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.

Categories

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%"

Performance

F1 Score (Macro): 0.9976
Accuracy: 0.9976
Precision: 0.9976
Recall: 0.9976

Usage

python

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}]

Training Details

Base model: distilbert-base-uncased
Training data: 5,400+ synthetic B2B signal examples
Epochs: 4
Learning rate: 2e-5
Batch size: 32

Built By

Srihari Venkatesan — GTM Engineer | AI Consultant

License

MIT