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projection_biencoder
polodealvarado/projection_biencoder
projection_biencoder is a zero-shot classification model from polodealvarado. Use it when you need labels you did not train the model on. It is set up for transformers. The card lists the license as mit.
CLIP-inspired with projection heads, L2 norm, and learnable temperature.
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At a glance
- Task
- Zero-Shot Classification
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Mar 1, 2026
- Updated
- Mar 1, 2026
- SHA
- 46d3bc82
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Base models
Model card
From the Hugging Face model README
Zero-Shot Text Classification — projection_biencoder
CLIP-inspired with projection heads, L2 norm, and learnable temperature.
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 | projection_biencoder |
| Training steps | 1000 |
| Batch size | 2 |
| Learning rate | 2e-05 |
| Trainable params | 109,679,105 |
| Training time | 318.3s |
Dataset
Trained on polodealvarado/zeroshot-classification.
Evaluation Results
| Metric | Score |
|---|---|
| Precision | 0.9431 |
| Recall | 0.9826 |
| F1 Score | 0.9624 |
Usage
from models.projection import ProjectionBiEncoderModel
model = ProjectionBiEncoderModel.from_pretrained("polodealvarado/projection_biencoder")
predictions = model.predict(
texts=["The stock market crashed yesterday."],
labels=[["Finance", "Sports", "Biology", "Economy"]],
)
print(predictions)
# [{"text": "...", "scores": {"Finance": 0.98, "Economy": 0.85, ...}}]
- Task
- Zero-Shot Classification
- Library
- transformers
- License
- mit
- Languages
- en
- Created
- Mar 1, 2026
- Updated
- Mar 1, 2026