--- license: apache-2.0 language: en size_categories: - 10K` for template-generated rows - `raw_text` (eval only) — original text before prefix wrap - `note` (eval only) — curator's category note Label ID mapping: ``` {"APPROVE": 0, "DECLINE": 1, "HOLD": 2, "COUNTER_SIZE": 3, "COUNTER_PRICE": 4, "UNCLEAR": 5} ``` ## Structural prefix scheme Every row's `text` field starts with three tags carrying chat context: ``` [dm|group][reply_to:N|no_reply_to][in_flight:K] ``` These give the model context that pure text would not — "yes" with 1 proposal in flight is APPROVE; "yes" with 3 in flight and no quote-reply is structurally ambiguous and trained as UNCLEAR. The prefix is concatenated into the input string and tokenized as regular subword pieces — no special-token registration required. ## Variation dimensions Each of the 1,047 human-written seeds expands to ~24 training rows via two stacked variation axes: **1. Surface variants (6 per seed, randomly sampled):** - Case: lowercase / title / uppercase / as_is - Punctuation: none / `.` / `!` / `?` - Whitespace: none / leading 2 spaces / trailing 2 spaces **2. Structural prefix variants (4 per surface form):** - `(reply_to: present, in_flight: 1)` - `(reply_to: present, in_flight: 2)` - `(reply_to: present, in_flight: 3)` - `(reply_to: absent, in_flight: 1)` — single-default rule Additionally, the UNCLEAR class includes **480 template-generated rows** using a 100-ticker pool (mega-caps, ETFs, ADRs, mid-caps, speculative names) and 20 multi-intent templates ("approve {A} not {B}", "yes {A} no {B}", "swap {A} for {B}", etc.). This teaches the model the multi-intent PATTERN across diverse ticker tokens without bias toward any specific ticker. The model generalizes to ticker pairs it has never seen in training. ## Zero-leakage verification Eval phrases are NEVER present in training: ```python import json training_seeds = set( json.loads(l)["seed"].strip().lower() for l in open("training.jsonl") ) overlaps = sum( 1 for l in open("eval.jsonl") if json.loads(l)["raw_text"].strip().lower() in training_seeds ) assert overlaps == 0 ``` This is enforced on every regeneration. The eval set's adversarial value depends on the model learning patterns it can generalize to novel phrasings, not memorizing surface forms. ## Design decisions **Author-written seeds, not LLM-generated.** The 1,047 seed phrases reflect natural reply phrasings a human would actually type — short, casual, sometimes with typos, sometimes with adjacency to system commands. Seeds were hand-written and iteratively reviewed against eval failures. **Programmatic augmentation, not LLM expansion.** Surface variation is deterministic — case/punctuation/whitespace flipping plus structural-prefix combinations. No language model generates content. This keeps the dataset auditable and reproducible. **Six labels with COUNTER_PRICE separated from COUNTER_SIZE.** Earlier prototypes used five labels. The sixth (COUNTER_PRICE) was added because resize ("size 10") and reprice ("at $49") require different downstream extraction — share count vs limit price — and conflating them would force the consumer to disambiguate post-classification. **Balanced classes by design.** All classes are within 4,080–4,608 rows (~13% spread). Class weighting in training becomes functionally uniform, simplifying the training loss and removing a configuration knob. **Eval set adversarial-leaning.** The held-out 175 examples lean toward edge cases that surfaced during iterative training — casual register ("yup", "yeppers"), action metaphors ("press the button"), negation-as-deferral ("not now", "dont fire yet"), and multi-intent ambiguity ("yes but actually no"). Easy canonical examples are under-represented in eval intentionally. ## Intended downstream task Train a classifier that routes short free-form text replies on trade proposals into one of 6 action intents: ``` APPROVE execute as proposed DECLINE kill the proposal HOLD defer the decision COUNTER_SIZE execute at a different share count COUNTER_PRICE execute at a different limit price UNCLEAR cannot safely commit (refuse) ``` The data assumes the consumer pairs the model with a confidence threshold, deterministic safety rails (budget/position limits), and a fallback confirmation mechanism. The dataset is NOT intended for standalone-classifier deployments without those layers. ## Loading ```python from datasets import load_dataset ds = load_dataset("DoDataThings/trade-decision-classifier-v1-dataset") ds["train"] # 25,200 rows ds["test"] # 175 rows (held out, used for eval — never trained on) ``` Or directly: ```python import json train = [json.loads(l) for l in open("training.jsonl")] test = [json.loads(l) for l in open("eval.jsonl")] ``` ## Trained model A reference classifier trained on this dataset: [DoDataThings/distilbert-trade-decision-classifier-v1](https://huggingface.co/DoDataThings/distilbert-trade-decision-classifier-v1). Reaches macro F1 0.954 on the held-out eval set with zero high-confidence misclassifications. ## Limitations - **Surface coverage.** The 1,047 human-written seeds reflect representative reply phrasings curated through 12 training iterations against an adversarial eval set. Real production replies will still exceed seed coverage on long-tail phrasing; expect ~5% of replies to land at the confidence floor and route to confirmation fallback. - **Class boundaries on multi-intent inputs** ("approve but cut size") are ambiguous; eval labels reflect a single curator's judgment. - The "more shares" eval row is architecturally ambiguous (resize intent without a number) — a model trained on this dataset will correctly emit UNCLEAR rather than COUNTER_SIZE, which is safe behavior but reads as a miss against the eval label. - **English-only.** No localization in v1. ## License Apache 2.0.