Jeffy — Pretrained Classifiers for Text, Games, and More

Pretrained classifiers.
CPU only. pip install.

68 classifiers ship ready to use. Text, game state, numeric features. No GPU, no API keys, no cloud dependency.

Try it live
Classifier:

Live demos

Pac-Man AI playing via text classification

Pac-Man AI

Regex parses junction descriptions into features, Jeffy classifies the best direction. 8,035 mean score, 2.5x the current #1, 3ms per decision.

2.5x #1 3ms / decision
Read the tutorial →
Doom classifier demo: real-time game-state decisions
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Doom Classifier

Real-time game-state classification. 24 numeric features extracted each frame decide whether to fire, turn left, or turn right.

3 classes 100% accuracy
Open demo →
Poker AI demo: four players making decisions at a Texas Hold'em table
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Poker AI

Four AI players at a Texas Hold'em table. Each makes fold, check, call, or raise decisions from 18 game-state features, streamed live.

4 classes 88% accuracy
Open demo →
Fly Navigation Demo
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Fly Navigation

A biomechanical fruit fly forages for food via chemotaxis. Jeffy classifies walk, turn-left, or turn-right from 16 physics features at each step.

3 classes 98% accuracy
Open demo →
68
Classifiers
99.1%
Top accuracy
<80ms
Latency
CPU
No GPU needed
~/project

17 English classifiers

51 multilingual classifiers — 60 voice-command intents per language, frozen multilingual embeddings

Predict

from jeffy.engine import Engine engine = Engine() engine.load() result = engine.predict( "banking77", "I was charged twice for the same transaction" ) # {"label": "transaction_charged_twice", "confidence": 0.999}

Train your own

from jeffy.train import train_classifier clf = train_classifier( texts=["great product!", "terrible", ...], labels=["positive", "negative", ...], task_id="my_reviews", ) clf.predict("exceeded expectations") # {"label": "positive", "confidence": 0.87}