---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: Who was Cleopatra? She was a queen of ancient Egypt.
- text: Did you go anywhere interesting this weekend? Yes, I went to the zoo.
- text: Can robots think like humans? Not exactly, but AI can mimic some thinking
    processes.
- text: Can you name an adjective? 'Quick' is an adjective because it describes.
- text: How does the water cycle work? Water evaporates, condenses into clouds, and
    then precipitates back to the ground.
pipeline_tag: text-classification
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:** 7 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                                                                                                                                                                                                                                                                                                                                                                            |
|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| English    | <ul><li>"Can you tell me about your favorite book? I love 'Harry Potter' because it's full of magic and adventure."</li><li>'What did you learn about poems today? We learned about rhymes and how they create a rhythm in poems.'</li><li>"Can you make a sentence using the word 'enigmatic'? The old man's smile was enigmatic, making me wonder what secrets he hid."</li></ul> |
| Math       | <ul><li>"What is 8 times 9? It's 72."</li><li>'How do you find the area of a rectangle? Multiply the length by the width.'</li><li>"What's the difference between a prime number and a composite number? A prime number has only two factors, 1 and itself, while a composite number has more than two factors."</li></ul>                                                          |
| Art        | <ul><li>'What colors do you mix to make green? Yellow and blue make green.'</li><li>'Who painted the Mona Lisa? Leonardo da Vinci painted it.'</li><li>"What's the difference between sculpture and pottery? Sculpture is the art of making figures while pottery is specifically making vessels from clay."</li></ul>                                                              |
| Science    | <ul><li>"What is photosynthesis? It's the process by which plants make their food using sunlight."</li><li>'Can you name the planets in our solar system? Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune.'</li><li>"What's the difference between a solid and a liquid? A solid has a fixed shape while a liquid takes the shape of its container."</li></ul>    |
| History    | <ul><li>'Who was the first president of the United States? George Washington was the first president.'</li><li>'Can you tell me about the Egyptian pyramids? They were massive tombs built for pharaohs, the biggest is the Pyramid of Giza.'</li><li>'What was the Renaissance? It was a period of great cultural and scientific advancement in Europe.'</li></ul>                 |
| Technology | <ul><li>"What is the Internet? It's a global network of computers that can share information."</li><li>'Can you name a famous computer scientist? Alan Turing is known as one of the fathers of computer science.'</li><li>"What does 'AI' stand for? It stands for Artificial Intelligence."</li></ul>                                                                             |
| NONE       | <ul><li>'What did you have for lunch today? I had a sandwich and some fruit.'</li><li>'Do you like playing outside? Yes, I love playing soccer with my friends.'</li><li>"What's your favorite TV show? I love watching 'SpongeBob SquarePants'."</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-subject-model-basic")
# Run inference
preds = model("Who was Cleopatra? She was a queen of ancient Egypt.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 14.1333 | 30  |

| Label      | Training Sample Count |
|:-----------|:----------------------|
| Art        | 10                    |
| English    | 10                    |
| History    | 10                    |
| Math       | 10                    |
| NONE       | 15                    |
| Science    | 10                    |
| Technology | 10                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0067 | 1    | 0.1987        | -               |
| 0.3333 | 50   | 0.1814        | -               |
| 0.6667 | 100  | 0.128         | -               |
| 1.0    | 150  | 0.0146        | -               |
| 1.3333 | 200  | 0.006         | -               |
| 1.6667 | 250  | 0.0037        | -               |
| 2.0    | 300  | 0.0031        | -               |
| 2.3333 | 350  | 0.0027        | -               |
| 2.6667 | 400  | 0.0024        | -               |
| 3.0    | 450  | 0.0024        | -               |
| 3.3333 | 500  | 0.002         | -               |
| 3.6667 | 550  | 0.002         | -               |
| 4.0    | 600  | 0.0017        | -               |
| 4.3333 | 650  | 0.0019        | -               |
| 4.6667 | 700  | 0.0018        | -               |
| 5.0    | 750  | 0.0014        | -               |
| 5.3333 | 800  | 0.0013        | -               |
| 5.6667 | 850  | 0.0014        | -               |
| 6.0    | 900  | 0.0014        | -               |
| 6.3333 | 950  | 0.0014        | -               |
| 6.6667 | 1000 | 0.0016        | -               |
| 7.0    | 1050 | 0.0013        | -               |
| 7.3333 | 1100 | 0.0013        | -               |
| 7.6667 | 1150 | 0.0012        | -               |
| 8.0    | 1200 | 0.0014        | -               |
| 8.3333 | 1250 | 0.001         | -               |
| 8.6667 | 1300 | 0.0012        | -               |
| 9.0    | 1350 | 0.0014        | -               |
| 9.3333 | 1400 | 0.0012        | -               |
| 9.6667 | 1450 | 0.0012        | -               |
| 10.0   | 1500 | 0.0011        | -               |

### 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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