BatuhanECB/vibethinker-3b-finance-sftmini-data-public-version

VibeThinker-3B Finance-Reader — SFT Training Data · PUBLIC-SAFE subset

🟢 This is vibethinker-3b-finance-sftmini-data-public-version — the redistribution-safe slice of the full vibethinker-3b-finance-sftmini-data dataset, containing only US-government public-domain sources (SEC EDGAR family + Federal Register). Same schema, same pipeline, same teacher — just the legally shareable rows. (Currently private; intended to be made public.)

The supervised fine-tuning dataset ("sftmini": a deterministic 10k-sample draw, here filtered to its public-domain subset) used to train the model family in BatuhanECB/vibethinker-3b-finance-sft-quantized-v1. It teaches a 3B model to read one piece of financial text and emit strict, schema-valid JSON describing what the text implies ex-ante — text-only, the model never sees future prices.

• 4,295 train + 204 held-out eval examples (single-turn user→assistant).
• Teacher-distilled: every label written by a Qwen3.6-35B-A3B reasoning teacher under guided-JSON decoding (no human gold labels exist anywhere in this set).
• Ex-ante target + realized-return sidecar: the SFT target is text-only; a separate, non-input sidecar carries forward excess returns for a future RLVR stage (it never enters the prompt or the target).

🔗 Links

• 🤖 Model trained on this data: BatuhanECB/vibethinker-3b-finance-sft-quantized-v1 — we trained that VibeThinker-3B finance reader with the full dataset; this public subset is the shareable part of it.
• 🗃️ Full dataset (private): BatuhanECB/vibethinker-3b-finance-sftmini-data — all 9,588 records (includes API/news sources that cannot be redistributed).
• 💻 Code, full pipeline & reports: github.com/batuhan3526/vibe_train — how this data was selected, teacher-labeled, and turned into the model.

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⚖️ Why this is a filtered subset (read this)

The model was trained on a 9,588-record set drawn from many feeds. Not all of those feeds can be redistributed: news/aggregator APIs (Finnhub, NewsAPI, GNews, RSS full-text) carry copyrighted article text and their Terms of Service prohibit re-sharing the content — that restriction is a contract, and it stands even when the text is paraphrased or turned into labels. So to publish a dataset anyone can safely use, we kept only sources that are U.S.-Government works in the public domain:

kept ✅ (public domain)	dropped ❌ (ToS / copyright)
sec_fulltext — SEC EDGAR filing text	finnhub_news, newsapi, gnews, rss_news (copyrighted news, no-redistribution ToS)
sec_enforcement — SEC enforcement actions	policy_uncertainty, earnings, fear_greed, cnn_fear_greed, google_trends, election_polls (provider ToS / unclear provenance — dropped to stay fully defensible)
sec_edgar — EDGAR filing metadata
sec_rss — SEC RSS feed
federal_register — Federal Register notices

This removes ~55% of the rows (mostly the news text) but leaves a clean, citation-free, public-domain core. The full private dataset above still contains everything for our own reproducibility.

⚠️ Side effect — this subset is no-signal-heavy. SEC filings are mostly administrative, so ~83% of rows are neutral / market_relevance=none. It's excellent for teaching no-signal handling and clean JSON structure, but has less directional diversity than the full set (which mixes in market-moving news). If you fine-tune only on this, expect a strong neutral prior. See Label distribution below.

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Record format

{
  "messages": [
    {"role": "user",      "content": "You are a financial analyst. Read ONLY the text below and return STRICT JSON ... <the financial text>"},
    {"role": "assistant", "content": "{\"reasoning\":\"...\",\"event_type\":\"...\",\"entities\":[...],\"sector_context\":[...],\"market_relevance\":\"...\",\"direction\":\"...\",\"score_bucket\":0.5,\"confidence\":0.9}"}
  ],
  "id": "sec_fulltext-1009",
  "source": "sec_fulltext",
  "split_group": "HCA|2008-01-14|sec_fulltext",
  "sample_weight": 1.0,
  "realized_outcome": { "level": "single-name", "anchor": "2008-01-14", "ticker": "HCA", "sector_etf": "...",
    "horizons": { "h5": {"ret": ..., "spy_excess": ..., "sector_excess": ...}, "h20": {...}, "h60": {...} } },
  "alignment": { "ex_ante_dir": 0, "realized_dir": 1, "realized_excess": 0.0123, "agree": false, "weight": 1.0, "tag": "...", "use_for_rlvr": true }
}

top-level key	role
messages	the only thing SFT uses. user = prompt, assistant = target JSON.
id, source	provenance (one of the 5 public-domain feeds above).
split_group	TICKER|anchor-date|type — document-level split key (see Splits).
sample_weight	loss weight: 1.0 (4,058 recs), 0.3 (236, teacher reasoning auto-repaired), 0.15 (1 extreme).
realized_outcome, alignment	RLVR sidecar only — point-in-time forward returns; never part of SFT input or target.

• SFT target = messages[assistant] — the ex-ante JSON only. Train with prompt/completion masking (completion_only_loss=true): mask the user turn, compute loss on the assistant turn.
• The sidecar's realized_outcome.horizons carries T+{5,20,60}-day returns, each with ret (raw), spy_excess (vs SPY) and sector_excess (vs the name's sector ETF), point-in-time. alignment summarizes whether the ex-ante call matched the realized move (use_for_rlvr=false when there is no usable label).

Output schema (the assistant JSON)

field	values
reasoning	2–4 concise third-person factual sentences (no first person / no deliberation)
event_type	free text (filing, regulatory, enforcement, metadata, …)
entities	tickers/orgs explicitly named in the text only
sector_context	inferred sectors/themes (not entities)
market_relevance	none / low / medium / high
direction	bearish / slightly_bearish / neutral / slightly_bullish / bullish
score_bucket	one of -1.0, -0.5, -0.2, 0.0, 0.2, 0.5, 1.0 (graded by materiality)
confidence	0.0–1.0 (quality of evidence, not strength of direction)

Enforced invariants: market_relevance=none → score_bucket=0.0 and direction=neutral; direction is derived from score_bucket (no sentiment/score contradiction); entities filtered to in-text mentions.

Label distribution (train, n=4,295)

direction — neutral 3,553 (83%) · slightly_bullish 366 (9%) · bearish 148 (3%) · slightly_bearish 125 (3%) · bullish 103 (2%).

market_relevance — none 3,553 (83%) · medium 344 (8%) · high 254 (6%) · low 144 (3%). (none count == neutral count — the invariant above.)

score_bucket — 0.0: 3,553 · 0.2: 366 · -0.2: 125 · -0.5: 117 · 0.5: 91 · -1.0: 31 · 1.0: 12.

confidence — median 0.90, mean 0.72. reasoning length — median 51 words (p90 70, max 152). Long sec_fulltext chunks dominate the input-length tail (serve/read at ≥8192 context).

RLVR sidecar coverage (informational — not used by SFT)

use_for_rlvr=true on 568 (13%) records (most public-domain rows are filings with no clean single-name return label). Horizon labels present: h5: 2,943 · h20: 2,922 · h60: 2,884. Each horizon holds {ret, spy_excess, sector_excess}.

Sources & composition (train, public-safe only)

sec_fulltext 2,376 · sec_enforcement 1,392 · sec_edgar 486 · federal_register 26 · sec_rss 15 (total 4,295). Eval (204): sec_fulltext 123 · sec_enforcement 65 · sec_edgar 14 · federal_register 1 · sec_rss 1.

How it was built

1. A deterministic, source-stratified 10,000-record sample (seed 42) is drawn from a ~1 GB financial corpus.
2. The 35B teacher reads each text (text-only, ex-ante) under guided-JSON decoding and writes the schema above. Quality is enforced by schema validators + normalization — no human gold labels.
3. Realized excess returns are attached as a separate sidecar (point-in-time; the future never enters the text).
4. Cleaning / validation / document-level splitting → 9,588 train / 411 eval; this release keeps only the public-domain subset → 4,295 train / 204 eval.

Splits

Document-level, not record-level: the split key is split_group = ticker|anchor|type, so multiple chunks of the same filing never straddle train/eval (prevents leakage on long sec_fulltext documents).

Hygiene fixes applied (verified)

• No-signal neutrality — administrative/metadata text forced to 0.0 / neutral (kills positive-default bias).
• Junk filtered — literal placeholder rows, pre-1995 dates, malformed filings.
• (The earnings ticker-disambiguation fix from the full set does not apply here — earnings rows are not in this subset.)

Loading

from datasets import load_dataset
ds = load_dataset("BatuhanECB/vibethinker-3b-finance-sftmini-data-public-version")  # private for now → pass token=...
train, eval = ds["train"], ds["eval"]
import json
ex = train[0]
prompt = [m for m in ex["messages"] if m["role"] == "user"][0]["content"]
target = json.loads([m for m in ex["messages"] if m["role"] == "assistant"][0]["content"])

What the trained model actually does (measured on the full 411-eval + a 500-doc unseen set)

• 100% schema-valid JSON (base VibeThinker-3B: ~1% — the task is learned from scratch).
• Tracks the 35B teacher's reading closely: direction-agreement 86%, sign 91%, bucket 84%.
• Field-level vs teacher: market_relevance 80% · event-type family 82% · direction (3-way) 88% · entities 73%; strongest on structured sources (SEC/regulatory 97–100%), weakest on truncated sec_fulltext (69%).

Note: the released model was trained on the full set; this public subset reproduces the public-domain portion of that training data, not a separately-trained model.

License & attribution

• Source text: U.S.-Government works (SEC EDGAR, Federal Register) — public domain.
• Labels (assistant JSON) + sidecar: generated by us via a teacher model; released under CC-BY-4.0.
• Please cite this repo and github.com/batuhan3526/vibe_train if you use it.

Caveats

• Teacher-labeled, not human gold — the quality ceiling is the 35B teacher's reading.
• No-signal-heavy (~83% neutral) — by construction (SEC filings); less directional diversity than the full set.
• Ex-ante reading, not return prediction — models trained on this are at chance on realized returns (so is the 35B teacher and a 12B model we tested); sentiment ≠ alpha. The sidecar is for RLVR, not a supervised return label.
• Survivorship (returns universe = current S&P 500) biases any predictive use — not the reading task.
• Not investment advice.

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🤖 Model: BatuhanECB/vibethinker-3b-finance-sft-quantized-v1 · 🗃️ Full (private) dataset: ...-sftmini-data · 💻 Code: github.com/batuhan3526/vibe_train. Built 2026-06.