Hybrid entity extraction combining high-precision regex patterns for structured entities (emails, phones, URLs, money, dates) with a pointer-network NER for unstructured entities (person, company, product, role). A re-typing layer reclassifies detected entities in context — e.g., "John" becomes decision_maker vs. reference based on surrounding text.

Usage

python

from salescue import SalesCueModel

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

Labels

person
company
product
role
location
technology

Architecture

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

python

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.