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):

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)

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)

Per-topic F1 (selected):

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_dovish only 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}
}