---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Tennengebirge Reef
- text: Outcrop next to I-84 East
- text: scenic overview
- text: Ruby Star for sale now Please contact us for more details. Regards
- text: torre rocosa de grans dimensions. 3 blocs partits
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: BAAI/bge-small-en-v1.5
---

# SetFit with BAAI/bge-small-en-v1.5

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

## Model Details

### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
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### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label | Examples                                                                                                                          |
|:------|:----------------------------------------------------------------------------------------------------------------------------------|
| 0     | <ul><li>'Calcite'</li><li>'biotite. Contact metamorphosis'</li><li>'rail trail'</li></ul>                                         |
| 1     | <ul><li>'Geafitti on tree and burn scar on ground'</li><li>'another beautiful rock from the same place'</li><li>'Vhfgv'</li></ul> |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("scenic overview")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max  |
|:-------------|:----|:-------|:-----|
| Word count   | 1   | 7.2788 | 1899 |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 2997                  |
| 1     | 783                   |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1    | 0.2331        | -               |
| 0.0106 | 50   | 0.2391        | -               |
| 0.0212 | 100  | 0.238         | -               |
| 0.0317 | 150  | 0.2309        | -               |
| 0.0423 | 200  | 0.2117        | -               |
| 0.0529 | 250  | 0.1879        | -               |
| 0.0635 | 300  | 0.1745        | -               |
| 0.0741 | 350  | 0.1708        | -               |
| 0.0847 | 400  | 0.1402        | -               |
| 0.0952 | 450  | 0.1349        | -               |
| 0.1058 | 500  | 0.1092        | -               |
| 0.1164 | 550  | 0.1031        | -               |
| 0.1270 | 600  | 0.0828        | -               |
| 0.1376 | 650  | 0.0756        | -               |
| 0.1481 | 700  | 0.0587        | -               |
| 0.1587 | 750  | 0.0487        | -               |
| 0.1693 | 800  | 0.0557        | -               |
| 0.1799 | 850  | 0.0456        | -               |
| 0.1905 | 900  | 0.0371        | -               |
| 0.2011 | 950  | 0.0412        | -               |
| 0.2116 | 1000 | 0.0382        | -               |
| 0.2222 | 1050 | 0.0376        | -               |
| 0.2328 | 1100 | 0.0353        | -               |
| 0.2434 | 1150 | 0.0346        | -               |
| 0.2540 | 1200 | 0.0364        | -               |
| 0.2646 | 1250 | 0.0317        | -               |
| 0.2751 | 1300 | 0.0374        | -               |
| 0.2857 | 1350 | 0.0282        | -               |
| 0.2963 | 1400 | 0.0255        | -               |
| 0.3069 | 1450 | 0.023         | -               |
| 0.3175 | 1500 | 0.0287        | -               |
| 0.3280 | 1550 | 0.025         | -               |
| 0.3386 | 1600 | 0.0216        | -               |
| 0.3492 | 1650 | 0.0241        | -               |
| 0.3598 | 1700 | 0.0234        | -               |
| 0.3704 | 1750 | 0.0279        | -               |
| 0.3810 | 1800 | 0.0239        | -               |
| 0.3915 | 1850 | 0.0199        | -               |
| 0.4021 | 1900 | 0.0252        | -               |
| 0.4127 | 1950 | 0.0219        | -               |
| 0.4233 | 2000 | 0.0228        | -               |
| 0.4339 | 2050 | 0.0204        | -               |
| 0.4444 | 2100 | 0.0231        | -               |
| 0.4550 | 2150 | 0.0144        | -               |
| 0.4656 | 2200 | 0.0229        | -               |
| 0.4762 | 2250 | 0.0129        | -               |
| 0.4868 | 2300 | 0.0219        | -               |
| 0.4974 | 2350 | 0.0194        | -               |
| 0.5079 | 2400 | 0.0172        | -               |
| 0.5185 | 2450 | 0.0177        | -               |
| 0.5291 | 2500 | 0.0252        | -               |
| 0.5397 | 2550 | 0.0251        | -               |
| 0.5503 | 2600 | 0.014         | -               |
| 0.5608 | 2650 | 0.0204        | -               |
| 0.5714 | 2700 | 0.0248        | -               |
| 0.5820 | 2750 | 0.0146        | -               |
| 0.5926 | 2800 | 0.0191        | -               |
| 0.6032 | 2850 | 0.0223        | -               |
| 0.6138 | 2900 | 0.0206        | -               |
| 0.6243 | 2950 | 0.0163        | -               |
| 0.6349 | 3000 | 0.0235        | -               |
| 0.6455 | 3050 | 0.0245        | -               |
| 0.6561 | 3100 | 0.0199        | -               |
| 0.6667 | 3150 | 0.0145        | -               |
| 0.6772 | 3200 | 0.016         | -               |
| 0.6878 | 3250 | 0.0143        | -               |
| 0.6984 | 3300 | 0.0206        | -               |
| 0.7090 | 3350 | 0.0187        | -               |
| 0.7196 | 3400 | 0.0168        | -               |
| 0.7302 | 3450 | 0.0148        | -               |
| 0.7407 | 3500 | 0.0212        | -               |
| 0.7513 | 3550 | 0.0185        | -               |
| 0.7619 | 3600 | 0.015         | -               |
| 0.7725 | 3650 | 0.0187        | -               |
| 0.7831 | 3700 | 0.0161        | -               |
| 0.7937 | 3750 | 0.0204        | -               |
| 0.8042 | 3800 | 0.0182        | -               |
| 0.8148 | 3850 | 0.0157        | -               |
| 0.8254 | 3900 | 0.0197        | -               |
| 0.8360 | 3950 | 0.0133        | -               |
| 0.8466 | 4000 | 0.0211        | -               |
| 0.8571 | 4050 | 0.0155        | -               |
| 0.8677 | 4100 | 0.0197        | -               |
| 0.8783 | 4150 | 0.0168        | -               |
| 0.8889 | 4200 | 0.0139        | -               |
| 0.8995 | 4250 | 0.0132        | -               |
| 0.9101 | 4300 | 0.018         | -               |
| 0.9206 | 4350 | 0.014         | -               |
| 0.9312 | 4400 | 0.017         | -               |
| 0.9418 | 4450 | 0.0173        | -               |
| 0.9524 | 4500 | 0.0163        | -               |
| 0.9630 | 4550 | 0.0178        | -               |
| 0.9735 | 4600 | 0.0176        | -               |
| 0.9841 | 4650 | 0.0126        | -               |
| 0.9947 | 4700 | 0.0194        | -               |

### Framework Versions
- Python: 3.12.9
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citation

### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}
```

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