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:
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):
# 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:
model = AutoModelForSequenceClassification.from_pretrained(path, dtype=torch.float32)
With float32 inference the released encoders reproduce the shipped
predictions_*.parquet logits to ~1e-5.