setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:BAAI/bge-small-en-v1.5 base_model:finetune:BAAI/bge-small-en-v1.5 region:us
---
base_model: BAAI/bge-small-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: What's your favorite way to learn? Through books, videos, or experiments?
    Experiments. I like seeing science in action.
- text: Can you name a living organism's basic needs? Food, water... Can we change
    the subject?
- text: What do you find fascinating about the human body? That our brain works like
    a supercomputer.
- text: What's something you learned about in technology? We learned about coding.
    I made a simple game.
- text: Do you know how to code? Nope. Sounds complicated.
inference: true
---

# 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) -->
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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                                                                                                                                                                                                                                                                                                                               |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'What did you learn in school today? Nothing much, just the usual stuff.'</li><li>"Do you know the capital of France? Don't know, don't care."</li><li>"Can you tell me what 2 + 2 equals? Guess it's 4, but why does it matter?"</li></ul>                                                                                    |
| positive | <ul><li>"What's your favorite subject? Science, because I love experiments."</li><li>'Can you tell me the planets in order? Sure, Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune. Pluto used to be one, but not anymore.'</li><li>"Do you enjoy math class? Yeah, it's cool, especially when we do geometry."</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("bew/setfit-engagement-model-basic")
# Run inference
preds = model("Do you know how to code? Nope. Sounds complicated.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 15.0470 | 26  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 79                    |
| positive | 70                    |

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0028 | 1    | 0.2418        | -               |
| 0.1416 | 50   | 0.2311        | -               |
| 0.2833 | 100  | 0.2425        | -               |
| 0.4249 | 150  | 0.0572        | -               |
| 0.5666 | 200  | 0.0049        | -               |
| 0.7082 | 250  | 0.0031        | -               |
| 0.8499 | 300  | 0.0019        | -               |
| 0.9915 | 350  | 0.0018        | -               |
| 1.1331 | 400  | 0.0015        | -               |
| 1.2748 | 450  | 0.001         | -               |
| 1.4164 | 500  | 0.0011        | -               |
| 1.5581 | 550  | 0.0008        | -               |
| 1.6997 | 600  | 0.0008        | -               |
| 1.8414 | 650  | 0.0007        | -               |
| 1.9830 | 700  | 0.0008        | -               |
| 2.1246 | 750  | 0.0007        | -               |
| 2.2663 | 800  | 0.0005        | -               |
| 2.4079 | 850  | 0.0006        | -               |
| 2.5496 | 900  | 0.0005        | -               |
| 2.6912 | 950  | 0.0005        | -               |
| 2.8329 | 1000 | 0.0005        | -               |
| 2.9745 | 1050 | 0.0005        | -               |
| 3.1161 | 1100 | 0.0005        | -               |
| 3.2578 | 1150 | 0.0005        | -               |
| 3.3994 | 1200 | 0.0004        | -               |
| 3.5411 | 1250 | 0.0004        | -               |
| 3.6827 | 1300 | 0.0004        | -               |
| 3.8244 | 1350 | 0.0004        | -               |
| 3.9660 | 1400 | 0.0004        | -               |
| 4.1076 | 1450 | 0.0004        | -               |
| 4.2493 | 1500 | 0.0003        | -               |
| 4.3909 | 1550 | 0.0004        | -               |
| 4.5326 | 1600 | 0.0004        | -               |
| 4.6742 | 1650 | 0.0003        | -               |
| 4.8159 | 1700 | 0.0003        | -               |
| 4.9575 | 1750 | 0.0004        | -               |
| 5.0992 | 1800 | 0.0003        | -               |
| 5.2408 | 1850 | 0.0003        | -               |
| 5.3824 | 1900 | 0.0003        | -               |
| 5.5241 | 1950 | 0.0003        | -               |
| 5.6657 | 2000 | 0.0003        | -               |
| 5.8074 | 2050 | 0.0003        | -               |
| 5.9490 | 2100 | 0.0003        | -               |
| 6.0907 | 2150 | 0.0003        | -               |
| 6.2323 | 2200 | 0.0003        | -               |
| 6.3739 | 2250 | 0.0003        | -               |
| 6.5156 | 2300 | 0.0003        | -               |
| 6.6572 | 2350 | 0.0003        | -               |
| 6.7989 | 2400 | 0.0002        | -               |
| 6.9405 | 2450 | 0.0003        | -               |
| 7.0822 | 2500 | 0.0003        | -               |
| 7.2238 | 2550 | 0.0003        | -               |
| 7.3654 | 2600 | 0.0003        | -               |
| 7.5071 | 2650 | 0.0003        | -               |
| 7.6487 | 2700 | 0.0003        | -               |
| 7.7904 | 2750 | 0.0003        | -               |
| 7.9320 | 2800 | 0.0003        | -               |
| 8.0737 | 2850 | 0.0003        | -               |
| 8.2153 | 2900 | 0.0003        | -               |
| 8.3569 | 2950 | 0.0003        | -               |
| 8.4986 | 3000 | 0.0002        | -               |
| 8.6402 | 3050 | 0.0003        | -               |
| 8.7819 | 3100 | 0.0003        | -               |
| 8.9235 | 3150 | 0.0003        | -               |
| 9.0652 | 3200 | 0.0003        | -               |
| 9.2068 | 3250 | 0.0002        | -               |
| 9.3484 | 3300 | 0.0003        | -               |
| 9.4901 | 3350 | 0.0002        | -               |
| 9.6317 | 3400 | 0.0003        | -               |
| 9.7734 | 3450 | 0.0003        | -               |
| 9.9150 | 3500 | 0.0002        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.17.0
- Tokenizers: 0.15.2

## 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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