thread tokenization and chunking
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Joey Eamigh
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docs/STATUS.md
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docs/STATUS.md
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# Project Status — 2026-03-29
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## What's Done
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### Data Pipeline
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- [x] 72,045 paragraphs extracted from ~9,000 10-K filings + 207 8-K filings
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- [x] 14 filing generators identified, quality metrics per generator
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- [x] 6 surgical patches applied (orphan words + heading stripping)
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- [x] Quality tier system: clean (80.7%), headed (10.3%), degraded (6.0%), minor (3.0%)
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- [x] Embedded bullet detection (2,163 paragraphs flagged degraded, 0.5x sample weight)
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- [x] All data integrity rules formalized (frozen originals, UUID-linked patches)
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### GenAI Labeling (Stage 1)
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- [x] Prompt v2.5 locked after 12+ iterations
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- [x] 3-model panel: gemini-flash-lite + mimo-v2-flash + grok-4.1-fast
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- [x] 150,009 annotations completed ($115.88, 0 failures)
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- [x] Orphan word re-annotation: 1,537 paragraphs re-run ($3.30), merged into `stage1.patched.jsonl`
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- [x] Codebook v3.0 with 3 major rulings
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### DAPT Corpus
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- [x] 14,568 documents, ~1.056B tokens, cleaned (XBRL, URLs, page numbers stripped)
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- [x] Training pipeline verified end-to-end (PyTorch 2.10, CUDA, ModernBERT loads, tokenization works)
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- [x] Config: 8192 seq_len, batch=1, grad_accum=32, 1 epoch, bf16
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- [x] Procedure documented in `docs/DAPT-PROCEDURE.md`
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### Documentation
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- [x] `docs/DATA-QUALITY-AUDIT.md` — full audit with all patches and quality tiers
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- [x] `docs/EDGAR-FILING-GENERATORS.md` — 14 generators with signatures and quality profiles
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- [x] `docs/DAPT-PROCEDURE.md` — pre-flight checklist, commands, monitoring guide
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- [x] `docs/NARRATIVE.md` — 11 phases documented through DAPT corpus prep
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## What's In Progress
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### DAPT Training (~4-8h)
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```bash
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cd python && bun run py:train dapt --config configs/dapt/modernbert.yaml
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```
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No dependencies. Run anytime.
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### Human Labeling (139/1,200)
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- 3 of 6 annotators started: 68 + 50 + 21 paragraphs completed
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- Deployed via labelapp with quiz gating + warmup
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- Each annotator needs 600 paragraphs (BIBD assignment)
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## What's Next (in dependency order)
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### 1. TAPT (~2-3h, blocked on DAPT)
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Continue MLM on 72K Item 1C paragraphs using the DAPT checkpoint.
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```bash
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bun run py:train dapt --config configs/dapt/modernbert.yaml \
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--model-path ../checkpoints/dapt/modernbert-large/final \
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--data-path ../data/paragraphs/paragraphs-clean.patched.jsonl \
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--output-dir ../checkpoints/tapt/modernbert-large --stage tapt
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```
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### 2. Fine-tuning pipeline (no blockers — can build now)
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Build the dual-head classifier (7-class category + 4-class specificity) with:
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- Shared ModernBERT backbone + 2 linear classification heads
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- Sample weighting from quality tiers (1.0 clean/headed/minor, 0.5 degraded)
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- Confidence-stratified label assembly (unanimous → majority → judge)
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- Train/val/test split with stratification
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- Ablation configs: base vs +DAPT vs +DAPT+TAPT
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### 3. Judge prompt v3.0 update (no blockers — can do now)
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Update `buildJudgePrompt()` with codebook v3.0 rulings:
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- Materiality disclaimers → Strategy Integration
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- SPACs → None/Other
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- Person-vs-function test for Management↔RMP
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Then re-bench against gold labels.
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### 4. Training data assembly (blocked on judge + human labels)
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Combine all annotation sources into final training dataset:
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- Unanimous Stage 1 labels (35,204 paragraphs, ~97% accuracy)
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- Calibrated majority labels (~9-12K, ~85-90%)
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- Judge high-confidence labels (~2-3K, ~84%)
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- Judge low-confidence → downweight or exclude
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- Quality tier sample weights applied
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### 5. Judge production run (blocked on human gold labels)
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Run judge on ~409 unresolved + flagged majority cases. Validate against expanded gold set from human labels.
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### 6. Fine-tuning + ablations (blocked on steps 1-4)
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7 experiments: {base, +DAPT, +DAPT+TAPT} × {with/without SCL} + best config.
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### 7. Evaluation + paper (blocked on everything above)
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Full GenAI benchmark (9 models) on 1,200 holdout. Comparison tables. Write-up.
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## Parallel Tracks
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```
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Track A (GPU): DAPT ──→ TAPT ──→ Fine-tuning ──→ Eval
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↑
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Track B (API): Judge v3 → Judge run ───┤
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↑
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Track C (Human): Labeling (139/1200) → Gold set validation
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↑
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Track D (Code): Fine-tune pipeline build ┘
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```
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Tracks A and D can proceed now. Track B can start (prompt update) but production run waits for Track C. Everything converges at fine-tuning.
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## Key File Locations
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| What | Where |
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|------|-------|
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| Patched paragraphs | `data/paragraphs/training.patched.jsonl` (49,795) |
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| Patched annotations | `data/annotations/stage1.patched.jsonl` (150,009) |
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| Quality scores | `data/paragraphs/quality/quality-scores.jsonl` (72,045) |
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| DAPT corpus | `data/dapt-corpus/shard-*.jsonl` (14,756 docs) |
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| DAPT config | `python/configs/dapt/modernbert.yaml` |
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| Training CLI | `python/main.py dapt --config ...` |
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@ -42,34 +42,46 @@ def train(config: DAPTConfig) -> None:
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)
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print(f" Model parameters: {model.num_parameters() / 1e6:.0f}M")
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# Load and prepare data
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print(f" Loading corpus from {config.data.corpus_path}...")
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dataset = load_corpus(config.data.corpus_path, config.data.text_field)
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print(f" Raw documents: {len(dataset):,}")
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# Load and prepare data (with disk cache to avoid re-tokenizing on resume)
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output_dir = Path(config.training.output_dir)
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cache_dir = output_dir / ".data_cache"
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if cache_dir.exists():
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print(f" Loading cached dataset from {cache_dir}...")
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from datasets import DatasetDict
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split = DatasetDict.load_from_disk(str(cache_dir))
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print(f" Train: {len(split['train']):,} | Val: {len(split['test']):,}\n")
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else:
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print(f" Loading corpus from {config.data.corpus_path}...")
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dataset = load_corpus(config.data.corpus_path, config.data.text_field)
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print(f" Raw documents: {len(dataset):,}")
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# Filter tiny documents (cover pages, empty filings)
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min_chars = 10_000
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before = len(dataset)
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dataset = dataset.filter(lambda x: len(x[config.data.text_field]) >= min_chars)
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filtered = before - len(dataset)
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if filtered > 0:
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print(f" Filtered {filtered} docs < {min_chars:,} chars → {len(dataset):,} remaining")
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# Filter tiny documents (cover pages, empty filings)
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min_chars = 10_000
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before = len(dataset)
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dataset = dataset.filter(lambda x: len(x[config.data.text_field]) >= min_chars)
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filtered = before - len(dataset)
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if filtered > 0:
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print(f" Filtered {filtered} docs < {min_chars:,} chars → {len(dataset):,} remaining")
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print(f" Tokenizing and chunking to {config.data.max_seq_length} tokens...")
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chunked = tokenize_and_chunk(
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dataset,
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tokenizer,
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text_field=config.data.text_field,
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max_seq_length=config.data.max_seq_length,
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)
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print(f" Training sequences: {len(chunked):,}")
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print(f" Tokenizing and chunking to {config.data.max_seq_length} tokens...")
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chunked = tokenize_and_chunk(
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dataset,
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tokenizer,
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text_field=config.data.text_field,
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max_seq_length=config.data.max_seq_length,
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)
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print(f" Training sequences: {len(chunked):,}")
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# Train/val split
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split = chunked.train_test_split(
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test_size=config.data.validation_split,
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seed=config.training.seed,
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)
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print(f" Train: {len(split['train']):,} | Val: {len(split['test']):,}\n")
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# Train/val split
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split = chunked.train_test_split(
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test_size=config.data.validation_split,
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seed=config.training.seed,
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)
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print(f" Train: {len(split['train']):,} | Val: {len(split['test']):,}")
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# Cache to disk for fast resume
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split.save_to_disk(str(cache_dir))
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print(f" Cached to {cache_dir}\n")
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# Data collator — handles dynamic masking each epoch
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collator = DataCollatorForLanguageModeling(
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