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README.md

@@ -1,31 +1,56 @@
 ---
-dataset_info:
-  features:
-  - name: example_id
-    dtype: int64
-  - name: metadata
-    dtype: string
-  - name: classification_prompt
-    dtype: string
-  - name: classification_completion
-    dtype: string
-  - name: classification_text
-    dtype: string
-  - name: improved_signature
-    dtype: string
-  - name: improved_model_weights
-    dtype: string
-  - name: training_metrics
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 910326957
-    num_examples: 10000
-  download_size: 545568638
-  dataset_size: 910326957
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+language: en
+task_categories:
+- text-generation
 ---
+# Subject Models for Interpretability Training
+
+These examples are intended for training an interpreter to:
+- Identify what patterns a model classifies as positive based on an activation signature, with examples of: trained model + signature → pattern identification.
+
+| Signature Extraction |                                                                             |
+|----------------------|-----------------------------------------------------------------------------|
+| Neuron Profile Methods | mean, std, pca, fourier |
+| Prompt Format        | separate |
+| Signature Dataset    | configs/dataset_gen/signature_dataset.json                       |
+
+| Model Architecture   |                                                                       |
+|----------------------|-----------------------------------------------------------------------------|
+| Number of Layers     | 6 to 8 |
+| Neurons per Layer    | 7 to 12 |
+| Activation Types     | relu, gelu                       |
+| Pattern Vocab Size   | 10                                        |
+| Pattern Sequence Len | 5                                   |
+
+| Training Datasets    |                                                                             |
+|----------------------|-----------------------------------------------------------------------------|
+| Enabled Patterns     | palindrome, sorted_ascending, sorted_descending, alternating, contains_abc, starts_with, ends_with, no_repeats, has_majority, increasing_pairs, decreasing_pairs, vowel_consonant, first_last_match, mountain_pattern         |
+| Patterns per Batch   | 1-1 |
+| Pos/Neg Ratio        | 1:1                     |
+| Target Total Examples per Subject Model | 250 |
+
+| Staged Training      |                                                                             |
+|----------------------|-----------------------------------------------------------------------------|
+| Min Improvement Threshold | 0.05 (5.0%) |
+| Corruption Rate      | 0.15 (15.0%) |
+
+## Dataset Fields
+
+| Field                | Description                                                                 |
+|----------------------|-----------------------------------------------------------------------------|
+| example_id           | Unique identifier for each example                                         |
+| metadata             | JSON string containing:                                                   |
+|                      | - `target_pattern`: The pattern that was corrupted during training         |
+|                      | - `degraded_accuracy`: Accuracy of the model trained on corrupted data     |
+|                      | - `improved_accuracy`: Accuracy of the model after training on clean data  |
+|                      | - `improvement`: Delta between degraded and improved accuracy              |
+|                      | - `model_config`: Subject model architecture and hyperparameters           |
+|                      | - `corruption_stats`: Details about label corruption                      |
+|                      | - `selected_patterns`: All patterns in the subject model's training dataset |
+|                      | - `precision`: Model weight precision                                     |
+|                      | - `quantization`: Quantization type applied to weights                    |
+|                      | - `config_signature`: Hash of critical config fields for validation        |
+| classification_prompt | Input prompt with improved model weights and signature |
+| classification_completion | Target completion identifying the pattern         |
+| classification_text | Full concatenated text (prompt + completion)           |
+