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README.md

@@ -1,31 +1,70 @@
 ---
-dataset_info:
-  features:
-  - name: condition
-    dtype: string
-  - name: condition_description
-    dtype: string
-  - name: sequence_index
-    dtype: int64
-  - name: text
-    dtype: string
-  - name: n_tokens
-    dtype: int64
-  - name: n_chars
-    dtype: int64
-  - name: n_documents
-    dtype: int64
-  - name: source_files
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 808396
-    num_examples: 40
-  download_size: 245455
-  dataset_size: 808396
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: mit
+tags:
+- prepretraining
+- data-inspection
+- training-samples
 ---
+
+# prepretraining-training-samples-v1
+
+First 10 training sequences (4096 tokens each) for each of the 4 conditions, decoded back to human-readable text. Shows exactly what the model sees during training. Use this to verify data quality, ordering, and condition differentiation.
+
+## Dataset Info
+
+- **Rows**: 40
+- **Columns**: 8
+
+## Columns
+
+| Column | Type | Description |
+|--------|------|-------------|
+| condition | Value('string') | Data scheduling condition: baseline, front-load, constant-mix, or anneal |
+| condition_description | Value('string') | What this condition feeds the model during its first phase |
+| sequence_index | Value('int64') | Index of this sequence within the condition (0 = very first thing the model sees) |
+| text | Value('string') | Decoded text of the 4096-token training sequence (human-readable) |
+| n_tokens | Value('int64') | Number of tokens in this sequence (always 4096) |
+| n_chars | Value('int64') | Character count of the decoded text |
+| n_documents | Value('int64') | Number of documents packed into this sequence (separated by EOS tokens) |
+| source_files | Value('string') | Source .npy file names (first 3) |
+
+
+## Generation Parameters
+
+```json
+{
+  "script_name": "analysis/sample_training_batches.py",
+  "model": "N/A (data inspection, not model output)",
+  "description": "First 10 training sequences (4096 tokens each) for each of the 4 conditions, decoded back to human-readable text. Shows exactly what the model sees during training. Use this to verify data quality, ordering, and condition differentiation.",
+  "hyperparameters": {
+    "sequence_length": 4096,
+    "n_sequences_per_condition": 10,
+    "tokenizer": "allenai/gpt-neox-olmo-dolma-v1_5"
+  },
+  "input_datasets": [
+    "reasoning-degeneration-dev/prepretraining-gold-v1",
+    "reasoning-degeneration-dev/prepretraining-web-v1"
+  ],
+  "experiment_id": "prepretraining",
+  "artifact_type": "input_data",
+  "visualizer_type": "table",
+  "artifact_group": "data-inspection"
+}
+```
+
+## Experiment Documentation
+
+For complete experiment details, see [https://github.com/Zayne-sprague/SC-Research-Notes/tree/main/experiments/prepretraining](https://github.com/Zayne-sprague/SC-Research-Notes/tree/main/experiments/prepretraining)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("reasoning-degeneration-dev/prepretraining-training-samples-v1", split="train")
+print(f"Loaded {len(dataset)} rows")
+```
+
+---
+
+*This dataset is tracked in [reasoning-degeneration-dev/PROJECT-MANIFEST](https://huggingface.co/datasets/reasoning-degeneration-dev/PROJECT-MANIFEST)*