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SrihariV/b2b-intent-signal-classifier
b2b-intent-signal-classifier is a text classification model from SrihariV. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
Fine-tuned DistilBERT model that classifies B2B company signals into 6 intent categories for GTM teams.
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From the Hugging Face model README
Fine-tuned DistilBERT model that classifies B2B company signals into 6 intent categories for GTM teams.
This model detects buyer intent signals from text — news articles, job postings, press releases — and classifies them into actionable categories for sales and marketing teams.
| Category | Description | Example |
|---|---|---|
hiring_surge | Aggressive hiring in specific area | "TechCorp hiring 50 ML engineers" |
funding_round | Investment received | "DataCo closes $50M Series C" |
product_launch | New product/feature release | "CloudInc launches AI platform" |
leadership_change | Executive hired/departed | "New CTO joins from Google" |
expansion | Geographic/market expansion | "Opens Singapore office" |
cost_cutting | Layoffs, budget cuts | "Reduces workforce by 20%" |
from transformers import pipeline
classifier = pipeline("text-classification", model="SrihariV/b2b-intent-signal-classifier")
result = classifier("Stripe raises $200M Series D led by Sequoia")
# Output: [{'label': 'funding_round', 'score': 0.98}]
Srihari Venkatesan — GTM Engineer | AI Consultant
MIT