Buckets:
| # Policy classifier: progress snapshot, 5 November 2026 | |
| Everything from the 5 November training session on one H100 80GB: code, datasets, every checkpoint, all | |
| logs, local W&B run files, the report and the experiment catalogue. The layout mirrors the working | |
| repository, so every path in `CATALOGUE.md` and `training/README.md` is valid from this folder. | |
| Start with **`REPORT.md`** (results) and **`CATALOGUE.md`** (which run lives where, with W&B links). | |
| ## What is where | |
| | Path | Contents | | |
| | --- | --- | | |
| | `REPORT.md` | Experiment report: hypothesis, configs, all result tables, and the natural-vs-edited confound analysis. The next-steps section is still to be agreed | | |
| | `CATALOGUE.md` | Every dataset and run, with config, output folder, log files and W&B run link | | |
| | `CLAUDE.md` | Working rules for the coding assistant used in this project | | |
| | `training/` | Training code. `engine.py` is the shared loop; `train_modernbert.py`, `train_qwen.py`, `train_qwen35.py` are the model scripts; `smoke.py` is the pre-run check and batch profiler; `evaluate.py` / `predict.py` score checkpoints; `*.yaml` are the configs. `training/README.md` has every command | | |
| | `scripts/` | `resplit_by_domain.py` (builds the domain split), `run_queue*.sh` (sequential GPU queues used for the six runs), `log_checkpoints_wandb.py` (uploads checkpoints and reports to W&B), `collect_results.py` (prints every number in the report) | | |
| | `policy_v2/` | Original export: 27,000 train / 3,000 validation / 7,023 test (unseen domains), plus `manifest.json` and `artifact_flags.json` | | |
| | `policy_v2_domain/` | Dataset used by all runs: train and validation pooled and re-split per domain (26,925 / 3,075), same test set; `manifest.json` lists the validation cases | | |
| | `checkpoints/<run>/` | One folder per run (see table below) | | |
| | `*.log` (top level) | Training logs (`modernbert.log`, `qwen.log`, `qwen35.log`, and `*_2ep.log`), smoke-test logs (`smoke_*.log`) and queue logs (`queue*.log`) | | |
| | `wandb/` | Local W&B run folders: config, console output, summary, media tables (logged errors) and debug logs. The same data is on W&B | | |
| | `environment/` | `pip freeze` of the system Python (transformers 4.57.3; ModernBERT and Qwen3 runs) and of the separate Qwen3.5 environment (transformers 5.20.2, peft 0.21.2, sharing the system torch 2.8.0+cu128) | | |
| ## Runs | |
| | Run folder | Model | Epochs | Status | | |
| | --- | --- | ---: | --- | | |
| | `modernbert-large-30k` | ModernBERT-large | 1.5 | complete | | |
| | `modernbert-large-30k-2ep` | ModernBERT-large | 2 | complete | | |
| | `qwen3-0.6b-30k` | Qwen3-0.6B | 1.5 | complete | | |
| | `qwen3-0.6b-30k-2ep` | Qwen3-0.6B | 2 | complete | | |
| | `qwen3.5-0.8b-30k` | Qwen3.5-0.8B (text decoder) | 1.5 | complete | | |
| | `qwen3.5-0.8b-30k-2ep` | Qwen3.5-0.8B (text decoder) | 2 | stopped at step 210 of 842 (first quarter checkpoint only) | | |
| Inside each run folder: | |
| | File / folder | Contents | | |
| | --- | --- | | |
| | `checkpoint-<k>of4-step<N>/` | LoRA adapter + trained classification head + tokenizer + `input_format.json`, at each quarter of training | | |
| | `final/` | Same as the last quarter checkpoint (it was also the best on validation in every run) | | |
| | `smoke_adapter/` | Adapter from the 10-example smoke test (save/reload check only, not a trained model) | | |
| | `resolved.yaml` | The exact config trained (batch settings chosen by the smoke test) | | |
| | `requested.yaml` / `training_config.json` | Config as requested / as passed to training | | |
| | `smoke_summary.json`, `profile_progress.json` | Smoke-test result and batch-size profiling | | |
| | `validation_report.json`, `test_ood_report.json` | Metrics, confusion matrix, classification report, and natural/edited breakdown | | |
| | `validation_predictions.jsonl`, `test_ood_predictions.jsonl` | Every row with its predicted label and class probabilities | | |
| | `trainer_state.json`, `train_results.json`, `all_results.json` | Trainer log history (per-checkpoint validation metrics, losses) | | |
| | `hardware_metrics.json` | GPU, peak memory, training wall time | | |
| | `wandb_run.json` | W&B run id and URL | | |
| Loading a checkpoint needs the base model from the Hugging Face Hub (`answerdotai/ModernBERT-large`, | |
| `Qwen/Qwen3-0.6B`, `Qwen/Qwen3.5-0.8B`); the exact revision is recorded in `input_format.json`. | |
| Qwen3.5 checkpoints must be loaded from a transformers 5 environment, as described in `training/README.md`. | |
| ## Left out on purpose | |
| - Hugging Face model cache and pip/uv caches: re-downloadable. | |
| - The Qwen3.5 virtual environment: machine-specific; rebuild it from `environment/` and `training/README.md`. | |
| - W&B's binary sync files (`*.wandb`) and temporary folders: raw upload streams that also contain the | |
| account email; their content is already on W&B. | |
| - Python bytecode and notebook checkpoint folders. | |
| - Credentials: none are included. The account email and pod hostname inside the local W&B run files | |
| were replaced with `<redacted-email>` and `<redacted-host>`. | |
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