binomial-shannon-2
A financial news characterizer with two modes: it reads ticker-tagged company news the way binomial-shannon-1 does (19 structured features), and reads macro news (central banks, inflation, rates, FX, commodities, geopolitics) with a dedicated 35-output macro head bank. A built-in router selects the right head set per article. ~15-30 ms on CPU.
Quick start
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained("BinomialTechnologies/binomial-shannon-2")
model = AutoModel.from_pretrained("BinomialTechnologies/binomial-shannon-2",
trust_remote_code=True)
inputs = tok("[FEED: reuters] [SITE: reuters.com] [DATE: 2026-03-18]\n\n"
"TITLE: Fed holds rates, signals two cuts later this year\n\nBODY: ...",
return_tensors="pt", truncation=True, max_length=1024)
out = model.predict(**inputs)
out["mode_prob"] # [P(ticker), P(macro)]
out["topic_prob"] # 18-way macro topic distribution
out["directional_read"] # signed macro read in [-1, +1]
out["hawkish_dovish_prob"] # 5-way, meaningful on monetary-policy / rates articles
What it outputs
Ticker mode (19 features, identical to shannon-1) — event type (10 binary), tone, implied_direction, novelty, claim_type (4), specificity, materiality_if_true.
Macro mode (35 features):
| Head | Type | Meaning |
|---|---|---|
topic | softmax (18) | monetary_policy / fiscal_policy / inflation / growth / labor / rates_fixed_income / equities_markets / fx_currency / energy / commodities / credit_banking / crypto / mergers_acquisitions / trade_policy / geopolitics / single_company / technicals / other |
directional_read | [-1, +1] | net read for risk assets implied by the article |
severity | softmax (5) | noise / minor / notable / major / crisis |
novelty | softmax (3) | rehash / commentary / breaking |
claim_type | softmax (4) | fact / opinion / rumor / forecast |
hawkish_dovish | softmax (5) | dovish → hawkish; meaningful on monetary-policy / rates articles |
Every macro head is a softmax or a signed scalar — argmax for a label, the weighted score for a continuous summary, or the entropy for uncertainty.
Eval
Held-out forward-temporal test set (Oct 2025 – May 2026, never seen during training). Numbers from a reproducible harness over all 15,805 macro test articles + a seeded 10,000 ticker sample.
Ticker heads (parity with shannon-1)
| Event-flag macro F1 | implied_direction | tone | claim acc |
|---|---|---|---|
| 0.79 | 0.854 | 0.834 | 89.5% |
The ticker bank matches the standalone shannon-1 model — shannon-2 is a strict superset, adding macro without regressing ticker quality.
Macro heads (n=15,805)
| Head | Metric | Value |
|---|---|---|
| topic (18-way) | accuracy | 0.814 |
| directional_read | Pearson vs panel | +0.783 |
| severity (5-way) | accuracy | 0.708 |
| novelty (3-way) | accuracy | 0.648 |
| claim_type (4-way) | accuracy | 0.785 |
| hawkish_dovish (5-way) | accuracy | 0.616 (n=1,650) |
Per-topic F1 (selected):
| Topic | F1 | Support |
|---|---|---|
| commodities | 0.94 | 2,061 |
| equities_markets | 0.88 | 3,613 |
| fx_currency | 0.88 | 3,861 |
| monetary_policy | 0.79 | 1,662 |
| inflation | 0.70 | 526 |
| geopolitics | 0.48 | 346 |
| technicals | 0.25 | 517 |
Strongest on high-volume market topics (commodities, FX, equities, monetary policy); weakest on technicals and geopolitics, which are lower-support and more heterogeneous.
Routing. Ticker and macro articles arrive on structurally distinct feeds (per-company news vs. macro wires), so the router separates the two modes essentially perfectly — it is a convenience for serving mixed streams, not a hard classification result.
Architecture
A specialized ~150M-parameter encoder shared across a 2-way router and two head banks (ticker + macro), each a 3-layer MLP over a CLS+masked-mean pooled representation.
- ~150M encoder params + lightweight head banks
- 4096-token context (1024 default at inference)
- bf16 GPU / fp32 CPU
- ~15-30 ms CPU
How it was trained
- Corpus: ticker-tagged company news + press releases and a macro news corpus (2018-2026)
- Labels: distilled from a frontier reasoning model against per-mode rubrics (separate ticker and macro labeling specs)
- Split: forward temporal — train on ≤2025-09-30, test on 2025-10 → 2026-05
- Compute: trained from the base encoder on a single B200
Caveats
- Trained against frontier-LLM labels. Eval correlations are partly imitation; treat the outputs as structured features, not ground truth.
- Macro corpus is English-language wire news, weighted toward 2024-2026.
hawkish_dovishonly fires meaningfully on monetary-policy / rates articles (it is loss-masked elsewhere during training).- Tier 2 — research preview. Don't use the outputs as standalone trading signals; combine with your own pipelines.
License
Apache 2.0, like the rest of the Binomial AI Research model zoo.
Citation
@misc{binomial-shannon-2-2026,
title = {binomial-shannon-2: A dual-mode financial news characterizer (ticker + macro)},
author = {Binomial AI Research},
year = {2026},
url = {https://huggingface.co/BinomialTechnologies/binomial-shannon-2}
}