| | --- |
| | language: |
| | - en |
| | license: mit |
| | library_name: transformers |
| | pipeline_tag: zero-shot-classification |
| | tags: |
| | - zero-shot |
| | - multi-label |
| | - text-classification |
| | - pytorch |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | base_model: bert-base-uncased |
| | datasets: |
| | - polodealvarado/zeroshot-classification |
| | --- |
| | |
| | # Zero-Shot Text Classification — biencoder |
| |
|
| | Shared BERT encoder with dot-product similarity and sigmoid activation. |
| |
|
| | 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 | `biencoder` | |
| | | Training steps | 1000 | |
| | | Batch size | 2 | |
| | | Learning rate | 2e-05 | |
| | | Trainable params | 109,482,240 | |
| | | Training time | 345.0s | |
| |
|
| | ## Dataset |
| |
|
| | Trained on [polodealvarado/zeroshot-classification](https://huggingface.co/datasets/polodealvarado/zeroshot-classification). |
| |
|
| | ## Evaluation Results |
| |
|
| | | Metric | Score | |
| | |--------|-------| |
| | | Precision | 0.9486 | |
| | | Recall | 0.9660 | |
| | | F1 Score | 0.9572 | |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from models.base import BiEncoderModel |
| | |
| | model = BiEncoderModel.from_pretrained("polodealvarado/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, ...}}] |
| | ``` |
| |
|
