binomial-marks 0.1.1

Binomial's earnings-call NLP scorer — 23 structured signals per transcript.

binomial-marks

Binomial's earnings-call NLP scorer — 23 structured signals per transcript, in ~50ms on CPU.

Part of Binomial AI Research's specialist zoo — small, deployable models for quantitative finance. Named after Howard Marks (Oaktree), whose memos parse market sentiment and tone.

Model card: BinomialTechnologies/binomial-marks-1.

Install

pip install binomial-marks

Pulls in torch, transformers>=4.48, huggingface-hub. Weights (~1.6 GB) are downloaded from HuggingFace on first use.

One-shot

from binomial_marks import score

result = score(
    transcript="Operator: Welcome to NVIDIA's Q4 2025 earnings call...",
    ticker="NVDA", sector="Technology", country="US",
    year=2025, quarter=4,
)

Returns:

{
  "topics": {
    "guidance":       {"mentioned": True, "mention_prob": 0.94, "score":  1.7},
    "revenue_growth": {"mentioned": True, "mention_prob": 0.97, "score":  1.5},
    "margins":        {"mentioned": True, "mention_prob": 0.91, "score":  0.8},
    # ... 7 more topics
  },
  "mgmt_confidence":    4.6,   # 1 = uncertain "we hope" → 5 = "we will deliver X"
  "mgmt_defensiveness": 1.4,   # 1 = open Q&A → 5 = pivots, refuses to commit
  "analyst_skepticism": 1.8,   # 1 = congratulatory → 5 = re-asking same question
}

Batched

from binomial_marks import MarksScorer

scorer = MarksScorer()                       # loads model once
results = scorer.score_batch([
    {"transcript": "...", "ticker": "NVDA", "sector": "Technology", "year": 2025, "quarter": 4},
    {"transcript": "...", "ticker": "AAPL", "sector": "Technology", "year": 2025, "quarter": 1},
])

Configuration

MarksScorer(
    model_id="BinomialTechnologies/binomial-marks-1",  # or local path / pinned version
    device="cuda",                # auto-detect: cuda > mps > cpu
    dtype=torch.bfloat16,         # default: bf16 on GPU, fp32 on CPU
    max_length=16384,             # tokenizer truncation cap
    mention_threshold=0.5,        # sigmoid threshold for the topic_mentioned heads
)

What it returns

10 topic-direction scores (each: was the topic discussed? if so, what direction in [-2, +2]?):

Topic	What −2 / +2 mean
guidance	lowered hard / raised significantly
revenue_growth	decelerating / accelerating
margins	compressing / expanding
demand	softening / strong
buybacks	paused or reduced / new or upsized
dividends	cut or skipped / raised or initiated
m_and_a	divestiture / strategic acquisition
headcount	layoffs / aggressive hiring
macro_exposure	clear headwind / clear tailwind
competition	losing share / gaining share

3 tone scores (1 to 5): mgmt_confidence, mgmt_defensiveness, analyst_skepticism.

When mentioned=False, score is forced to 0 — the topic wasn't discussed, so direction is undefined.

Hardware

• CPU: ~50ms/call on a modern laptop.
• GPU: ~10ms/call on A100/H100/B200, ~12 calls/sec batched.
• Memory: ~1.6 GB weights, ~3 GB peak with a 16k-token context.

Limitations

The model card has the full picture. In short:

1. headcount is the weakest dimension (Spearman 0.39 vs. frontier — half the others).
2. Tone has rank-order signal but absolute levels drift; normalize cross-sectionally.
3. English transcripts only.
4. Truncates at 16,384 tokens (~50k characters; covers ~p99 of calls).
5. Pure NLP scorer — outputs are features, not trades. The trading rule is yours.

License

Apache 2.0. If you build on this, a citation is appreciated.