Zero-Shot Text Classification — polyencoder

Learnable poly-codes with label-conditioned cross-attention.

This model encodes texts and candidate labels into a shared embedding space using BERT, enabling classification into arbitrary categories without retraining for new labels.

Training Details

Parameter	Value
Base model	bert-base-uncased
Model variant	polyencoder
Training steps	1000
Batch size	2
Learning rate	2e-05
Trainable params	109,494,528
Training time	359.7s

Dataset

Trained on polodealvarado/zeroshot-classification.

Evaluation Results

Metric	Score
Precision	0.9463
Recall	0.9677
F1 Score	0.9569

Usage

from models.polyencoder import PolyEncoderModel

model = PolyEncoderModel.from_pretrained("polodealvarado/polyencoder")

predictions = model.predict(
    texts=["The stock market crashed yesterday."],
    labels=[["Finance", "Sports", "Biology", "Economy"]],
)
print(predictions)
# [{"text": "...", "scores": {"Finance": 0.98, "Economy": 0.85, ...}}]