---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: hotel in geneva airport
- text: what payroll deduction is mpp
- text: weather in erlanger ky
- text: what is the coordinates of point p
- text: what's the weather in roseburg
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                                                                                                                                             |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'how far is palms casino from the airport in las vegas'</li><li>'anarkali bazar lahore'</li><li>'what county is alma nebraska in?'</li></ul> |
| 0     | <ul><li>'what is symptom of bipolar disorder'</li><li>'early symptoms of shingles outbreak'</li><li>'bnsf total employees'</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("weather in erlanger ky")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 2   | 6.3028 | 21  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 755                   |
| 1     | 718                   |

### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1e-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 |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0001 | 1     | 0.2507        | -               |
| 0.0294 | 500   | 0.1803        | -               |
| 0.0589 | 1000  | 0.0135        | -               |
| 0.0883 | 1500  | 0.0021        | -               |
| 0.1178 | 2000  | 0.001         | -               |
| 0.1472 | 2500  | 0.0007        | -               |
| 0.1766 | 3000  | 0.0005        | -               |
| 0.2061 | 3500  | 0.0004        | -               |
| 0.2355 | 4000  | 0.0004        | -               |
| 0.2649 | 4500  | 0.0003        | -               |
| 0.2944 | 5000  | 0.0003        | -               |
| 0.3238 | 5500  | 0.0003        | -               |
| 0.3533 | 6000  | 0.0003        | -               |
| 0.3827 | 6500  | 0.0002        | -               |
| 0.4121 | 7000  | 0.0003        | -               |
| 0.4416 | 7500  | 0.0002        | -               |
| 0.4710 | 8000  | 0.0002        | -               |
| 0.5004 | 8500  | 0.0002        | -               |
| 0.5299 | 9000  | 0.0002        | -               |
| 0.5593 | 9500  | 0.0002        | -               |
| 0.5888 | 10000 | 0.0002        | -               |
| 0.6182 | 10500 | 0.0002        | -               |
| 0.6476 | 11000 | 0.0001        | -               |
| 0.6771 | 11500 | 0.0001        | -               |
| 0.7065 | 12000 | 0.0001        | -               |
| 0.7359 | 12500 | 0.0001        | -               |
| 0.7654 | 13000 | 0.0001        | -               |
| 0.7948 | 13500 | 0.0001        | -               |
| 0.8243 | 14000 | 0.0001        | -               |
| 0.8537 | 14500 | 0.0001        | -               |
| 0.8831 | 15000 | 0.0001        | -               |
| 0.9126 | 15500 | 0.0001        | -               |
| 0.9420 | 16000 | 0.0001        | -               |
| 0.9714 | 16500 | 0.0001        | -               |

### Framework Versions
- Python: 3.11.5
- SetFit: 1.1.2
- Sentence Transformers: 4.0.2
- Transformers: 4.55.2
- PyTorch: 2.8.0
- Datasets: 2.15.0
- 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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