SalesCue — spam
SpamHead module from the
SalesCue sales intelligence library.
Status: untrained — architecture only, random initialization. Use as a starting point for fine-tuning.
Research Contribution
Multi-Probe Hierarchical Bayesian Attention Gating with Adversarial Calibration
Spam signals are level-dependent: spammy tokens ('FREE') contribute differently than spammy sentence structures (urgency patterns) versus spammy document profiles (link density, header anomalies). SpamHead introduces a Hierarchical Bayesian Attention Gate (HBAG) with 4 aspect-specific probes (content, structure, deception, synthetic) that operate at token, sentence, and document level simultaneously. Each probe computes independent Beta(α,β) posteriors, blended via a learned gating mechanism. Per-sentence token-span encoding ensures true hierarchical processing — each sentence gets its own neural signal from its token span. An adversarial calibration loss forces provider-specific scores to match empirical inbox placement distributions. Uncertainty is decomposed into aleatoric (category entropy) and epistemic (Beta variance) components.
Sub-modules:
- HierarchicalBayesianAttentionGate: 4-probe multi-aspect attention → per-sentence token-span aggregation (12 structural features) → document-level 7-category classification with uncertainty decomposition
- AdversarialStyleTransferDetector: 32 information-theoretic features (Yule's K, Shannon entropy, Honoré's R, trigram repetition, perplexity ratio, trajectory smoothness, watermark detection per Kirchenbauer et al. 2023)
- HeaderAnalyzer: SPF/DKIM/DMARC + routing analysis (16-dim feature vector)
- TemporalBurstDetector: Cross-email send pattern analysis (Kleinberg burst model)
- CampaignSimilarityDetector: Template detection via pairwise CLS cosine similarity with proper union-find clustering
- ProviderCalibration: 6-provider deliverability (Gmail, Outlook, Yahoo, ProtonMail, Apple Mail, Corporate) with 10-feature input and adversarial calibration discriminator
7-category taxonomy: clean, template_spam, ai_generated, low_effort, role_account, domain_suspect, content_violation. Residual gate decision network with layer norm. Production path: DeBERTa model distills to 24-feature logistic regression weights loaded by a Rust SpamClassifier with SoA batch processing.
Usage
1from salescue import SalesCueModel
2
3model = SalesCueModel.from_pretrained("v9ai/salescue-spam-v1")
4result = model.predict("your sales text here")
5print(result)
Labels
cleantemplate_spamai_generatedlow_effortrole_accountdomain_suspectcontent_violation
Architecture
- Backbone:
microsoft/deberta-v3-base (shared encoder, 768-dim)
- Head:
SpamHead
- Parameters: head only (backbone loaded separately)
Intended Use
- Primary: B2B sales intelligence — lead scoring, email analysis, conversation insights
- Users: Sales teams, RevOps, GTM engineers building sales automation
- Input: English sales text (emails, call transcripts, prospect communications)
Limitations
- Untrained weights: This release contains the architecture only. Weights are randomly initialized and must be fine-tuned on domain-specific data before production use.
- English only: Designed for English sales text. Performance on other languages is untested.
- Domain-specific: Optimized for B2B sales communications. May not generalize to other text domains.
- Shared backbone: Requires
microsoft/deberta-v3-base loaded via the SalesCue library.
About SalesCue
SalesCue is a sales intelligence library with 12 ML modules sharing a single
DeBERTa-v3-base encoder backbone. Modules can be composed via Unix-style piping:
1from salescue import Document
2result = Document("interested in pricing") | ai.score | ai.intent | ai.sentiment
All modules: score intent reply triggers icp objection sentiment spam entities call subject emailgen
