v9ai/salescue-sentiment-v1

SalesCue — sentiment

DisentangledSentimentIntentHead module from the SalesCue sales intelligence library.

Status: untrained — architecture only, random initialization. Use as a starting point for fine-tuning.

Research Contribution

MI-Minimized Sentiment-Intent Disentanglement

Sentiment and intent are entangled in text representations. DisentangledSentimentIntentHead enforces disentanglement explicitly: the sentiment representation contains zero information about intent, and vice versa. Uses mutual information minimization via the CLUB bound (Cheng et al., 2020) to provably decorrelate the two representations, then learns inversion patterns from their interaction.

Usage

from salescue import SalesCueModel

model = SalesCueModel.from_pretrained("v9ai/salescue-sentiment-v1")
result = model.predict("your sales text here")
print(result)

Labels

• enthusiastic
• positive_engaged
• neutral_professional
• cautious_interest
• polite_decline
• frustrated_objection
• hostile_rejection

Architecture

• Backbone: microsoft/deberta-v3-base (shared encoder, 768-dim)
• Head: DisentangledSentimentIntentHead
• 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:

from salescue import Document
result = Document("interested in pricing") | ai.score | ai.intent | ai.sentiment

All modules: score intent reply triggers icp objection sentiment spam entities call subject emailgen

See the SalesCue documentation for details.