license: apache-2.0
language:
- en
task_categories:
- text-classification
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train.jsonl
- split: validation
path: data/validation.jsonl
Commitment statements — synthetic pilot
Small assistant-authored English statements for an experimental binary commitment/decision classifier. Released for reproducibility, not as an independently collected benchmark.
- Training: 118 rows, 59 per label.
- Validation/development: 14 rows, 7 per label.
- The original 16-row test set is deliberately not distributed or evaluated.
- Related contrastive families were kept together during the original split; all 48 extension rows were appended only to training.
- Exact text, ID and group overlap was checked. Semantic topic independence is not guaranteed.
Labels
DECISION_EXPRESSED means an affirmed chosen course of action, firm intention
or commitment. Choosing not to act and explicitly reported past choices count.
NO_DECISION_EXPRESSED covers preferences, requests, questions, possibilities,
predictions, example quotations and completed-action reports without an expressed
choice. This is a broad commitment definition, not a complete logging policy.
Authorship and review
The first pilot contained 100 assistant-authored statements in contrastive families. An extension added 48 fictional, newly worded statements inspired by structural patterns in a user-provided decision log. The source log, names, paths, entry identifiers and project-specific measurements are not included. The extension was authored after inspecting development errors.
The user accepted proceeding after data validation. This is not a claim of
independent expert annotation or review of each label. Original
review_status: human-review-pending fields are retained for provenance rather
than rewritten as expert-approved ground truth.
Extra fields on extension rows, including settled_outcome_candidate, are
proposed review annotations, not separately validated training targets. Some
rows lack these fields; consumers should treat them as optional. Training uses
only text and label.
Load
from datasets import load_dataset
data = load_dataset("TwilightTechie/commitment-statements-pilot")
print(data["train"][0])
Limitations and intended use
Use for reproducing the small pipeline and learning experiment. Do not use validation metrics as estimates of real-world reliability. It is short English text with limited topic and wording diversity; ambiguous, canceled, sarcastic and multi-turn examples are not qualified. Keep independent real examples for future evaluation and collect permission before introducing conversation data.
License: Apache-2.0. The repository preserves the Semantic Router license used
for this experiment's source/data context. No license over the private source
log is asserted. Associated model: TwilightTechie/vela-commitment-classifier-307m.