Datasets:

Beicicc
/

probeshift-activation-cache

license: mit
task_categories:
  - feature-extraction
tags:
  - interpretability
  - linear-probes
  - llm
  - activations
  - ood
  - probe-stability
pretty_name: ProbeShift Activation Cache
size_categories:
  - 100B<n<1T

ProbeShift Activation Cache

Residual-stream activations backing the ProbeShift benchmark — a label-free study of linear-probe direction stability under label-preserving semantic shift. Ships so the benchmark's numbers reproduce in minutes (no re-extraction needed).

Layout

cache_seed{0..4}/<model>/<dataset>/<distribution>/
    acts.npy     float16  [N, L+1, H]   masked-mean-pooled residual stream (L+1 = embeddings + L layers)
    labels.npy   int64    [N]
    ids.npy      int64    [N]            stable example ids (align across distributions)
    meta.json    {model, dataset, distribution, pooling, n, n_layers, hidden}

models (8): pythia-70m/160m/410m/1.4b/6.9b, gpt2, gpt2-medium, qwen2.5-0.5b
datasets (14 → 12 concepts): sst2, imdb (sentiment); ag_news, dbpedia (topic); counterfact (truth); emotion; tweet_hate; tweet_irony; tweet_offensive; subj (subjectivity); spam; cola (grammaticality); stance; amazon_cf (counterfactual)
distributions: train, iid, paraphrase/domain/length (label-preserving OOD shifts), aug0/aug1/aug2 (de/fr/ru back-translation augmentations)
seeds: 0–4 (each an independent example draw + independent paraphrase — Option A replication)

Load

import numpy as np
acts = np.load("cache_seed0/pythia-410m/sst2/iid/acts.npy", mmap_mode="r")  # [N, L+1, H] fp16
labels = np.load("cache_seed0/pythia-410m/sst2/iid/labels.npy")

Code & paper: see the ProbeShift repository. All activations produced on a single RTX 4090 (≤200 GPU·h, $0 API, zero new annotation).