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README.md

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+# mmlu (filtered)
+
+Filtered version of [cais/mmlu](https://huggingface.co/datasets/cais/mmlu), produced as part of the `assistant_core_midtraining` data mixture.
+
+## Source Dataset
+
+- **Original HuggingFace path**: `cais/mmlu`
+- **Disjoint train/val splits**: yes
+- **train split**: 1 subset(s) from `train` split
+- **val split**: 57 subset(s) from `test` split
+
+## Filtering Methodology
+
+Each example in the original dataset was reviewed by an LLM (GPT-OSS-120B) which assessed quality across several dimensions including factual accuracy, formatting consistency, and instructional clarity. Examples that failed review were collected into a blocklist keyed by source name and original row index.
+
+The filtered dataset was produced by:
+
+1. Loading the original dataset from HuggingFace
+2. Stamping each row with its original index (`__source_orig_idx__`) for traceability
+3. Removing all rows whose index appears in the blocklist
+4. Validating that row counts match expectations exactly
+5. Spot-checking a random sample of surviving rows against the original to verify data integrity
+
+## Filtering Impact
+
+| Split | Original Rows | Removed | Filtered Rows | % Removed |
+|-------|--------------|---------|---------------|-----------|
+| train | 99,842 | 6,928 | 92,914 | 6.94% |
+| val | 14,042 | 0 | 14,042 | 0.00% |
+
+## Schema
+
+All original columns are preserved. One column is added:
+
+- `__source_orig_idx__`: The row's index in the original (unfiltered) dataset. This provides complete lineage back to the source for debugging and future analysis.
+
+**Note**: The original `train` split had a nested structure (`subsplit_name: "train"`). This has been flattened into top-level columns. When updating `data_source_configurations.py` to point to this repo, remove the `subsplit_name` field from the `train` split config.
+
+## Provenance
+
+- **Blocklist**: `data/instruct_mix/train/review_results/blocklist_proposed_20260218_232356.jsonl`
+- **Mixture**: `assistant_core_midtraining`
+- **Generated**: 2026-02-20T12:05:03.203204+00:00

train/filtered.parquet

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val/filtered.parquet

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