---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: approach affects entrepreneurship intention
- text: innovation affects m & a success
- text: total retail sales affects m & a success
- text: stimulation of the sales staff in business organization affects entrepreneurship
intention
- text: country-level economy affects ceo pay
pipeline_tag: text-classification
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: 0.8117647058823529
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
### 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 |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | - 'oecd affects ceo pay'
- 'marketing and sales affects entrepreneurship intention'
- 'australian research affects entrepreneurship intention'
|
| 1 | - 'academic performance affects entrepreneurship intention'
- 'collectivism affects entrepreneurship intention'
- 'responsibility affects ceo pay'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.8118 |
## 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("innovation affects m & a success")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 4 | 5.4661 | 13 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 164 |
| 1 | 175 |
### 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 | 0.303 | - |
| 0.0276 | 50 | 0.2825 | - |
| 0.0553 | 100 | 0.2567 | - |
| 0.0829 | 150 | 0.2345 | - |
| 0.1106 | 200 | 0.2347 | - |
| 0.1382 | 250 | 0.1693 | - |
| 0.1658 | 300 | 0.0862 | - |
| 0.1935 | 350 | 0.0184 | - |
| 0.2211 | 400 | 0.0042 | - |
| 0.2488 | 450 | 0.0042 | - |
| 0.2764 | 500 | 0.0263 | - |
| 0.3040 | 550 | 0.0019 | - |
| 0.3317 | 600 | 0.0058 | - |
| 0.3593 | 650 | 0.0095 | - |
| 0.3870 | 700 | 0.0011 | - |
| 0.4146 | 750 | 0.0012 | - |
| 0.4422 | 800 | 0.0009 | - |
| 0.4699 | 850 | 0.0011 | - |
| 0.4975 | 900 | 0.001 | - |
| 0.5252 | 950 | 0.0215 | - |
| 0.5528 | 1000 | 0.0024 | - |
| 0.5804 | 1050 | 0.0034 | - |
| 0.6081 | 1100 | 0.0008 | - |
| 0.6357 | 1150 | 0.0161 | - |
| 0.6633 | 1200 | 0.0132 | - |
| 0.6910 | 1250 | 0.0009 | - |
| 0.7186 | 1300 | 0.0073 | - |
| 0.7463 | 1350 | 0.0089 | - |
| 0.7739 | 1400 | 0.0166 | - |
| 0.8015 | 1450 | 0.0005 | - |
| 0.8292 | 1500 | 0.0005 | - |
| 0.8568 | 1550 | 0.0006 | - |
| 0.8845 | 1600 | 0.0098 | - |
| 0.9121 | 1650 | 0.0005 | - |
| 0.9397 | 1700 | 0.0005 | - |
| 0.9674 | 1750 | 0.0263 | - |
| 0.9950 | 1800 | 0.0006 | - |
| 1.0227 | 1850 | 0.0005 | - |
| 1.0503 | 1900 | 0.0089 | - |
| 1.0779 | 1950 | 0.0074 | - |
| 1.1056 | 2000 | 0.0057 | - |
| 1.1332 | 2050 | 0.0006 | - |
| 1.1609 | 2100 | 0.0004 | - |
| 1.1885 | 2150 | 0.0004 | - |
| 1.2161 | 2200 | 0.0006 | - |
| 1.2438 | 2250 | 0.0005 | - |
| 1.2714 | 2300 | 0.0004 | - |
| 1.2991 | 2350 | 0.0088 | - |
| 1.3267 | 2400 | 0.0004 | - |
| 1.3543 | 2450 | 0.0005 | - |
| 1.3820 | 2500 | 0.0004 | - |
| 1.4096 | 2550 | 0.0118 | - |
| 1.4373 | 2600 | 0.0004 | - |
| 1.4649 | 2650 | 0.0149 | - |
| 1.4925 | 2700 | 0.0004 | - |
| 1.5202 | 2750 | 0.0004 | - |
| 1.5478 | 2800 | 0.0003 | - |
| 1.5755 | 2850 | 0.0004 | - |
| 1.6031 | 2900 | 0.0004 | - |
| 1.6307 | 2950 | 0.0136 | - |
| 1.6584 | 3000 | 0.0083 | - |
| 1.6860 | 3050 | 0.0094 | - |
| 1.7137 | 3100 | 0.0088 | - |
| 1.7413 | 3150 | 0.0004 | - |
| 1.7689 | 3200 | 0.0003 | - |
| 1.7966 | 3250 | 0.0004 | - |
| 1.8242 | 3300 | 0.0004 | - |
| 1.8519 | 3350 | 0.0101 | - |
| 1.8795 | 3400 | 0.0112 | - |
| 1.9071 | 3450 | 0.0003 | - |
| 1.9348 | 3500 | 0.0117 | - |
| 1.9624 | 3550 | 0.0003 | - |
| 1.9900 | 3600 | 0.0003 | - |
| 2.0177 | 3650 | 0.0003 | - |
| 2.0453 | 3700 | 0.0083 | - |
| 2.0730 | 3750 | 0.0003 | - |
| 2.1006 | 3800 | 0.0132 | - |
| 2.1282 | 3850 | 0.0003 | - |
| 2.1559 | 3900 | 0.0003 | - |
| 2.1835 | 3950 | 0.0003 | - |
| 2.2112 | 4000 | 0.0004 | - |
| 2.2388 | 4050 | 0.0003 | - |
| 2.2664 | 4100 | 0.0003 | - |
| 2.2941 | 4150 | 0.0003 | - |
| 2.3217 | 4200 | 0.0003 | - |
| 2.3494 | 4250 | 0.0003 | - |
| 2.3770 | 4300 | 0.0079 | - |
| 2.4046 | 4350 | 0.0003 | - |
| 2.4323 | 4400 | 0.0003 | - |
| 2.4599 | 4450 | 0.0003 | - |
| 2.4876 | 4500 | 0.0057 | - |
| 2.5152 | 4550 | 0.0003 | - |
| 2.5428 | 4600 | 0.0003 | - |
| 2.5705 | 4650 | 0.0003 | - |
| 2.5981 | 4700 | 0.0003 | - |
| 2.6258 | 4750 | 0.0003 | - |
| 2.6534 | 4800 | 0.0003 | - |
| 2.6810 | 4850 | 0.0003 | - |
| 2.7087 | 4900 | 0.0003 | - |
| 2.7363 | 4950 | 0.0003 | - |
| 2.7640 | 5000 | 0.0019 | - |
| 2.7916 | 5050 | 0.0157 | - |
| 2.8192 | 5100 | 0.0003 | - |
| 2.8469 | 5150 | 0.0098 | - |
| 2.8745 | 5200 | 0.0003 | - |
| 2.9022 | 5250 | 0.0117 | - |
| 2.9298 | 5300 | 0.0003 | - |
| 2.9574 | 5350 | 0.0003 | - |
| 2.9851 | 5400 | 0.0087 | - |
| 3.0127 | 5450 | 0.0002 | - |
| 3.0404 | 5500 | 0.0003 | - |
| 3.0680 | 5550 | 0.0085 | - |
| 3.0956 | 5600 | 0.0159 | - |
| 3.1233 | 5650 | 0.0003 | - |
| 3.1509 | 5700 | 0.0053 | - |
| 3.1786 | 5750 | 0.0003 | - |
| 3.2062 | 5800 | 0.0086 | - |
| 3.2338 | 5850 | 0.0002 | - |
| 3.2615 | 5900 | 0.0003 | - |
| 3.2891 | 5950 | 0.0055 | - |
| 3.3167 | 6000 | 0.0002 | - |
| 3.3444 | 6050 | 0.0092 | - |
| 3.3720 | 6100 | 0.0153 | - |
| 3.3997 | 6150 | 0.0002 | - |
| 3.4273 | 6200 | 0.0002 | - |
| 3.4549 | 6250 | 0.0002 | - |
| 3.4826 | 6300 | 0.0003 | - |
| 3.5102 | 6350 | 0.0101 | - |
| 3.5379 | 6400 | 0.0003 | - |
| 3.5655 | 6450 | 0.0003 | - |
| 3.5931 | 6500 | 0.0091 | - |
| 3.6208 | 6550 | 0.0002 | - |
| 3.6484 | 6600 | 0.0085 | - |
| 3.6761 | 6650 | 0.0003 | - |
| 3.7037 | 6700 | 0.0002 | - |
| 3.7313 | 6750 | 0.0002 | - |
| 3.7590 | 6800 | 0.0068 | - |
| 3.7866 | 6850 | 0.0003 | - |
| 3.8143 | 6900 | 0.0079 | - |
| 3.8419 | 6950 | 0.0175 | - |
| 3.8695 | 7000 | 0.0066 | - |
| 3.8972 | 7050 | 0.0003 | - |
| 3.9248 | 7100 | 0.0002 | - |
| 3.9525 | 7150 | 0.0065 | - |
| 3.9801 | 7200 | 0.0094 | - |
### 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}
}
```