Datasets:

lorob
/

CES

pretty_name: Causal Edit Serialization (CES) — Data and checkpoints
license: other
license_name: ces-component-licenses
license_link: https://huggingface.co/datasets/lorob/CES/blob/main/LICENSES.md
language:
  - en
tags:
  - causal-edit-serialization
  - text-editing
  - grammatical-error-correction
configs:
  - config_name: generated-edit-candidates
    data_files:
      - split: train
        path: data/pretraining/gpt5mini-edit-candidates/*.parquet
  - config_name: controlled-repair
    data_files:
      - split: validation
        path: data/evaluations/controlled-edit-val-v1/manifest.jsonl
  - config_name: balanced-controlled-training
    data_files:
      - split: train
        path: >-
          data/posttraining/ces/v1/balanced-sft/controlled-edit-train-balanced-v1.jsonl

Causal Edit Serialization: data and checkpoints

Louis Robinson · CES code and paper project

This repository contains CES training candidates, controlled-repair evaluation data, and recipes for reconstructing the CoEdIT-based inputs locally. It also includes 66 model checkpoints with their configuration metadata. Experiment logs are excluded.

Directory	Contents
`data/pretraining/`	361,768 synthetic-edit candidate rows in 109 Parquet files, plus copy-scan records
`data/evaluations/controlled-edit-val-v1/`	1,000 fixed controlled-repair examples
`data/posttraining/ces/v1/balanced-sft/`	1,500 controlled recovery training examples
`reconstruction/`	CoEdIT example references, hashes, generated edit recipes and a local reconstruction script

The full raw ClimbMix corpus is not included. Its source passages occur in the prepared ClimbMix-based examples and retain the original CC BY-NC 4.0 terms. Five candidate rows containing credential-shaped strings were omitted whole; retained text and edit offsets are unchanged. The released candidate subset is therefore not byte-identical to the historical training input. Arithmetic experiments are excluded.

Why the CoEdIT sentences are not included

We would prefer to distribute all prepared inputs directly, so reproducing CES would require fewer setup steps. However, the CoEdIT dataset card advertises Apache 2.0 while its paper identifies underlying GEC corpora with additional restrictions. In particular, NUCLE's source agreement restricts redistribution, and NAIST Lang-8 limits availability to research and educational purposes. We have not established how the distribution's Apache statement resolves those underlying permissions.

To avoid asserting rights we cannot establish, this release provides references and preparation code instead of republishing the CoEdIT source/target sentences, their serialized copies, or complete generated corruptions. This adds a download and reconstruction step. It is a limitation of the release, not a claim that the CoEdIT authors refused us permission or acted improperly.

Users obtain the source files from the official CoEdIT distribution and remain responsible for complying with the terms applicable to their use. Reconstruction does not remove the original restrictions or create new permissions.

Reconstruct the CoEdIT inputs

Install huggingface_hub and pyarrow in a Python environment. From this repository's downloaded directory, run:

python reconstruction/reconstruct_coedit.py \
  --download \
  --upstream-dir ../ces-upstream/coedit \
  --output ../ces-local

The download uses the official grammarly/coedit repository at commit e9a255c33ef910bc33a9d2b522653fa87521583e. This revision has been verified against the retained CES examples; it is not claimed to be the unrecorded historical download revision. File and text hashes are checked before reconstruction. To use source files you already obtained, omit --download and place the pinned train.jsonl and validation.jsonl files in --upstream-dir.

The script reconstructs, in historical order:

12,756 training, 485 development, 2,000 selection and 5,000 confirmation pairs;
the 5,000 clean confirmation inputs and 12,000 clean corruption-generation targets;
the 6,813 approved generated corruptions, including the historical character edit programs used by the training loader.

Generated corruption recipes contain offsets into source text and added payloads, not complete source sentences. No model/API calls are needed to reconstruct them. The training fields match the historical records; API response traces and raw model-operation prose are omitted. Parquet binary files need not be byte-identical.

Keep upstream downloads and reconstructed outputs outside this public repository. The script enforces separate directories to reduce accidental re-upload. See reconstruction/README.md for rebuilding the optional tokenizer-dependent fine-tuning shards using CES code.

Download only what you need

from huggingface_hub import HfApi, snapshot_download

revision = HfApi().repo_info("lorob/CES", repo_type="dataset").sha
print("Save this artifact revision:", revision)
snapshot_download(
    "lorob/CES", repo_type="dataset", revision=revision,
    local_dir="ces-data",
    allow_patterns=["reconstruction/*", "LICENSES.md", "NOTICE.md", "licenses/*"],
)

Use data/pretraining/*, data/evaluations/* or data/posttraining/* in allow_patterns for those components. SHA256SUMS covers every released file except itself and manifest.jsonl; verify a complete download with sha256sum -c SHA256SUMS from its directory.

Licences and attribution

CES reconstruction code is MIT; original annotations, edit recipes and documentation are CC BY 4.0, limited to rights held by the author. ClimbMix source text retains CC BY-NC 4.0. These are component-specific permissions, not a blanket unrestricted licence. See LICENSES.md and NOTICE.md.

Checkpoints

checkpoints/ contains 48 pretrained/CoEdIT-finetuned method-grid weights, 9 final CES RL weights, 6 matched clean-AR references, and 2 pointer-ablation weights. Each model_*.pt has its adjacent meta_*.json. Total size is approximately 132.9 GiB; use selective downloads for the model you need. No optimizer states or training logs are included.

These custom checkpoints require CES code and the canonical ces32k_v1 tokenizer; they are not Transformers AutoModel repositories. The tokenizer is a separate prerequisite and is not included in this checkpoint upload. For example, select checkpoints/rl-posttraining/ces/d12/posttrain-seed-42/* in the download example above for the smallest final CES RL model.

Original CES weights are CC BY 4.0 to the extent of the author's rights; this does not relicense training text or establish permissions for all downstream uses.

Checkpoint identities

checkpoints/models.json lists model IDs, download paths and complete training lineages. The d12 RL family uses a separate pretrained base at checkpoints/rl-posttraining/ces/d12/pretrained/model_074800.pt; the d12 methods-grid pretrained checkpoint is a different run. Adapted-CES includes clean pretraining and CES adaptation within one experiment; its internal clean-stage weights are not distributed.