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Datasets:
AdaptiveChunking
/
hnet-chunking-probes

Tasks:
Text Generation
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Text
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json
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< 1K
Tags:
dynamic-chunking
tokenization
hnet
boundaries
training-dynamics
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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Add H-Net dynamic-chunking artefacts

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  1. README.md +51 -0
  2. baseline_en/step_000000.npz +3 -0
  3. baseline_en/step_000500.npz +3 -0
  4. baseline_en/step_001000.npz +3 -0
  5. baseline_en/step_001500.npz +3 -0
  6. baseline_en/step_002000.npz +3 -0
  7. baseline_en/step_002500.npz +3 -0
  8. baseline_en/step_003000.npz +3 -0
  9. baseline_en/step_003500.npz +3 -0
  10. baseline_en/step_004000.npz +3 -0
  11. baseline_en/step_004500.npz +3 -0
  12. baseline_en/step_005000.npz +3 -0
  13. emactl_genreg_s0/000000__de.npz +3 -0
  14. emactl_genreg_s0/000000__el.npz +3 -0
  15. emactl_genreg_s0/000000__en.npz +3 -0
  16. emactl_genreg_s0/000000__fi.npz +3 -0
  17. emactl_genreg_s0/000000__hi.npz +3 -0
  18. emactl_genreg_s0/000000__my.npz +3 -0
  19. emactl_genreg_s0/000000__ru.npz +3 -0
  20. emactl_genreg_s0/000000__ta.npz +3 -0
  21. emactl_genreg_s0/000000__th.npz +3 -0
  22. emactl_genreg_s0/000000__tr.npz +3 -0
  23. emactl_genreg_s0/000000__vi.npz +3 -0
  24. emactl_genreg_s0/000000__zh.npz +3 -0
  25. emactl_genreg_s0/000001__de.npz +3 -0
  26. emactl_genreg_s0/000001__el.npz +3 -0
  27. emactl_genreg_s0/000001__en.npz +3 -0
  28. emactl_genreg_s0/000001__fi.npz +3 -0
  29. emactl_genreg_s0/000001__hi.npz +3 -0
  30. emactl_genreg_s0/000001__my.npz +3 -0
  31. emactl_genreg_s0/000001__ru.npz +3 -0
  32. emactl_genreg_s0/000001__ta.npz +3 -0
  33. emactl_genreg_s0/000001__th.npz +3 -0
  34. emactl_genreg_s0/000001__tr.npz +3 -0
  35. emactl_genreg_s0/000001__vi.npz +3 -0
  36. emactl_genreg_s0/000001__zh.npz +3 -0
  37. emactl_genreg_s0/000002__de.npz +3 -0
  38. emactl_genreg_s0/000002__el.npz +3 -0
  39. emactl_genreg_s0/000002__en.npz +3 -0
  40. emactl_genreg_s0/000002__fi.npz +3 -0
  41. emactl_genreg_s0/000002__hi.npz +3 -0
  42. emactl_genreg_s0/000002__my.npz +3 -0
  43. emactl_genreg_s0/000002__ru.npz +3 -0
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  48. emactl_genreg_s0/000002__zh.npz +3 -0
  49. emactl_genreg_s0/000004__de.npz +3 -0
  50. emactl_genreg_s0/000004__el.npz +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ task_categories: [text-generation]
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+ tags: [dynamic-chunking, tokenization, hnet, boundaries, training-dynamics]
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+ ---
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+
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+ # H-Net chunker boundary probes
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+
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+ Longitudinal boundary decisions for 31 H-Net runs, logged on a **fixed, byte-identical**
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+ FLORES+ probe at every checkpoint. This is the raw material for studying *when* a learned
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+ segmentation stabilises.
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+
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+ ## Layout
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+
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+ `<run>/{step:06d}__{lang}.npz`, plus `<run>/probe_text.jsonl` (the raw probe text, so byte
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+ offsets can be aligned to external gold data).
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+
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+ - **40 log-spaced steps**: 0, 1, 2, 4, 8, 16, 32, 64, 128, 200, then every 200 to 6000.
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+ The early log-spacing is deliberate — uniform 200-step spacing misses the sharp early
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+ transitions, and it cannot be recovered after a run.
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+ - **12 languages**: de, el, en, fi, hi, my, ru, ta, th, tr, vi, zh
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+ - 481 files per run (`jitter_*` runs have 157; they were logged at a coarser cadence).
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+
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+ ### npz keys
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+
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+ | key | shape | meaning |
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+ |---|---|---|
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+ | `mask` | `(L,) uint8` | 1 = a unit boundary falls before this byte |
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+ | `score` | `(L,) float32` | the router's continuous boundary probability |
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+ | `sent_offsets` | `(n_sent+1,) int64` | byte offset of each probe sentence |
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+ | `bpb` | scalar | per-language bits-per-byte at this checkpoint |
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+ | `step` | scalar | training step |
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+ | `b_pad`, `p_pad`, `pad_mask` | `(128, 315)` | per-sentence padded views |
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+
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+ **Known issue:** `mask` and `mask_level1` (and `score`/`score_level1`) are byte-for-byte
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+ identical — the two-level logging wrote the same level twice, so per-level word-vs-morph
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+ typing cannot be done from these dumps. For Thai, `len(mask)` exceeds the probe byte length
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+ by 21 bytes. Both are logging bugs, recorded here rather than silently patched.
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+
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+ ## Caveats worth knowing before you use these
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+
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+ - Boundaries are **phase-locked to a seed-arbitrary sub-character byte position**. Chunk
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+ starts land on a UTF-8 character boundary 29–31% of the time against a 33% chance rate
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+ (verified independently against the span dumps, not just the mask).
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+ - 20 of 36 language-seed pairs have **not** converged by step 6000, so a step-6000 analysis
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+ is measuring an unconverged segmentation for the low-resource half.
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+ - Probe text decoded with `errors="ignore"` no longer byte-aligns to the mask for truncated
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+ sentences (up to 35/128 for `my`); drop those rather than assume alignment.
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+
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+ Companion [models](https://huggingface.co/AdaptiveChunking/hnet-chunking-pilots) and
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+ [results](https://huggingface.co/datasets/AdaptiveChunking/hnet-chunking-results).
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