Reconstructing the historical CoEdIT inputs
specification.json pins the official upstream files and maps reference recipes
to local output paths. IDs are source-distribution IDs; SHA256 checks bind each
reference to its exact source or target string after instruction-prefix removal.
manifest-*.jsonl and clean-shard-*.jsonl contain metadata and references only.
corruptions.jsonl stores source references, generated additions and character
edit programs. A slice is a half-open character interval into the separately
obtained source string. In inverse-edit payloads, slices refer to the reconstructed
corrupted string. These recipes do not include full sentence text or token IDs
that encode the original sentences.
Run reconstruct_coedit.py as described in the root README. It recreates the
training/evaluation pairs, identity inputs, clean target shards and approved
corruption training fields. It does not re-run GPT generation or promise the
same Parquet bytes. Raw API traces and model-operation prose are not reconstructed.
Optional fine-tuning corpus serialization
With the CES code environment and canonical tokenizer already installed, set
NANOCHAT_BASE_DIR to a runtime directory containing tokenizer/. From the CES
code checkout, use the reconstructed manifests:
uv run ces prepare corpus \
--train-manifest ../ces-local/data/evaluations/coedit-gec-transfer-v1/train.jsonl \
--validation-manifest ../ces-local/data/evaluations/coedit-gec-v1/development.jsonl \
--output-dir ../ces-local/data/finetuning/coedit-gec-transfer-corpus \
--train-shards 8 --seed 20260727
This recreates the eight training shards and development shard in raw/ and
candidates/. Canonical character and token edit programs are derived locally.
The tokenizer is an external prerequisite for this optional step; it is not
included in the data-only release. The text reconstruction step needs no tokenizer.