Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+features.jsonl filter=lfs diff=lfs merge=lfs -text
+extractor/tokenizer.json filter=lfs diff=lfs merge=lfs -text

NOTICE

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+Gemma is provided under and subject to the Gemma Terms of Use found at
+ai.google.dev/gemma/terms.

README.md

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+---
+library_name: detectorscope
+license: gemma
+base_model: google/gemma-2-2b
+tags:
+- ai-text-detection
+- sparse-autoencoder
+- interpretability
+---
+
+# COLING · Gemma-2 base
+
+An auditable AI-text detector from *Building Auditable AI Text Detectors with
+Sparse Autoencoders*: a Gemma-2-2b language model with hooks (bundled under
+`extractor/`; Gemma Terms of Use, see `NOTICE`), one JumpReLU sparse
+autoencoder per hooked layer (from [Gemma
+Scope](https://huggingface.co/google/gemma-scope-2b-pt-res), CC-BY-4.0, as
+are the heads), and sparse heads over the max-pooled SAE features. Every decision is the sum
+of per-feature contributions, each with a validated natural-language
+description.
+
+```python
+from detectorscope import AuditableDetector   # pip install detectorscope
+
+det = AuditableDetector.from_pretrained("liamdugan/detector-scope")
+out = det("The authors propose a novel framework for ...")
+out.p_ai, out.label           # P(AI); "ai" or "human" at the head's threshold
+out.features[:3]              # Feature(id, importance, description, highlights), |importance| descending
+```
+
+`head="xgboost"` or `tau=0.5` on `from_pretrained` or on the call pick
+another head below; `return_highlights=True` adds `out.tokens` and, per
+feature, the `(token index, activation)` pairs where it fires.
+
+## Branches
+
+Each branch of this repo is a complete detector: `main` is the paper's
+flagship setting (fine-tuned model and fine-tuned SAEs); `ft-sae` keeps
+`google/gemma-2-2b` with the fine-tuned SAEs and `base-sae` keeps it with the
+Gemma Scope SAEs. For the peer-review repo the branch also names the target
+generator (`llama`, `gpt4o-ft-sae`, ...; `gpt4o` is `main`). Pass the branch
+as `revision=` to `from_pretrained`.
+
+## Files
+
+- `extractor/`: the language model as a transformers checkpoint
+  (`AutoModelForSequenceClassification.from_pretrained(repo, subfolder="extractor")`
+  for the fine-tuned ones).
+- `model.safetensors`: the SAEs, read into `AuditableDetector` by
+  `from_pretrained` (`config.json` holds the hook points and default head).
+- `heads/<type>_tau<tau>.<safetensors|json>`: one head per type and
+  auto-interpretation threshold tau; each carries its decision threshold.
+- `features.jsonl`: one record per described SAE feature, verbatim from the
+  auto-interpretation stage, with its log-odds ratio on the training split.
+
+## Heads
+
+The default is logreg at tau 0.85, the paper's operating point.
+
+| head | tau | decision threshold (logit) | source |
+|---|---|---|---|
+| logreg | 0.00 | 0.3011 | `sgd_lr1e-02_l1-1e-01_threshold0.0_coling_logreg_ep3.pkl` |
+| logreg | 0.05 | 0.2593 | `sgd_lr1e-02_l1-1e-01_threshold0.05_coling_logreg_ep3.pkl` |
+| logreg | 0.10 | 0.2527 | `sgd_lr1e-02_l1-1e-01_threshold0.10_coling_logreg_ep3.pkl` |
+| logreg | 0.15 | 0.2481 | `sgd_lr1e-02_l1-1e-01_threshold0.15_coling_logreg_ep3.pkl` |
+| logreg | 0.20 | 0.2511 | `sgd_lr1e-02_l1-1e-01_threshold0.20_coling_logreg_ep3.pkl` |
+| logreg | 0.25 | 0.2512 | `sgd_lr1e-02_l1-1e-01_threshold0.25_coling_logreg_ep3.pkl` |
+| logreg | 0.30 | 0.2572 | `sgd_lr1e-02_l1-1e-01_threshold0.30_coling_logreg_ep3.pkl` |
+| logreg | 0.35 | 0.3022 | `sgd_lr1e-02_l1-1e-01_threshold0.35_coling_logreg_ep3.pkl` |
+| logreg | 0.40 | 0.2969 | `sgd_lr1e-02_l1-1e-01_threshold0.40_coling_logreg_ep3.pkl` |
+| logreg | 0.45 | 0.2839 | `sgd_lr1e-02_l1-1e-01_threshold0.45_coling_logreg_ep3.pkl` |
+| logreg | 0.50 | 0.3375 | `sgd_lr1e-02_l1-1e-01_threshold0.50_coling_logreg_ep3.pkl` |
+| logreg | 0.55 | 0.3544 | `sgd_lr1e-02_l1-1e-01_threshold0.55_coling_logreg_ep3.pkl` |
+| logreg | 0.60 | 0.3616 | `sgd_lr1e-02_l1-1e-01_threshold0.60_coling_logreg_ep3.pkl` |
+| logreg | 0.65 | 0.3854 | `sgd_lr1e-02_l1-1e-01_threshold0.65_coling_logreg_ep3.pkl` |
+| logreg | 0.70 | 0.3597 | `sgd_lr1e-02_l1-1e-01_threshold0.70_coling_logreg_ep3.pkl` |
+| logreg | 0.75 | 0.4081 | `sgd_lr1e-02_l1-1e-01_threshold0.75_coling_logreg_ep3.pkl` |
+| logreg | 0.80 | 0.4447 | `sgd_lr1e-02_l1-1e-01_threshold0.80_coling_logreg_ep3.pkl` |
+| logreg | 0.85 | 0.5008 | `sgd_lr1e-02_l1-1e-01_threshold0.85_coling_logreg_ep3.pkl` |
+| logreg | 0.90 | 0.5354 | `sgd_lr1e-02_l1-1e-01_threshold0.90_coling_logreg_ep3.pkl` |
+| logreg | 0.95 | inf | `sgd_lr1e-02_l1-1e-01_threshold0.95_coling_logreg_ep3.pkl` |
+| xgboost | 0.00 | 0.6842 | `threshold0.0_coling_xgbmodel.pkl` |
+| xgboost | 0.05 | 0.7290 | `threshold0.05_coling_xgbmodel.pkl` |
+| xgboost | 0.10 | 0.7455 | `threshold0.1_coling_xgbmodel.pkl` |
+| xgboost | 0.15 | 0.7075 | `threshold0.15_coling_xgbmodel.pkl` |
+| xgboost | 0.20 | 0.7255 | `threshold0.2_coling_xgbmodel.pkl` |
+| xgboost | 0.25 | 0.6771 | `threshold0.25_coling_xgbmodel.pkl` |
+| xgboost | 0.30 | 0.5776 | `threshold0.3_coling_xgbmodel.pkl` |
+| xgboost | 0.35 | 0.6960 | `threshold0.35_coling_xgbmodel.pkl` |
+| xgboost | 0.40 | 0.7782 | `threshold0.4_coling_xgbmodel.pkl` |
+| xgboost | 0.45 | 0.8097 | `threshold0.45_coling_xgbmodel.pkl` |
+| xgboost | 0.50 | 0.8531 | `threshold0.5_coling_xgbmodel.pkl` |
+| xgboost | 0.55 | 0.8291 | `threshold0.55_coling_xgbmodel.pkl` |
+| xgboost | 0.60 | 0.7867 | `threshold0.6_coling_xgbmodel.pkl` |
+| xgboost | 0.65 | 0.7935 | `threshold0.65_coling_xgbmodel.pkl` |
+| xgboost | 0.70 | 0.8680 | `threshold0.7_coling_xgbmodel.pkl` |
+| xgboost | 0.75 | 0.7816 | `threshold0.75_coling_xgbmodel.pkl` |
+| xgboost | 0.80 | 0.8019 | `threshold0.8_coling_xgbmodel.pkl` |
+| xgboost | 0.85 | 0.6463 | `threshold0.85_coling_xgbmodel.pkl` |
+| xgboost | 0.90 | 0.1872 | `threshold0.9_coling_xgbmodel.pkl` |
+| xgboost | 0.95 | 0.6281 | `threshold0.95_coling_xgbmodel.pkl` |
+
+## Features with the largest weights (default head)
+
+| id | weight | description |
+|---|---|---|
+| 20122 | +0.1577 | introductory phrases or filler text commonly used by ai language models when declining personal questions |
+| 17929 | +0.1263 | academic and scientific research methodology and technical analysis terminology |
+| 1262 | -0.1218 | contractions containing the letter n and apostrophe specifically focusing on the n't ending |
+| 9535 | -0.1091 | newline characters in scientific or technical text |
+| 1076 | +0.0803 | conversational filler phrases used in informal speech |
+| 14666 | -0.0796 | wikipedia category and navigation section headers |
+| 14967 | -0.0780 | comma punctuation following a newline or preceding sentence start |
+| 44946 | +0.0648 | critical commentary or argumentative analysis of student policies and social responsibility |
+| 32030 | -0.0596 | markdown editing and formatting indicators |
+| 5896 | -0.0593 | newline characters in scientific or technical text |
+| 7178 | -0.0549 | parenthetical clauses or aside remarks in text |
+| 10954 | -0.0481 | neuron fires on the digit 0 inside url placeholders like url 0 |
+| 41406 | +0.0452 | neuron activates on random text fragments and noisy character sequences often found in garbled or fragmented data |
+| 15058 | -0.0435 | wikipedia category and list formatting specifically numerical prefixes and lists |
+| 7991 | +0.0381 | reads more or similar web page navigation links |
+| 13528 | -0.0321 | tokenized components of medical or diagnostic phrasing and lists |
+| 33943 | +0.0309 | repeated phrases or sequences in text |
+| 25049 | -0.0302 | newline characters and line breaks within technical or academic documents |
+| 6780 | -0.0298 | commas occurring within parenthetical or descriptive clauses |
+| 27535 | -0.0296 | newline characters in mathematical or technical contexts |

config.json

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+{
+  "base_model": null,
+  "head": "logreg",
+  "hook_points": [
+    "model.layers.8",
+    "model.layers.14",
+    "model.layers.18",
+    "model.layers.23"
+  ],
+  "tau": 0.85
+}

extractor/config.json

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+  ],
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+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
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+    "sliding_attention",
+    "full_attention",
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+    "full_attention",
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+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 256000
+}

extractor/generation_config.json

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+  "cache_implementation": "hybrid",
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.57.6"
+}

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