setfit / README.md

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	---
	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: 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
	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: 0.9058823529411765
	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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