---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: A young researcher presenting their findings on a novel approach to information
    retrieval
- text: A cartoonist specializing in educational materials
- text: A fellow child actor who shares the excitement and challenges of being part
    of research
- text: A teenage daughter who excels in her studies due to the integration of technology
    in her education
- text: An under 16 teenager who lost the chance to be selected for the national team
    in U-15 championship
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: BAAI/bge-small-en-v1.5
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.96
      name: Accuracy
---

# 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 -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                                                                                                                                                                                 |
|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| not_minor | <ul><li>'A young manager in her very first leadership position.'</li><li>'A detail-oriented student who excels in organizing group study sessions in the library'</li><li>'A fellow student involved in a book club who prefers physical copies for annotation and discussion'</li></ul>                                                 |
| minor     | <ul><li>'A teenage girl from a disadvantaged background who is empowered by the health education programs'</li><li>"A young child from a diverse family background who is involved in the candidate's research studies"</li><li>'A child with a passion for music who learns best through creative and interactive activities'</li></ul> |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.96     |

## 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("A cartoonist specializing in educational materials")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 3   | 14.34  | 26  |

| Label     | Training Sample Count |
|:----------|:----------------------|
| not_minor | 100                   |
| minor     | 100                   |

### Training Hyperparameters
- batch_size: (64, 64)
- num_epochs: (4, 4)
- 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
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0032 | 1    | 0.2636        | -               |
| 0.1582 | 50   | 0.2471        | -               |
| 0.3165 | 100  | 0.2067        | -               |
| 0.4747 | 150  | 0.0207        | -               |
| 0.6329 | 200  | 0.0021        | -               |
| 0.7911 | 250  | 0.0015        | -               |
| 0.9494 | 300  | 0.0013        | -               |
| 1.0    | 316  | -             | 0.0825          |
| 1.1076 | 350  | 0.0011        | -               |
| 1.2658 | 400  | 0.001         | -               |
| 1.4241 | 450  | 0.0009        | -               |
| 1.5823 | 500  | 0.0008        | -               |
| 1.7405 | 550  | 0.0008        | -               |
| 1.8987 | 600  | 0.0007        | -               |
| 2.0    | 632  | -             | 0.0813          |
| 2.0570 | 650  | 0.001         | -               |
| 2.2152 | 700  | 0.0007        | -               |
| 2.3734 | 750  | 0.0007        | -               |
| 2.5316 | 800  | 0.0006        | -               |
| 2.6899 | 850  | 0.0006        | -               |
| 2.8481 | 900  | 0.0006        | -               |
| 3.0    | 948  | -             | 0.0736          |
| 3.0063 | 950  | 0.0006        | -               |
| 3.1646 | 1000 | 0.0006        | -               |
| 3.3228 | 1050 | 0.0005        | -               |
| 3.4810 | 1100 | 0.0006        | -               |
| 3.6392 | 1150 | 0.0005        | -               |
| 3.7975 | 1200 | 0.0006        | -               |
| 3.9557 | 1250 | 0.0005        | -               |
| 4.0    | 1264 | -             | 0.0754          |

### Framework Versions
- Python: 3.12.4
- SetFit: 1.1.0
- Sentence Transformers: 3.2.1
- Transformers: 4.45.2
- PyTorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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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