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dynquery
polodealvarado/dynquery
dynquery 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.
DyREx-inspired dynamic label queries via cross-attention over text tokens.
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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
- 9aa8271d
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Base models
Model card
From the Hugging Face model README
Zero-Shot Text Classification — dynquery
DyREx-inspired dynamic label queries via cross-attention over text tokens.
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 | dynquery |
| Training steps | 1000 |
| Batch size | 2 |
| Learning rate | 2e-05 |
| Trainable params | 111,844,608 |
| Training time | 383.0s |
Dataset
Trained on polodealvarado/zeroshot-classification.
Evaluation Results
| Metric | Score |
|---|---|
| Precision | 0.7704 |
| Recall | 0.9773 |
| F1 Score | 0.8616 |
Usage
from models.dynquery import DynQueryModel
model = DynQueryModel.from_pretrained("polodealvarado/dynquery")
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