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ThinkMorph-SSL data release

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.gitattributes CHANGED
@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ text_only/images.jsonl filter=lfs diff=lfs merge=lfs -text
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+ text_only/train.jsonl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,161 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
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+ license_name: per-source-terms
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+ pretty_name: Pretext-270K
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+ task_categories:
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+ - any-to-any
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+ tags:
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+ - thinkmorph-ssl
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+ - primer
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+ - interleaved-reasoning
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+ - spatial-reasoning
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+ configs:
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+ - config_name: preview
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+ data_files:
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+ - split: train
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+ path: preview/*.parquet
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+ ---
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+
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+ # Pretext-270K
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+
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+ Pretext-270K is the Stage 1 data of PRIMER. It trains the Stage 1 model
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+ [ThinkMorph-SSL-Base](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Base), from which
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+ [ThinkMorph-SSL-Instruct](https://huggingface.co/ThinkMorph/ThinkMorph-SSL-Instruct) is trained further on
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+ Instruct-181K. It has **270,896 instruction-free text-image-text traces**. Each trace starts
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+ from a corrupted input image; a textual description follows, then the clean target image,
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+ then a further description.
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+
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+ - **Images:** the target images come from the original source images.
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+ - **Text:** a teacher model (GPT-5) writes the text segments. It first captions the clean
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+ image, then sees the caption and both images, so it knows the target, which the student
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+ does not see in its input.
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+
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+ Stage 1 trains on the train rows only: 270,896 rows here, which is the paper's count. The
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+ 1,600 val rows were not used for training.
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+
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+ ## Composition
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+
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+ | Subset | Rows with images | Text-only rows | Sources |
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+ | --- | ---: | ---: | --- |
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+ | `mixed_100k` | 100,000 | — | VSI-590K pools (YouTube room tours, Hypersim, ARKitScenes, ScanNet, ...), LLaVA-OneVision-1.5 pools, DeepVision-103K |
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+ | `jigsaw_fragments_18k` | 8,085 | 10,266 | Matterport3D (text only), Realsee3D, 360+x |
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+ | `sa1b_55k` | 55,442 | — | SA-1B |
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+ | `general_99k` | 98,703 | — | PixMo-Cap |
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+ | **Total** | **262,230** | **10,266** | |
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+
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+ ### Counts by transformation family (train rows)
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+
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+ | Family | Rows | Paper |
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+ | --- | ---: | ---: |
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+ | Jigsaw restoration | 52,975 | 52,975 |
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+ | Colorization | 36,104 | 36,104 |
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+ | Super-resolution | 36,409 | 36,409 |
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+ | Masked inpainting | 35,950 | 35,950 |
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+ | Rotation/flip recovery | 36,763 | 36,763 |
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+ | Denoising | 36,023 | 36,023 |
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+ | Deformation recovery | 36,672 | 36,672 |
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+ | **Total** | **270,896** | **270,896** |
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+
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+ ### Counts by source (train rows) and the 443-row difference
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+
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+ The paper's source table adds up to **270,453**, which is **443 fewer** than the family total
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+ of 270,896. The data explains the gap:
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+
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+ | Source | Rows in data | Paper |
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+ | --- | ---: | ---: |
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+ | PixMo-Cap | 98,303 | 98,303 |
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+ | SA-1B | 55,042 | 55,042 |
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+ | `mixed_100k`: VSI-590K + LLaVA-OneVision-1.5 + DeepVision-103K | 99,600 | 58,546 + 27,379 + 13,675 = 99,600 |
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+ | Matterport3D | 10,035 | 10,035 |
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+ | Realsee3D | 7,251 | 7,251 |
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+ | 360+x (`source` = `360x_pairs_merged`) | 222 | 222 |
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+ | 360+x (`source` = `stage0`) | 443 | not listed |
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+ | **Total** | **270,896** | **270,453** |
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+
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+ - **The 443 rows.** They are 360+x images whose `source` field reads `stage0`.
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+ - **DeepVision.** It can be counted directly in `mixed_100k`: 8,455 + 5,220 = 13,675.
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+
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+ ## Files
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+
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+ | Path | What it is |
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+ | --- | --- |
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+ | `shards/SHARDS.json`, `shards/data-*.tar` | The release with images: 260,861 train rows, 1,369 val rows and 533,366 images (140.9 GB), in 133 deterministic tar shards |
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+ | `text_only/` | 10,266 rows whose images come from Matterport3D. Their images are not distributed here; see [Rows without images](#rows-without-images) |
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+ | `preview/*.parquet` | The Dataset Viewer preview: a few rows per subset, with images downscaled to at most 768 px |
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+
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+ ## Download and restore
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+
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+ ```bash
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+ python scripts/download_release.py shards/SHARDS.json \
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+ --base-url https://huggingface.co/datasets/ThinkMorph/Pretext-270K/resolve/<COMMIT>/shards --output shards
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+ python scripts/shard_data_release.py restore shards/SHARDS.json shards DATA_ROOT/primer-pretext-270k
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+ ```
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+
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+ (`hf download ThinkMorph/Pretext-270K --repo-type dataset --include "shards/*"` works too.)
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+
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+ - `download_release.py` checks each shard's size and SHA256.
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+ - `restore` checks every shard before extracting it, then checks every file against the
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+ `MANIFEST.json` inside the shards.
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+ - A restored release holds `train.jsonl` (and `val.jsonl` if present), `images/` and
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+ `MANIFEST.json`, which is the layout the BAGEL loader reads.
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+ - The scripts are in the PRIMER code release.
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+
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+ **Check.** Before upload, the restored release went through the BAGEL loader, which decoded
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+ every image: 262,230 usable rows and 533,366 images, result
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+ PASS.
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+
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+ ## Row format
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+
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+ ```json
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+ {"id": "<sha256>", "question": "...", "options": "...", "images": ["images/<sha256>.jpg"],
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+ "reasoning_thought_0": "...", "reasoning_image_0": "images/<sha256>.jpg",
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+ "reasoning_thought_1": "...", "answer": "...", "subset": "<source subset>", "source_split": "train"}
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+ ```
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+
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+ - The supervision text (`question`, `options`, `answer`/`gt_answer`, every
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+ `reasoning_thought_N`) and the image bytes are exactly as in the training copy.
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+ - Image paths are content-hashed relative paths, and `id` is a public id.
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+ - `subset` names the storage subset a row came from, and `source_split` the split it had
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+ there.
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+ - Source metadata fields are removed, because they contain internal storage paths.
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+
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+ ## Rows without images
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+
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+ `text_only/` holds the rows whose images come from **Matterport3D**. Their terms do not allow
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+ us to redistribute the images. Obtain the source data yourself and rebuild the images.
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+
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+ - `text_only/train.jsonl` (and `val.jsonl`): the rows, normalized like the main release
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+ (same public ids), with `subset` and `source_split`. The image fields keep the
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+ **original file names**.
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+ - `text_only/images.jsonl`: one record per original image (`subset`, `split`, `name`). Each
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+ record gives the byte size and MD5 of the exact file used in training, the upstream
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+ dataset and ids, the role and a derivation sentence.
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+ - `text_only/provenance.jsonl`: per row, the reconstruction parameters (for example yaw
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+ angles of paired views, jigsaw grid and tile permutation, original image id and size, take
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+ name and frame rank).
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+
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+ To get the source data:
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+
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+ - **Matterport3D:** sign the Matterport3D Terms of Use. Get the form from
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+ <https://niessner.github.io/Matterport/> and send it to matterport3d@googlegroups.com. The
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+ rows name the Matterport3D panoramas they use.
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+
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+ A rebuilt image may differ in bytes even when it shows the same content. Compare it with the
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+ recorded MD5.
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+
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+ ## Source terms
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+
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+ Each source keeps its own terms. The image release has no blanket license.
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+
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+ - **Code license.** SAM's Apache-2.0 code license does not cover the SA-1B images.
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+ - **360+x.** Its project page shows CC BY-NC-SA 4.0.
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+ - **Realsee3D.** Its Zenodo record lists CC BY 4.0.
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+ - **Matterport3D.** Its rows are text only.
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+
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+ ## Provenance
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+
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+ - **Training copy.** Training read an internal storage copy that is no longer reachable.
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+ - **This release.** It is built from the team's archived copy of the same training folders.
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+ - **What matches.** Train row counts, family counts and source counts match the paper's
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+ records.
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+ - **What could not be checked.** Byte identity with the training copy.
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