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convmatch
polodealvarado/convmatch
convmatch 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.
Multi-scale CNN encoder over pretrained embeddings (no transformer at inference).
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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
- f1ecc33b
Try a prompt
Base models
Model card
From the Hugging Face model README
Zero-Shot Text Classification — convmatch
Multi-scale CNN encoder over pretrained embeddings (no transformer at inference).
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 | convmatch |
| Training steps | 1000 |
| Batch size | 2 |
| Learning rate | 2e-05 |
| Trainable params | 24,948,992 |
| Training time | 84.1s |
Dataset
Trained on polodealvarado/zeroshot-classification.
Evaluation Results
| Metric | Score |
|---|---|
| Precision | 0.7531 |
| Recall | 0.9922 |
| F1 Score | 0.8563 |
Usage
from models.convmatch import ConvMatchModel
model = ConvMatchModel.from_pretrained("polodealvarado/convmatch")
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