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 model-index 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: sales affects ceo pay
- text: time affects entrepreneurship intention
- text: operations planning affects entrepreneurship intention
- text: entrepreneurial self-efficacy affects entrepreneurship intention
- text: empirical training affects entrepreneurship intention
inference: true
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: 0.9058823529411765
      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                                                                                                                                                                                   |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1     | <ul><li>'board diversity affects ceo pay'</li><li>'perceptions of formal learning affects entrepreneurship intention'</li><li>'proactiveness affects entrepreneurship intention'</li></ul> |
| 0     | <ul><li>'sales and takeovers affects entrepreneurship intention'</li><li>'uk affects entrepreneurship intention'</li><li>'economics affects entrepreneurship intention'</li></ul>          |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.9059   |

## 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("abehandlerorg/setfit")
# Run inference
preds = model("sales affects ceo pay")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count   | 4   | 5.4307 | 12  |

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 168                   |
| 1     | 171                   |

### Training Hyperparameters
- batch_size: (32, 32)
- 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
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False

### Training Results
| Epoch  | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0006 | 1    | 0.3133        | -               |
| 0.0277 | 50   | 0.289         | -               |
| 0.0553 | 100  | 0.2506        | -               |
| 0.0830 | 150  | 0.2243        | -               |
| 0.1107 | 200  | 0.2388        | -               |
| 0.1384 | 250  | 0.2084        | -               |
| 0.1660 | 300  | 0.1316        | -               |
| 0.1937 | 350  | 0.0142        | -               |
| 0.2214 | 400  | 0.0065        | -               |
| 0.2490 | 450  | 0.0037        | -               |
| 0.2767 | 500  | 0.003         | -               |
| 0.3044 | 550  | 0.002         | -               |
| 0.3320 | 600  | 0.0018        | -               |
| 0.3597 | 650  | 0.0026        | -               |
| 0.3874 | 700  | 0.0013        | -               |
| 0.4151 | 750  | 0.0012        | -               |
| 0.4427 | 800  | 0.0284        | -               |
| 0.4704 | 850  | 0.0145        | -               |
| 0.4981 | 900  | 0.0053        | -               |
| 0.5257 | 950  | 0.0075        | -               |
| 0.5534 | 1000 | 0.005         | -               |
| 0.5811 | 1050 | 0.0008        | -               |
| 0.6087 | 1100 | 0.0008        | -               |
| 0.6364 | 1150 | 0.0008        | -               |
| 0.6641 | 1200 | 0.0007        | -               |
| 0.6918 | 1250 | 0.0008        | -               |
| 0.7194 | 1300 | 0.0009        | -               |
| 0.7471 | 1350 | 0.0007        | -               |
| 0.7748 | 1400 | 0.0008        | -               |
| 0.8024 | 1450 | 0.0006        | -               |
| 0.8301 | 1500 | 0.0006        | -               |
| 0.8578 | 1550 | 0.0192        | -               |
| 0.8854 | 1600 | 0.0005        | -               |
| 0.9131 | 1650 | 0.002         | -               |
| 0.9408 | 1700 | 0.0204        | -               |
| 0.9685 | 1750 | 0.0039        | -               |
| 0.9961 | 1800 | 0.0007        | -               |
| 1.0238 | 1850 | 0.0005        | -               |
| 1.0515 | 1900 | 0.0004        | -               |
| 1.0791 | 1950 | 0.0005        | -               |
| 1.1068 | 2000 | 0.0006        | -               |
| 1.1345 | 2050 | 0.0004        | -               |
| 1.1621 | 2100 | 0.0006        | -               |
| 1.1898 | 2150 | 0.0004        | -               |
| 1.2175 | 2200 | 0.0004        | -               |
| 1.2452 | 2250 | 0.0018        | -               |
| 1.2728 | 2300 | 0.0041        | -               |
| 1.3005 | 2350 | 0.0004        | -               |
| 1.3282 | 2400 | 0.0107        | -               |
| 1.3558 | 2450 | 0.0005        | -               |
| 1.3835 | 2500 | 0.0004        | -               |
| 1.4112 | 2550 | 0.0004        | -               |
| 1.4388 | 2600 | 0.0167        | -               |
| 1.4665 | 2650 | 0.0068        | -               |
| 1.4942 | 2700 | 0.0004        | -               |
| 1.5219 | 2750 | 0.0064        | -               |
| 1.5495 | 2800 | 0.0041        | -               |
| 1.5772 | 2850 | 0.0004        | -               |
| 1.6049 | 2900 | 0.0003        | -               |
| 1.6325 | 2950 | 0.0004        | -               |
| 1.6602 | 3000 | 0.0004        | -               |
| 1.6879 | 3050 | 0.0003        | -               |
| 1.7156 | 3100 | 0.0057        | -               |
| 1.7432 | 3150 | 0.0044        | -               |
| 1.7709 | 3200 | 0.0004        | -               |
| 1.7986 | 3250 | 0.0166        | -               |
| 1.8262 | 3300 | 0.0004        | -               |
| 1.8539 | 3350 | 0.0032        | -               |
| 1.8816 | 3400 | 0.0133        | -               |
| 1.9092 | 3450 | 0.0003        | -               |
| 1.9369 | 3500 | 0.0003        | -               |
| 1.9646 | 3550 | 0.0052        | -               |
| 1.9923 | 3600 | 0.0004        | -               |
| 2.0199 | 3650 | 0.004         | -               |
| 2.0476 | 3700 | 0.0003        | -               |
| 2.0753 | 3750 | 0.0054        | -               |
| 2.1029 | 3800 | 0.0057        | -               |
| 2.1306 | 3850 | 0.0004        | -               |
| 2.1583 | 3900 | 0.0272        | -               |
| 2.1859 | 3950 | 0.0003        | -               |
| 2.2136 | 4000 | 0.006         | -               |
| 2.2413 | 4050 | 0.0044        | -               |
| 2.2690 | 4100 | 0.0003        | -               |
| 2.2966 | 4150 | 0.0167        | -               |
| 2.3243 | 4200 | 0.0048        | -               |
| 2.3520 | 4250 | 0.0086        | -               |
| 2.3796 | 4300 | 0.0051        | -               |
| 2.4073 | 4350 | 0.0003        | -               |
| 2.4350 | 4400 | 0.0037        | -               |
| 2.4626 | 4450 | 0.0003        | -               |
| 2.4903 | 4500 | 0.0021        | -               |
| 2.5180 | 4550 | 0.0003        | -               |
| 2.5457 | 4600 | 0.004         | -               |
| 2.5733 | 4650 | 0.0025        | -               |
| 2.6010 | 4700 | 0.0003        | -               |
| 2.6287 | 4750 | 0.0003        | -               |
| 2.6563 | 4800 | 0.0003        | -               |
| 2.6840 | 4850 | 0.0031        | -               |
| 2.7117 | 4900 | 0.0168        | -               |
| 2.7393 | 4950 | 0.0019        | -               |
| 2.7670 | 5000 | 0.004         | -               |
| 2.7947 | 5050 | 0.0003        | -               |
| 2.8224 | 5100 | 0.0003        | -               |
| 2.8500 | 5150 | 0.003         | -               |
| 2.8777 | 5200 | 0.0003        | -               |
| 2.9054 | 5250 | 0.0003        | -               |
| 2.9330 | 5300 | 0.0171        | -               |
| 2.9607 | 5350 | 0.0003        | -               |
| 2.9884 | 5400 | 0.0162        | -               |
| 3.0160 | 5450 | 0.0143        | -               |
| 3.0437 | 5500 | 0.0134        | -               |
| 3.0714 | 5550 | 0.0133        | -               |
| 3.0991 | 5600 | 0.0003        | -               |
| 3.1267 | 5650 | 0.0003        | -               |
| 3.1544 | 5700 | 0.0093        | -               |
| 3.1821 | 5750 | 0.0003        | -               |
| 3.2097 | 5800 | 0.0003        | -               |
| 3.2374 | 5850 | 0.0003        | -               |
| 3.2651 | 5900 | 0.0003        | -               |
| 3.2928 | 5950 | 0.0003        | -               |
| 3.3204 | 6000 | 0.0029        | -               |
| 3.3481 | 6050 | 0.0126        | -               |
| 3.3758 | 6100 | 0.0003        | -               |
| 3.4034 | 6150 | 0.0002        | -               |
| 3.4311 | 6200 | 0.0003        | -               |
| 3.4588 | 6250 | 0.0062        | -               |
| 3.4864 | 6300 | 0.0002        | -               |
| 3.5141 | 6350 | 0.0002        | -               |
| 3.5418 | 6400 | 0.0003        | -               |
| 3.5695 | 6450 | 0.0002        | -               |
| 3.5971 | 6500 | 0.0041        | -               |
| 3.6248 | 6550 | 0.0465        | -               |
| 3.6525 | 6600 | 0.0148        | -               |
| 3.6801 | 6650 | 0.0181        | -               |
| 3.7078 | 6700 | 0.0037        | -               |
| 3.7355 | 6750 | 0.0002        | -               |
| 3.7631 | 6800 | 0.0003        | -               |
| 3.7908 | 6850 | 0.0003        | -               |
| 3.8185 | 6900 | 0.0034        | -               |
| 3.8462 | 6950 | 0.0002        | -               |
| 3.8738 | 7000 | 0.0148        | -               |
| 3.9015 | 7050 | 0.0002        | -               |
| 3.9292 | 7100 | 0.0003        | -               |
| 3.9568 | 7150 | 0.0002        | -               |
| 3.9845 | 7200 | 0.0003        | -               |

### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.1
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## 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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