |
| | `checkpoints/{cfg}/` | ~2.5 GB | fine-tuned encoders (seed 0), for re-extracting features from scratch | |
|
|
| `{cfg}` is one of `{roberta,electra}_{cola,sst5,toxigen,newsgroups,goemotions}` |
| plus `roberta_yelp` and `electra_yelp`; `{split}` is `train`, `validation`, or `test`. |
| Splits are the ones the encoders were fine-tuned with, so there is no |
| train→validation leakage in detector training. |
|
|
| ## Note when re-extracting features from `checkpoints/` |
|
|
| The weights are stored in bfloat16. Load them in float32 — with |
| `transformers>=5`, `from_pretrained` follows the checkpoint dtype by |
| default and bf16 inference shifts logits by up to ~0.08, which flips a |
| few predictions and perturbs the depth-based features: |
|
|
| ```python |
| model = AutoModelForSequenceClassification.from_pretrained(path, dtype=torch.float32) |
| ``` |
|
|
| With float32 inference the released encoders reproduce the shipped |
| `predictions_*.parquet` logits to ~1e-5. |
|
|
