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---
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
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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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