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v9ai/salescue-reply-v1
salescue-reply-v1 is a text classification model from v9ai. Use it when you need a label for a piece of text. It is set up for salescue. The card lists the license as mit.
ReplyHead module from the SalesCue sales intelligence library.
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From the Hugging Face model README
ReplyHead module from the SalesCue sales intelligence library.
Status:
untrained— architecture only, random initialization. Use as a starting point for fine-tuning.
Constrained CRF for Structured Reply Classification
Multi-label classification treats labels independently, but reply labels have structural constraints: unsubscribe and genuinely_interested are mutually exclusive; referral implies not_now. ReplyHead enforces these via a constrained conditional random field where the transition matrix encodes label co-occurrence rules.
from salescue import SalesCueModel
model = SalesCueModel.from_pretrained("v9ai/salescue-reply-v1")
result = model.predict("your sales text here")
print(result)
genuinely_interestedpolitely_acknowledgingobjectionnot_nowunsubscribeout_of_officebouncemeeting_requestreferralnegative_sentimentmicrosoft/deberta-v3-base (shared encoder, 768-dim)ReplyHeadmicrosoft/deberta-v3-base loaded via the SalesCue library.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.