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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: 'fuel_network Fuel The worlds fastest modular execution layer Sway Language '
	- text: 'enjin Enjin Enjin Blockchain allows seamless no code integration of NFTs
	in video games and other platforms with NFT functions at the protocol level '
	- text: 'bobbyclee Bobby Lee Ballet Worlds EASIEST Cold Storage Founder CEO of was
	Board Member Cofounder BTCChina BTCC Author of The Promise of Bitcoin available
	on '
	- text: 'tradermayne Mayne '
	- text: 'novogratz Mike Novogratz CEO GLXY CN Early Investormushroom TheBailProject
	Disclaimer '
	pipeline_tag: text-classification
	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: 0.99
	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 \|
	\|:---------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| ORGANIZATIONAL \| <ul><li>'cryptonewton Shelby BitGet partner '</li><li>'trezor Trezor Crypto security made easy'</li><li>'forbes Forbes Sign up now for Forbes free daily newsletter for unmatched insights and exclusive reporting '</li></ul> \|
	\| INDIVIDUAL \| <ul><li>'anbessa100 ANBESSA No paid service Never DM u'</li><li>'sbf_ftx SBF '</li><li>'machibigbrother Machi Big Brother '</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.99 \|

	## 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("kasparas12/is_organizational_model")
	# Run inference
	preds = model("tradermayne Mayne ")
	```

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	List how someone could finetune this model on their own dataset.
	-->

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 3 \| 15.7338 \| 35 \|

	\| Label \| Training Sample Count \|
	\|:---------------\|:----------------------\|
	\| INDIVIDUAL \| 423 \|
	\| ORGANIZATIONAL \| 377 \|

	### Training Hyperparameters
	- batch_size: (32, 32)
	- num_epochs: (1, 1)
	- 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 \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0016 \| 1 \| 0.2511 \| - \|
	\| 0.0789 \| 50 \| 0.2505 \| - \|
	\| 0.1577 \| 100 \| 0.2225 \| - \|
	\| 0.2366 \| 150 \| 0.2103 \| - \|
	\| 0.3155 \| 200 \| 0.1383 \| - \|
	\| 0.3943 \| 250 \| 0.0329 \| - \|
	\| 0.4732 \| 300 \| 0.0098 \| - \|
	\| 0.5521 \| 350 \| 0.0034 \| - \|
	\| 0.6309 \| 400 \| 0.0019 \| - \|
	\| 0.7098 \| 450 \| 0.0015 \| - \|
	\| 0.7886 \| 500 \| 0.0014 \| - \|
	\| 0.8675 \| 550 \| 0.0012 \| - \|
	\| 0.0001 \| 1 \| 0.2524 \| - \|
	\| 0.0050 \| 50 \| 0.2115 \| - \|
	\| 0.0099 \| 100 \| 0.193 \| - \|
	\| 0.0001 \| 1 \| 0.2424 \| - \|
	\| 0.0050 \| 50 \| 0.2038 \| - \|
	\| 0.0099 \| 100 \| 0.1782 \| - \|
	\| 0.0001 \| 1 \| 0.2208 \| - \|
	\| 0.0050 \| 50 \| 0.1931 \| - \|
	\| 0.0099 \| 100 \| 0.1629 \| - \|
	\| 0.0149 \| 150 \| 0.2716 \| - \|
	\| 0.0199 \| 200 \| 0.18 \| - \|
	\| 0.0249 \| 250 \| 0.2504 \| - \|
	\| 0.0298 \| 300 \| 0.1936 \| - \|
	\| 0.0348 \| 350 \| 0.1764 \| - \|
	\| 0.0398 \| 400 \| 0.1817 \| - \|
	\| 0.0447 \| 450 \| 0.0624 \| - \|
	\| 0.0497 \| 500 \| 0.1183 \| - \|
	\| 0.0547 \| 550 \| 0.0793 \| - \|
	\| 0.0596 \| 600 \| 0.0281 \| - \|
	\| 0.0646 \| 650 \| 0.0876 \| - \|
	\| 0.0696 \| 700 \| 0.1701 \| - \|
	\| 0.0746 \| 750 \| 0.0468 \| - \|
	\| 0.0795 \| 800 \| 0.0525 \| - \|
	\| 0.0845 \| 850 \| 0.0783 \| - \|
	\| 0.0895 \| 900 \| 0.0342 \| - \|
	\| 0.0944 \| 950 \| 0.0158 \| - \|
	\| 0.0994 \| 1000 \| 0.0286 \| - \|
	\| 0.1044 \| 1050 \| 0.0016 \| - \|
	\| 0.1094 \| 1100 \| 0.0014 \| - \|
	\| 0.1143 \| 1150 \| 0.0298 \| - \|
	\| 0.1193 \| 1200 \| 0.018 \| - \|
	\| 0.1243 \| 1250 \| 0.0299 \| - \|
	\| 0.1292 \| 1300 \| 0.0019 \| - \|
	\| 0.1342 \| 1350 \| 0.0253 \| - \|
	\| 0.1392 \| 1400 \| 0.0009 \| - \|
	\| 0.1441 \| 1450 \| 0.0009 \| - \|
	\| 0.1491 \| 1500 \| 0.0011 \| - \|
	\| 0.1541 \| 1550 \| 0.0006 \| - \|
	\| 0.1591 \| 1600 \| 0.0006 \| - \|
	\| 0.1640 \| 1650 \| 0.0008 \| - \|
	\| 0.1690 \| 1700 \| 0.0005 \| - \|
	\| 0.1740 \| 1750 \| 0.0007 \| - \|
	\| 0.1789 \| 1800 \| 0.0006 \| - \|
	\| 0.1839 \| 1850 \| 0.0006 \| - \|
	\| 0.1889 \| 1900 \| 0.0006 \| - \|
	\| 0.1939 \| 1950 \| 0.0012 \| - \|
	\| 0.1988 \| 2000 \| 0.0004 \| - \|
	\| 0.2038 \| 2050 \| 0.0006 \| - \|
	\| 0.2088 \| 2100 \| 0.0005 \| - \|
	\| 0.2137 \| 2150 \| 0.0005 \| - \|
	\| 0.2187 \| 2200 \| 0.0005 \| - \|
	\| 0.2237 \| 2250 \| 0.0004 \| - \|
	\| 0.2287 \| 2300 \| 0.0005 \| - \|
	\| 0.2336 \| 2350 \| 0.0004 \| - \|
	\| 0.2386 \| 2400 \| 0.0004 \| - \|
	\| 0.2436 \| 2450 \| 0.0003 \| - \|
	\| 0.2485 \| 2500 \| 0.0004 \| - \|
	\| 0.2535 \| 2550 \| 0.0004 \| - \|
	\| 0.2585 \| 2600 \| 0.0004 \| - \|
	\| 0.2634 \| 2650 \| 0.0004 \| - \|
	\| 0.2684 \| 2700 \| 0.0004 \| - \|
	\| 0.2734 \| 2750 \| 0.0004 \| - \|
	\| 0.2784 \| 2800 \| 0.0056 \| - \|
	\| 0.2833 \| 2850 \| 0.0004 \| - \|
	\| 0.2883 \| 2900 \| 0.0003 \| - \|
	\| 0.2933 \| 2950 \| 0.0003 \| - \|
	\| 0.2982 \| 3000 \| 0.0004 \| - \|
	\| 0.3032 \| 3050 \| 0.0003 \| - \|
	\| 0.3082 \| 3100 \| 0.0003 \| - \|
	\| 0.3132 \| 3150 \| 0.0003 \| - \|
	\| 0.3181 \| 3200 \| 0.0003 \| - \|
	\| 0.3231 \| 3250 \| 0.0004 \| - \|
	\| 0.3281 \| 3300 \| 0.0003 \| - \|
	\| 0.3330 \| 3350 \| 0.0003 \| - \|
	\| 0.3380 \| 3400 \| 0.0003 \| - \|
	\| 0.3430 \| 3450 \| 0.0003 \| - \|
	\| 0.3479 \| 3500 \| 0.0003 \| - \|
	\| 0.3529 \| 3550 \| 0.0003 \| - \|
	\| 0.3579 \| 3600 \| 0.0003 \| - \|
	\| 0.3629 \| 3650 \| 0.0003 \| - \|
	\| 0.3678 \| 3700 \| 0.0003 \| - \|
	\| 0.3728 \| 3750 \| 0.0004 \| - \|
	\| 0.3778 \| 3800 \| 0.0004 \| - \|
	\| 0.3827 \| 3850 \| 0.0003 \| - \|
	\| 0.3877 \| 3900 \| 0.0003 \| - \|
	\| 0.3927 \| 3950 \| 0.0003 \| - \|
	\| 0.3977 \| 4000 \| 0.0003 \| - \|
	\| 0.4026 \| 4050 \| 0.0003 \| - \|
	\| 0.4076 \| 4100 \| 0.0003 \| - \|
	\| 0.4126 \| 4150 \| 0.0003 \| - \|
	\| 0.4175 \| 4200 \| 0.0003 \| - \|
	\| 0.4225 \| 4250 \| 0.0003 \| - \|
	\| 0.4275 \| 4300 \| 0.0003 \| - \|
	\| 0.4324 \| 4350 \| 0.0003 \| - \|
	\| 0.4374 \| 4400 \| 0.0002 \| - \|
	\| 0.4424 \| 4450 \| 0.0003 \| - \|
	\| 0.4474 \| 4500 \| 0.0003 \| - \|
	\| 0.4523 \| 4550 \| 0.0003 \| - \|
	\| 0.4573 \| 4600 \| 0.0003 \| - \|
	\| 0.4623 \| 4650 \| 0.0003 \| - \|
	\| 0.4672 \| 4700 \| 0.0002 \| - \|
	\| 0.4722 \| 4750 \| 0.0002 \| - \|
	\| 0.4772 \| 4800 \| 0.0003 \| - \|
	\| 0.4822 \| 4850 \| 0.0002 \| - \|
	\| 0.4871 \| 4900 \| 0.0002 \| - \|
	\| 0.4921 \| 4950 \| 0.0002 \| - \|
	\| 0.4971 \| 5000 \| 0.0003 \| - \|
	\| 0.5020 \| 5050 \| 0.0003 \| - \|
	\| 0.5070 \| 5100 \| 0.0002 \| - \|
	\| 0.5120 \| 5150 \| 0.0003 \| - \|
	\| 0.5169 \| 5200 \| 0.0002 \| - \|
	\| 0.5219 \| 5250 \| 0.0002 \| - \|
	\| 0.5269 \| 5300 \| 0.0002 \| - \|
	\| 0.5319 \| 5350 \| 0.0002 \| - \|
	\| 0.5368 \| 5400 \| 0.0003 \| - \|
	\| 0.5418 \| 5450 \| 0.0002 \| - \|
	\| 0.5468 \| 5500 \| 0.0002 \| - \|
	\| 0.5517 \| 5550 \| 0.0002 \| - \|
	\| 0.5567 \| 5600 \| 0.0002 \| - \|
	\| 0.5617 \| 5650 \| 0.0002 \| - \|
	\| 0.5667 \| 5700 \| 0.0002 \| - \|
	\| 0.5716 \| 5750 \| 0.0002 \| - \|
	\| 0.5766 \| 5800 \| 0.0002 \| - \|
	\| 0.5816 \| 5850 \| 0.0002 \| - \|
	\| 0.5865 \| 5900 \| 0.0002 \| - \|
	\| 0.5915 \| 5950 \| 0.0002 \| - \|
	\| 0.5965 \| 6000 \| 0.0002 \| - \|
	\| 0.6015 \| 6050 \| 0.0002 \| - \|
	\| 0.6064 \| 6100 \| 0.0002 \| - \|
	\| 0.6114 \| 6150 \| 0.0002 \| - \|
	\| 0.6164 \| 6200 \| 0.0002 \| - \|
	\| 0.6213 \| 6250 \| 0.0002 \| - \|
	\| 0.6263 \| 6300 \| 0.0002 \| - \|
	\| 0.6313 \| 6350 \| 0.0002 \| - \|
	\| 0.6362 \| 6400 \| 0.0002 \| - \|
	\| 0.6412 \| 6450 \| 0.0002 \| - \|
	\| 0.6462 \| 6500 \| 0.0002 \| - \|
	\| 0.6512 \| 6550 \| 0.0002 \| - \|
	\| 0.6561 \| 6600 \| 0.0002 \| - \|
	\| 0.6611 \| 6650 \| 0.0002 \| - \|
	\| 0.6661 \| 6700 \| 0.0002 \| - \|
	\| 0.6710 \| 6750 \| 0.0002 \| - \|
	\| 0.6760 \| 6800 \| 0.0002 \| - \|
	\| 0.6810 \| 6850 \| 0.0002 \| - \|
	\| 0.6860 \| 6900 \| 0.0002 \| - \|
	\| 0.6909 \| 6950 \| 0.0002 \| - \|
	\| 0.6959 \| 7000 \| 0.0002 \| - \|
	\| 0.7009 \| 7050 \| 0.0002 \| - \|
	\| 0.7058 \| 7100 \| 0.0002 \| - \|
	\| 0.7108 \| 7150 \| 0.0002 \| - \|
	\| 0.7158 \| 7200 \| 0.0002 \| - \|
	\| 0.7207 \| 7250 \| 0.0002 \| - \|
	\| 0.7257 \| 7300 \| 0.0002 \| - \|
	\| 0.7307 \| 7350 \| 0.0002 \| - \|
	\| 0.7357 \| 7400 \| 0.0002 \| - \|
	\| 0.7406 \| 7450 \| 0.0002 \| - \|
	\| 0.7456 \| 7500 \| 0.0002 \| - \|
	\| 0.7506 \| 7550 \| 0.0002 \| - \|
	\| 0.7555 \| 7600 \| 0.0002 \| - \|
	\| 0.7605 \| 7650 \| 0.0002 \| - \|
	\| 0.7655 \| 7700 \| 0.0248 \| - \|
	\| 0.7705 \| 7750 \| 0.0002 \| - \|
	\| 0.7754 \| 7800 \| 0.0002 \| - \|
	\| 0.7804 \| 7850 \| 0.0002 \| - \|
	\| 0.7854 \| 7900 \| 0.0002 \| - \|
	\| 0.7903 \| 7950 \| 0.0002 \| - \|
	\| 0.7953 \| 8000 \| 0.0002 \| - \|
	\| 0.8003 \| 8050 \| 0.0002 \| - \|
	\| 0.8052 \| 8100 \| 0.0002 \| - \|
	\| 0.8102 \| 8150 \| 0.0002 \| - \|
	\| 0.8152 \| 8200 \| 0.0002 \| - \|
	\| 0.8202 \| 8250 \| 0.0002 \| - \|
	\| 0.8251 \| 8300 \| 0.0002 \| - \|
	\| 0.8301 \| 8350 \| 0.0002 \| - \|
	\| 0.8351 \| 8400 \| 0.0002 \| - \|
	\| 0.8400 \| 8450 \| 0.0001 \| - \|
	\| 0.8450 \| 8500 \| 0.0002 \| - \|
	\| 0.8500 \| 8550 \| 0.0002 \| - \|
	\| 0.8550 \| 8600 \| 0.0001 \| - \|
	\| 0.8599 \| 8650 \| 0.0002 \| - \|
	\| 0.8649 \| 8700 \| 0.0002 \| - \|
	\| 0.8699 \| 8750 \| 0.0002 \| - \|
	\| 0.8748 \| 8800 \| 0.0002 \| - \|
	\| 0.8798 \| 8850 \| 0.0002 \| - \|
	\| 0.8848 \| 8900 \| 0.0002 \| - \|
	\| 0.8898 \| 8950 \| 0.0003 \| - \|
	\| 0.8947 \| 9000 \| 0.0002 \| - \|
	\| 0.8997 \| 9050 \| 0.0001 \| - \|
	\| 0.9047 \| 9100 \| 0.0002 \| - \|
	\| 0.9096 \| 9150 \| 0.0002 \| - \|
	\| 0.9146 \| 9200 \| 0.0002 \| - \|
	\| 0.9196 \| 9250 \| 0.0002 \| - \|
	\| 0.9245 \| 9300 \| 0.0002 \| - \|
	\| 0.9295 \| 9350 \| 0.0002 \| - \|
	\| 0.9345 \| 9400 \| 0.0002 \| - \|
	\| 0.9395 \| 9450 \| 0.0002 \| - \|
	\| 0.9444 \| 9500 \| 0.0002 \| - \|
	\| 0.9494 \| 9550 \| 0.0001 \| - \|
	\| 0.9544 \| 9600 \| 0.0001 \| - \|
	\| 0.9593 \| 9650 \| 0.0002 \| - \|
	\| 0.9643 \| 9700 \| 0.0002 \| - \|
	\| 0.9693 \| 9750 \| 0.0002 \| - \|
	\| 0.9743 \| 9800 \| 0.0001 \| - \|
	\| 0.9792 \| 9850 \| 0.0002 \| - \|
	\| 0.9842 \| 9900 \| 0.0002 \| - \|
	\| 0.9892 \| 9950 \| 0.0002 \| - \|
	\| 0.9941 \| 10000 \| 0.0002 \| - \|
	\| 0.9991 \| 10050 \| 0.0002 \| - \|

	### 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: 0.15.1

	## 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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