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	---
	base_model: BAAI/bge-base-en-v1.5
	library_name: setfit
	metrics:
	- f1
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Discussion on recent report publication
	- text: Growth
	- text: The roundtable was arranged in order to provide an overview of the work of
	Alliance members and promote international development policy positions to the
	Scottish Conservatives. During the meeting we presented the work of SCIAF and
	its campaign for a world leading climate change response. In particular SCIAF
	explained how climate change is already affecting some of the poorest communities
	in the world and is therefore a central concern for international development.
	We argued that Scotland needs to do what it can to mitigate climate change.
	- text: To introduce Energy UK discuss the energy industries contribution to tackling
	climate change and discuss stage 1 of theClimate Change (Emissions Reduction Targets)
	(Scotland) Bill. Also discussed the Scottish Government's ambition on electric
	vehicles and the role of the energy industry in a successful roll out.
	- text: To discuss our key asks on the Climate Change (Emissions Reduction Targets)
	(Scotland) Bill in advance of Stage 2 including support for amendments on regional
	land use partnerships and land use strategy as means to deliver climate mitigation
	for land.
	inference: True
	model-index:
	- name: SetFit with BAAI/bge-base-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: f1
	value: 0.9667149059334297
	name: F1
	- type: accuracy
	value: 0.9420654911838791
	name: Accuracy
	---

	# SetFit with BAAI/bge-base-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-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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.

	undefined = Health
	1 = Housing
	2 = Defence
	3 = Climate

	## Model Details

	### Model Description
	- Model Type: SetFit
	- Sentence Transformer body: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)
	- Classification head: a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
	- Maximum Sequence Length: 512 tokens
	<!-- - Number of Classes: Unknown -->
	<!-- - 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)

	## Evaluation

	### Metrics
	\| Label \| F1 \| Accuracy \|
	\|:--------\|:-------\|:---------\|
	\| all \| 0.9667 \| 0.9421 \|

	## 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("twright8/setfit_lobbying_classifier")
	# Run inference
	preds = model("Growth")
	```

	<!--
	### Downstream Use

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

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:--------\|:----\|
	\| Word count \| 1 \| 39.4538 \| 282 \|

	### Training Hyperparameters
	- batch_size: (16, 2)
	- num_epochs: (4, 9)
	- max_steps: -1
	- sampling_strategy: undersampling
	- body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05)
	- head_learning_rate: 0.0004470582121407239
	- loss: CoSENTLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: True
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0002 \| 1 \| 2.097 \| - \|
	\| 0.0077 \| 50 \| 8.5514 \| - \|
	\| 0.0155 \| 100 \| 3.5635 \| - \|
	\| 0.0232 \| 150 \| 2.9266 \| - \|
	\| 0.0310 \| 200 \| 2.1173 \| - \|
	\| 0.0387 \| 250 \| 3.1002 \| - \|
	\| 0.0465 \| 300 \| 3.6942 \| - \|
	\| 0.0542 \| 350 \| 3.4905 \| - \|
	\| 0.0620 \| 400 \| 4.0804 \| - \|
	\| 0.0697 \| 450 \| 1.6071 \| - \|
	\| 0.0774 \| 500 \| 2.3018 \| - \|
	\| 0.0852 \| 550 \| 2.3876 \| - \|
	\| 0.0929 \| 600 \| 0.2511 \| - \|
	\| 0.1007 \| 650 \| 0.2435 \| - \|
	\| 0.1084 \| 700 \| 2.2596 \| - \|
	\| 0.1162 \| 750 \| 1.121 \| - \|
	\| 0.1239 \| 800 \| 0.0907 \| - \|
	\| 0.1317 \| 850 \| 0.2172 \| - \|
	\| 0.1394 \| 900 \| 3.06 \| - \|
	\| 0.1471 \| 950 \| 0.0074 \| - \|
	\| 0.1549 \| 1000 \| 0.457 \| - \|
	\| 0.1626 \| 1050 \| 0.0575 \| - \|
	\| 0.1704 \| 1100 \| 0.0002 \| - \|
	\| 0.1781 \| 1150 \| 0.0003 \| - \|
	\| 0.1859 \| 1200 \| 0.0047 \| - \|
	\| 0.1936 \| 1250 \| 0.0004 \| - \|
	\| 0.2014 \| 1300 \| 0.0006 \| - \|
	\| 0.2091 \| 1350 \| 0.0027 \| - \|
	\| 0.2169 \| 1400 \| 0.0004 \| - \|
	\| 0.2246 \| 1450 \| 0.0009 \| - \|
	\| 0.2323 \| 1500 \| 0.0006 \| - \|
	\| 0.2401 \| 1550 \| 0.0003 \| - \|
	\| 0.2478 \| 1600 \| 0.0077 \| - \|
	\| 0.2556 \| 1650 \| 0.0004 \| - \|
	\| 0.2633 \| 1700 \| 0.0003 \| - \|
	\| 0.2711 \| 1750 \| 0.0005 \| - \|
	\| 0.2788 \| 1800 \| 0.0004 \| - \|
	\| 0.2866 \| 1850 \| 0.0007 \| - \|
	\| 0.2943 \| 1900 \| 0.0009 \| - \|
	\| 0.3020 \| 1950 \| 0.0062 \| - \|
	\| 0.3098 \| 2000 \| 0.0003 \| - \|
	\| 0.3175 \| 2050 \| 0.0001 \| - \|
	\| 0.3253 \| 2100 \| 0.0685 \| - \|
	\| 0.3330 \| 2150 \| 0.0008 \| - \|
	\| 0.3408 \| 2200 \| 0.0 \| - \|
	\| 0.3485 \| 2250 \| 0.0004 \| - \|
	\| 0.3563 \| 2300 \| 0.0004 \| - \|
	\| 0.3640 \| 2350 \| 0.0002 \| - \|
	\| 0.3717 \| 2400 \| 0.0001 \| - \|
	\| 0.3795 \| 2450 \| 0.0004 \| - \|
	\| 0.3872 \| 2500 \| 0.0004 \| - \|
	\| 0.3950 \| 2550 \| 0.0001 \| - \|
	\| 0.4027 \| 2600 \| 0.0001 \| - \|
	\| 0.4105 \| 2650 \| 0.0001 \| - \|
	\| 0.4182 \| 2700 \| 0.0005 \| - \|
	\| 0.4260 \| 2750 \| 0.0002 \| - \|
	\| 0.4337 \| 2800 \| 0.0001 \| - \|
	\| 0.4414 \| 2850 \| 0.0003 \| - \|
	\| 0.4492 \| 2900 \| 0.0005 \| - \|
	\| 0.4569 \| 2950 \| 0.0014 \| - \|
	\| 0.4647 \| 3000 \| 0.0001 \| - \|
	\| 0.4724 \| 3050 \| 0.0001 \| - \|
	\| 0.4802 \| 3100 \| 0.0002 \| - \|
	\| 0.4879 \| 3150 \| 0.0 \| - \|
	\| 0.4957 \| 3200 \| 0.0006 \| - \|
	\| 0.5034 \| 3250 \| 0.0 \| - \|
	\| 0.5112 \| 3300 \| 0.0 \| - \|
	\| 0.5189 \| 3350 \| 0.0002 \| - \|
	\| 0.5266 \| 3400 \| 0.0001 \| - \|
	\| 0.5344 \| 3450 \| 0.0006 \| - \|
	\| 0.5421 \| 3500 \| 0.0002 \| - \|
	\| 0.5499 \| 3550 \| 0.0001 \| - \|
	\| 0.5576 \| 3600 \| 0.0001 \| - \|
	\| 0.5654 \| 3650 \| 0.0001 \| - \|
	\| 0.5731 \| 3700 \| 0.0 \| - \|
	\| 0.5809 \| 3750 \| 0.0002 \| - \|
	\| 0.5886 \| 3800 \| 0.0 \| - \|
	\| 0.5963 \| 3850 \| 0.0044 \| - \|
	\| 0.6041 \| 3900 \| 0.0002 \| - \|
	\| 0.6118 \| 3950 \| 0.0001 \| - \|
	\| 0.6196 \| 4000 \| 0.0003 \| - \|
	\| 0.6273 \| 4050 \| 0.0005 \| - \|
	\| 0.6351 \| 4100 \| 0.0002 \| - \|
	\| 0.6428 \| 4150 \| 0.0 \| - \|
	\| 0.6506 \| 4200 \| 0.0003 \| - \|
	\| 0.6583 \| 4250 \| 0.0 \| - \|
	\| 0.6660 \| 4300 \| 0.0001 \| - \|
	\| 0.6738 \| 4350 \| 0.0 \| - \|
	\| 0.6815 \| 4400 \| 0.0008 \| - \|
	\| 0.6893 \| 4450 \| 0.0 \| - \|
	\| 0.6970 \| 4500 \| 0.0004 \| - \|
	\| 0.7048 \| 4550 \| 0.0001 \| - \|
	\| 0.7125 \| 4600 \| 0.0 \| - \|
	\| 0.7203 \| 4650 \| 0.0 \| - \|
	\| 0.7280 \| 4700 \| 0.0 \| - \|
	\| 0.7357 \| 4750 \| 0.0001 \| - \|
	\| 0.7435 \| 4800 \| 0.0001 \| - \|
	\| 0.7512 \| 4850 \| 0.001 \| - \|
	\| 0.7590 \| 4900 \| 0.0001 \| - \|
	\| 0.7667 \| 4950 \| 0.0 \| - \|
	\| 0.7745 \| 5000 \| 0.0001 \| - \|
	\| 0.7822 \| 5050 \| 0.0 \| - \|
	\| 0.7900 \| 5100 \| 0.0018 \| - \|
	\| 0.7977 \| 5150 \| 0.0001 \| - \|
	\| 0.8055 \| 5200 \| 0.0 \| - \|
	\| 0.8132 \| 5250 \| 0.0003 \| - \|
	\| 0.8209 \| 5300 \| 0.0003 \| - \|
	\| 0.8287 \| 5350 \| 0.0003 \| - \|
	\| 0.8364 \| 5400 \| 0.0001 \| - \|
	\| 0.8442 \| 5450 \| 0.0001 \| - \|
	\| 0.8519 \| 5500 \| 0.0001 \| - \|
	\| 0.8597 \| 5550 \| 0.0001 \| - \|
	\| 0.8674 \| 5600 \| 0.0001 \| - \|
	\| 0.8752 \| 5650 \| 0.0 \| - \|
	\| 0.8829 \| 5700 \| 0.0003 \| - \|
	\| 0.8906 \| 5750 \| 0.0003 \| - \|
	\| 0.8984 \| 5800 \| 0.0001 \| - \|
	\| 0.9061 \| 5850 \| 0.0001 \| - \|
	\| 0.9139 \| 5900 \| 0.0002 \| - \|
	\| 0.9216 \| 5950 \| 0.0 \| - \|
	\| 0.9294 \| 6000 \| 0.0001 \| - \|
	\| 0.9371 \| 6050 \| 0.0 \| - \|
	\| 0.9449 \| 6100 \| 0.0 \| - \|
	\| 0.9526 \| 6150 \| 0.0001 \| - \|
	\| 0.9603 \| 6200 \| 0.0 \| - \|
	\| 0.9681 \| 6250 \| 0.0001 \| - \|
	\| 0.9758 \| 6300 \| 0.0002 \| - \|
	\| 0.9836 \| 6350 \| 0.0 \| - \|
	\| 0.9913 \| 6400 \| 0.0 \| - \|
	\| 0.9991 \| 6450 \| 0.0002 \| - \|
	\| 1.0 \| 6456 \| - \| 1.3837 \|
	\| 1.0068 \| 6500 \| 0.0001 \| - \|
	\| 1.0146 \| 6550 \| 0.0001 \| - \|
	\| 1.0223 \| 6600 \| 0.0002 \| - \|
	\| 1.0300 \| 6650 \| 0.0001 \| - \|
	\| 1.0378 \| 6700 \| 0.0005 \| - \|
	\| 1.0455 \| 6750 \| 0.0001 \| - \|
	\| 1.0533 \| 6800 \| 0.0001 \| - \|
	\| 1.0610 \| 6850 \| 0.0 \| - \|
	\| 1.0688 \| 6900 \| 0.0 \| - \|
	\| 1.0765 \| 6950 \| 0.0009 \| - \|
	\| 1.0843 \| 7000 \| 0.0 \| - \|
	\| 1.0920 \| 7050 \| 0.0032 \| - \|
	\| 1.0998 \| 7100 \| 0.0001 \| - \|
	\| 1.1075 \| 7150 \| 0.0001 \| - \|
	\| 1.1152 \| 7200 \| 0.0001 \| - \|
	\| 1.1230 \| 7250 \| 0.0 \| - \|
	\| 1.1307 \| 7300 \| 0.0001 \| - \|
	\| 1.1385 \| 7350 \| 0.0 \| - \|
	\| 1.1462 \| 7400 \| 0.0 \| - \|
	\| 1.1540 \| 7450 \| 0.0002 \| - \|
	\| 1.1617 \| 7500 \| 0.0 \| - \|
	\| 1.1695 \| 7550 \| 0.0427 \| - \|
	\| 1.1772 \| 7600 \| 0.0 \| - \|
	\| 1.1849 \| 7650 \| 0.0 \| - \|
	\| 1.1927 \| 7700 \| 0.0 \| - \|
	\| 1.2004 \| 7750 \| 0.0002 \| - \|
	\| 1.2082 \| 7800 \| 0.0 \| - \|
	\| 1.2159 \| 7850 \| 0.0 \| - \|
	\| 1.2237 \| 7900 \| 0.0 \| - \|
	\| 1.2314 \| 7950 \| 0.0 \| - \|
	\| 1.2392 \| 8000 \| 0.0001 \| - \|
	\| 1.2469 \| 8050 \| 0.0 \| - \|
	\| 1.2546 \| 8100 \| 0.0001 \| - \|
	\| 1.2624 \| 8150 \| 0.0 \| - \|
	\| 1.2701 \| 8200 \| 0.0 \| - \|
	\| 1.2779 \| 8250 \| 0.0 \| - \|
	\| 1.2856 \| 8300 \| 0.0 \| - \|
	\| 1.2934 \| 8350 \| 0.0 \| - \|
	\| 1.3011 \| 8400 \| 0.0 \| - \|
	\| 1.3089 \| 8450 \| 0.0 \| - \|
	\| 1.3166 \| 8500 \| 0.0 \| - \|
	\| 1.3243 \| 8550 \| 0.0001 \| - \|
	\| 1.3321 \| 8600 \| 0.0 \| - \|
	\| 1.3398 \| 8650 \| 0.0002 \| - \|
	\| 1.3476 \| 8700 \| 0.0 \| - \|
	\| 1.3553 \| 8750 \| 0.0006 \| - \|
	\| 1.3631 \| 8800 \| 0.0 \| - \|
	\| 1.3708 \| 8850 \| 0.0 \| - \|
	\| 1.3786 \| 8900 \| 0.0001 \| - \|
	\| 1.3863 \| 8950 \| 0.0 \| - \|
	\| 1.3941 \| 9000 \| 0.0001 \| - \|
	\| 1.4018 \| 9050 \| 0.0 \| - \|
	\| 1.4095 \| 9100 \| 0.0002 \| - \|
	\| 1.4173 \| 9150 \| 0.0 \| - \|
	\| 1.4250 \| 9200 \| 0.0 \| - \|
	\| 1.4328 \| 9250 \| 0.0 \| - \|
	\| 1.4405 \| 9300 \| 0.0 \| - \|
	\| 1.4483 \| 9350 \| 0.0 \| - \|
	\| 1.4560 \| 9400 \| 0.0 \| - \|
	\| 1.4638 \| 9450 \| 0.0 \| - \|
	\| 1.4715 \| 9500 \| 0.0 \| - \|
	\| 1.4792 \| 9550 \| 0.0 \| - \|
	\| 1.4870 \| 9600 \| 0.0 \| - \|
	\| 1.4947 \| 9650 \| 0.0005 \| - \|
	\| 1.5025 \| 9700 \| 0.0 \| - \|
	\| 1.5102 \| 9750 \| 0.0001 \| - \|
	\| 1.5180 \| 9800 \| 0.0001 \| - \|
	\| 1.5257 \| 9850 \| 0.0001 \| - \|
	\| 1.5335 \| 9900 \| 0.0 \| - \|
	\| 1.5412 \| 9950 \| 0.0 \| - \|
	\| 1.5489 \| 10000 \| 0.0 \| - \|
	\| 1.5567 \| 10050 \| 0.0 \| - \|
	\| 1.5644 \| 10100 \| 0.0001 \| - \|
	\| 1.5722 \| 10150 \| 0.0 \| - \|
	\| 1.5799 \| 10200 \| 0.0002 \| - \|
	\| 1.5877 \| 10250 \| 0.0001 \| - \|
	\| 1.5954 \| 10300 \| 0.0005 \| - \|
	\| 1.6032 \| 10350 \| 0.0 \| - \|
	\| 1.6109 \| 10400 \| 0.0 \| - \|
	\| 1.6186 \| 10450 \| 0.0003 \| - \|
	\| 1.6264 \| 10500 \| 0.0002 \| - \|
	\| 1.6341 \| 10550 \| 0.0 \| - \|
	\| 1.6419 \| 10600 \| 0.0 \| - \|
	\| 1.6496 \| 10650 \| 0.0001 \| - \|
	\| 1.6574 \| 10700 \| 0.0002 \| - \|
	\| 1.6651 \| 10750 \| 0.0002 \| - \|
	\| 1.6729 \| 10800 \| 0.0054 \| - \|
	\| 1.6806 \| 10850 \| 0.0005 \| - \|
	\| 1.6884 \| 10900 \| 0.0001 \| - \|
	\| 1.6961 \| 10950 \| 0.0 \| - \|
	\| 1.7038 \| 11000 \| 0.0 \| - \|
	\| 1.7116 \| 11050 \| 0.0001 \| - \|
	\| 1.7193 \| 11100 \| 0.0001 \| - \|
	\| 1.7271 \| 11150 \| 0.0 \| - \|
	\| 1.7348 \| 11200 \| 0.0001 \| - \|
	\| 1.7426 \| 11250 \| 0.0 \| - \|
	\| 1.7503 \| 11300 \| 0.0001 \| - \|
	\| 1.7581 \| 11350 \| 0.0004 \| - \|
	\| 1.7658 \| 11400 \| 0.0 \| - \|
	\| 1.7735 \| 11450 \| 0.0001 \| - \|
	\| 1.7813 \| 11500 \| 0.0 \| - \|
	\| 1.7890 \| 11550 \| 0.0 \| - \|
	\| 1.7968 \| 11600 \| 0.0 \| - \|
	\| 1.8045 \| 11650 \| 0.0 \| - \|
	\| 1.8123 \| 11700 \| 0.0001 \| - \|
	\| 1.8200 \| 11750 \| 0.0002 \| - \|
	\| 1.8278 \| 11800 \| 0.0 \| - \|
	\| 1.8355 \| 11850 \| 0.0001 \| - \|
	\| 1.8432 \| 11900 \| 0.0 \| - \|
	\| 1.8510 \| 11950 \| 0.0001 \| - \|
	\| 1.8587 \| 12000 \| 0.0 \| - \|
	\| 1.8665 \| 12050 \| 0.0 \| - \|
	\| 1.8742 \| 12100 \| 0.0 \| - \|
	\| 1.8820 \| 12150 \| 0.0001 \| - \|
	\| 1.8897 \| 12200 \| 0.0 \| - \|
	\| 1.8975 \| 12250 \| 0.0 \| - \|
	\| 1.9052 \| 12300 \| 0.0 \| - \|
	\| 1.9129 \| 12350 \| 0.0 \| - \|
	\| 1.9207 \| 12400 \| 0.0 \| - \|
	\| 1.9284 \| 12450 \| 0.0 \| - \|
	\| 1.9362 \| 12500 \| 0.0 \| - \|
	\| 1.9439 \| 12550 \| 0.0003 \| - \|
	\| 1.9517 \| 12600 \| 0.0001 \| - \|
	\| 1.9594 \| 12650 \| 0.0 \| - \|
	\| 1.9672 \| 12700 \| 0.0001 \| - \|
	\| 1.9749 \| 12750 \| 0.0 \| - \|
	\| 1.9827 \| 12800 \| 0.0 \| - \|
	\| 1.9904 \| 12850 \| 0.0 \| - \|
	\| 1.9981 \| 12900 \| 0.0001 \| - \|
	\| 2.0 \| 12912 \| - \| 2.611 \|
	\| 2.0059 \| 12950 \| 0.0 \| - \|
	\| 2.0136 \| 13000 \| 0.0001 \| - \|
	\| 2.0214 \| 13050 \| 0.0001 \| - \|
	\| 2.0291 \| 13100 \| 0.0 \| - \|
	\| 2.0369 \| 13150 \| 0.0 \| - \|
	\| 2.0446 \| 13200 \| 0.0001 \| - \|
	\| 2.0524 \| 13250 \| 0.0 \| - \|
	\| 2.0601 \| 13300 \| 0.0002 \| - \|
	\| 2.0678 \| 13350 \| 0.0 \| - \|
	\| 2.0756 \| 13400 \| 0.0 \| - \|
	\| 2.0833 \| 13450 \| 0.0001 \| - \|
	\| 2.0911 \| 13500 \| 0.0001 \| - \|
	\| 2.0988 \| 13550 \| 0.0003 \| - \|
	\| 2.1066 \| 13600 \| 0.0 \| - \|
	\| 2.1143 \| 13650 \| 0.0001 \| - \|
	\| 2.1221 \| 13700 \| 0.0001 \| - \|
	\| 2.1298 \| 13750 \| 0.0001 \| - \|
	\| 2.1375 \| 13800 \| 0.0001 \| - \|
	\| 2.1453 \| 13850 \| 0.0 \| - \|
	\| 2.1530 \| 13900 \| 0.0 \| - \|
	\| 2.1608 \| 13950 \| 0.0 \| - \|
	\| 2.1685 \| 14000 \| 0.0 \| - \|
	\| 2.1763 \| 14050 \| 0.0 \| - \|
	\| 2.1840 \| 14100 \| 0.0001 \| - \|
	\| 2.1918 \| 14150 \| 0.0 \| - \|
	\| 2.1995 \| 14200 \| 0.0 \| - \|
	\| 2.2072 \| 14250 \| 0.0001 \| - \|
	\| 2.2150 \| 14300 \| 0.0 \| - \|
	\| 2.2227 \| 14350 \| 0.0 \| - \|
	\| 2.2305 \| 14400 \| 0.0004 \| - \|
	\| 2.2382 \| 14450 \| 0.0001 \| - \|
	\| 2.2460 \| 14500 \| 0.0 \| - \|
	\| 2.2537 \| 14550 \| 0.0003 \| - \|
	\| 2.2615 \| 14600 \| 0.0 \| - \|
	\| 2.2692 \| 14650 \| 0.0001 \| - \|
	\| 2.2770 \| 14700 \| 0.0001 \| - \|
	\| 2.2847 \| 14750 \| 0.0 \| - \|
	\| 2.2924 \| 14800 \| 0.0 \| - \|
	\| 2.3002 \| 14850 \| 0.0005 \| - \|
	\| 2.3079 \| 14900 \| 0.0 \| - \|
	\| 2.3157 \| 14950 \| 0.0002 \| - \|
	\| 2.3234 \| 15000 \| 0.0 \| - \|
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	\| 2.3389 \| 15100 \| 0.0001 \| - \|
	\| 2.3467 \| 15150 \| 0.0001 \| - \|
	\| 2.3544 \| 15200 \| 0.0002 \| - \|
	\| 2.3621 \| 15250 \| 0.0001 \| - \|
	\| 2.3699 \| 15300 \| 0.0 \| - \|
	\| 2.3776 \| 15350 \| 0.0 \| - \|
	\| 2.3854 \| 15400 \| 0.0002 \| - \|
	\| 2.3931 \| 15450 \| 0.0003 \| - \|
	\| 2.4009 \| 15500 \| 0.0 \| - \|
	\| 2.4086 \| 15550 \| 0.0 \| - \|
	\| 2.4164 \| 15600 \| 0.0 \| - \|
	\| 2.4241 \| 15650 \| 0.0001 \| - \|
	\| 2.4318 \| 15700 \| 0.0 \| - \|
	\| 2.4396 \| 15750 \| 0.0 \| - \|
	\| 2.4473 \| 15800 \| 0.0002 \| - \|
	\| 2.4551 \| 15850 \| 0.0 \| - \|
	\| 2.4628 \| 15900 \| 0.0 \| - \|
	\| 2.4706 \| 15950 \| 0.0 \| - \|
	\| 2.4783 \| 16000 \| 0.0 \| - \|
	\| 2.4861 \| 16050 \| 0.0001 \| - \|
	\| 2.4938 \| 16100 \| 0.0 \| - \|
	\| 2.5015 \| 16150 \| 0.0 \| - \|
	\| 2.5093 \| 16200 \| 0.0 \| - \|
	\| 2.5170 \| 16250 \| 0.0 \| - \|
	\| 2.5248 \| 16300 \| 0.0 \| - \|
	\| 2.5325 \| 16350 \| 0.0 \| - \|
	\| 2.5403 \| 16400 \| 0.0 \| - \|
	\| 2.5480 \| 16450 \| 0.0 \| - \|
	\| 2.5558 \| 16500 \| 0.0 \| - \|
	\| 2.5635 \| 16550 \| 0.0001 \| - \|
	\| 2.5713 \| 16600 \| 0.0 \| - \|
	\| 2.5790 \| 16650 \| 0.0 \| - \|
	\| 2.5867 \| 16700 \| 0.0 \| - \|
	\| 2.5945 \| 16750 \| 0.0 \| - \|
	\| 2.6022 \| 16800 \| 0.0009 \| - \|
	\| 2.6100 \| 16850 \| 0.0001 \| - \|
	\| 2.6177 \| 16900 \| 0.0 \| - \|
	\| 2.6255 \| 16950 \| 0.0001 \| - \|
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	\| 2.6487 \| 17100 \| 0.0001 \| - \|
	\| 2.6564 \| 17150 \| 0.0 \| - \|
	\| 2.6642 \| 17200 \| 0.0 \| - \|
	\| 2.6719 \| 17250 \| 0.0 \| - \|
	\| 2.6797 \| 17300 \| 0.0 \| - \|
	\| 2.6874 \| 17350 \| 0.0004 \| - \|
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	\| 2.7107 \| 17500 \| 0.0 \| - \|
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	\| 2.7261 \| 17600 \| 0.0 \| - \|
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	\| 2.7416 \| 17700 \| 0.0001 \| - \|
	\| 2.7494 \| 17750 \| 0.0 \| - \|
	\| 2.7571 \| 17800 \| 0.0 \| - \|
	\| 2.7649 \| 17850 \| 0.0001 \| - \|
	\| 2.7726 \| 17900 \| 0.0 \| - \|
	\| 2.7804 \| 17950 \| 0.0001 \| - \|
	\| 2.7881 \| 18000 \| 0.0001 \| - \|
	\| 2.7958 \| 18050 \| 0.0 \| - \|
	\| 2.8036 \| 18100 \| 0.0 \| - \|
	\| 2.8113 \| 18150 \| 0.0 \| - \|
	\| 2.8191 \| 18200 \| 0.0 \| - \|
	\| 2.8268 \| 18250 \| 0.0 \| - \|
	\| 2.8346 \| 18300 \| 0.0001 \| - \|
	\| 2.8423 \| 18350 \| 0.0 \| - \|
	\| 2.8501 \| 18400 \| 0.0 \| - \|
	\| 2.8578 \| 18450 \| 0.0 \| - \|
	\| 2.8656 \| 18500 \| 0.0 \| - \|
	\| 2.8733 \| 18550 \| 0.0 \| - \|
	\| 2.8810 \| 18600 \| 0.0 \| - \|
	\| 2.8888 \| 18650 \| 0.0 \| - \|
	\| 2.8965 \| 18700 \| 0.0 \| - \|
	\| 2.9043 \| 18750 \| 0.0 \| - \|
	\| 2.9120 \| 18800 \| 0.0001 \| - \|
	\| 2.9198 \| 18850 \| 0.0 \| - \|
	\| 2.9275 \| 18900 \| 0.0 \| - \|
	\| 2.9353 \| 18950 \| 0.0 \| - \|
	\| 2.9430 \| 19000 \| 0.0 \| - \|
	\| 2.9507 \| 19050 \| 0.0 \| - \|
	\| 2.9585 \| 19100 \| 0.0 \| - \|
	\| 2.9662 \| 19150 \| 0.0 \| - \|
	\| 2.9740 \| 19200 \| 0.0 \| - \|
	\| 2.9817 \| 19250 \| 0.0003 \| - \|
	\| 2.9895 \| 19300 \| 0.0001 \| - \|
	\| 2.9972 \| 19350 \| 0.0 \| - \|
	\| 3.0 \| 19368 \| - \| 2.0845 \|
	\| 3.0050 \| 19400 \| 0.0 \| - \|
	\| 3.0127 \| 19450 \| 0.0001 \| - \|
	\| 3.0204 \| 19500 \| 0.0 \| - \|
	\| 3.0282 \| 19550 \| 0.0 \| - \|
	\| 3.0359 \| 19600 \| 0.0 \| - \|
	\| 3.0437 \| 19650 \| 0.0 \| - \|
	\| 3.0514 \| 19700 \| 0.0 \| - \|
	\| 3.0592 \| 19750 \| 0.0 \| - \|
	\| 3.0669 \| 19800 \| 0.0001 \| - \|
	\| 3.0747 \| 19850 \| 0.0 \| - \|
	\| 3.0824 \| 19900 \| 0.0 \| - \|
	\| 3.0901 \| 19950 \| 0.0001 \| - \|
	\| 3.0979 \| 20000 \| 0.0 \| - \|
	\| 3.1056 \| 20050 \| 0.0 \| - \|
	\| 3.1134 \| 20100 \| 0.0 \| - \|
	\| 3.1211 \| 20150 \| 0.0001 \| - \|
	\| 3.1289 \| 20200 \| 0.0 \| - \|
	\| 3.1366 \| 20250 \| 0.0 \| - \|
	\| 3.1444 \| 20300 \| 0.0 \| - \|
	\| 3.1521 \| 20350 \| 0.0 \| - \|
	\| 3.1599 \| 20400 \| 0.0 \| - \|
	\| 3.1676 \| 20450 \| 0.0001 \| - \|
	\| 3.1753 \| 20500 \| 0.0 \| - \|
	\| 3.1831 \| 20550 \| 0.0001 \| - \|
	\| 3.1908 \| 20600 \| 0.0 \| - \|
	\| 3.1986 \| 20650 \| 0.0 \| - \|
	\| 3.2063 \| 20700 \| 0.0 \| - \|
	\| 3.2141 \| 20750 \| 0.0 \| - \|
	\| 3.2218 \| 20800 \| 0.0 \| - \|
	\| 3.2296 \| 20850 \| 0.0003 \| - \|
	\| 3.2373 \| 20900 \| 0.0 \| - \|
	\| 3.2450 \| 20950 \| 0.0 \| - \|
	\| 3.2528 \| 21000 \| 0.0 \| - \|
	\| 3.2605 \| 21050 \| 0.0 \| - \|
	\| 3.2683 \| 21100 \| 0.0001 \| - \|
	\| 3.2760 \| 21150 \| 0.0001 \| - \|
	\| 3.2838 \| 21200 \| 0.0 \| - \|
	\| 3.2915 \| 21250 \| 0.0 \| - \|
	\| 3.2993 \| 21300 \| 0.0 \| - \|
	\| 3.3070 \| 21350 \| 0.0 \| - \|
	\| 3.3147 \| 21400 \| 0.0 \| - \|
	\| 3.3225 \| 21450 \| 0.0001 \| - \|
	\| 3.3302 \| 21500 \| 0.0 \| - \|
	\| 3.3380 \| 21550 \| 0.0 \| - \|
	\| 3.3457 \| 21600 \| 0.0 \| - \|
	\| 3.3535 \| 21650 \| 0.0 \| - \|
	\| 3.3612 \| 21700 \| 0.0 \| - \|
	\| 3.3690 \| 21750 \| 0.0 \| - \|
	\| 3.3767 \| 21800 \| 0.0 \| - \|
	\| 3.3844 \| 21850 \| 0.0 \| - \|
	\| 3.3922 \| 21900 \| 0.0001 \| - \|
	\| 3.3999 \| 21950 \| 0.0 \| - \|
	\| 3.4077 \| 22000 \| 0.0 \| - \|
	\| 3.4154 \| 22050 \| 0.0001 \| - \|
	\| 3.4232 \| 22100 \| 0.0 \| - \|
	\| 3.4309 \| 22150 \| 0.0001 \| - \|
	\| 3.4387 \| 22200 \| 0.0 \| - \|
	\| 3.4464 \| 22250 \| 0.0 \| - \|
	\| 3.4542 \| 22300 \| 0.0 \| - \|
	\| 3.4619 \| 22350 \| 0.0001 \| - \|
	\| 3.4696 \| 22400 \| 0.0 \| - \|
	\| 3.4774 \| 22450 \| 0.0 \| - \|
	\| 3.4851 \| 22500 \| 0.0 \| - \|
	\| 3.4929 \| 22550 \| 0.0001 \| - \|
	\| 3.5006 \| 22600 \| 0.0002 \| - \|
	\| 3.5084 \| 22650 \| 0.0001 \| - \|
	\| 3.5161 \| 22700 \| 0.0 \| - \|
	\| 3.5239 \| 22750 \| 0.0001 \| - \|
	\| 3.5316 \| 22800 \| 0.0 \| - \|
	\| 3.5393 \| 22850 \| 0.0 \| - \|
	\| 3.5471 \| 22900 \| 0.0001 \| - \|
	\| 3.5548 \| 22950 \| 0.0 \| - \|
	\| 3.5626 \| 23000 \| 0.0 \| - \|
	\| 3.5703 \| 23050 \| 0.0 \| - \|
	\| 3.5781 \| 23100 \| 0.0 \| - \|
	\| 3.5858 \| 23150 \| 0.0001 \| - \|
	\| 3.5936 \| 23200 \| 0.0 \| - \|
	\| 3.6013 \| 23250 \| 0.0001 \| - \|
	\| 3.6090 \| 23300 \| 0.0001 \| - \|
	\| 3.6168 \| 23350 \| 0.0 \| - \|
	\| 3.6245 \| 23400 \| 0.0003 \| - \|
	\| 3.6323 \| 23450 \| 0.0 \| - \|
	\| 3.6400 \| 23500 \| 0.0 \| - \|
	\| 3.6478 \| 23550 \| 0.0001 \| - \|
	\| 3.6555 \| 23600 \| 0.0 \| - \|
	\| 3.6633 \| 23650 \| 0.0 \| - \|
	\| 3.6710 \| 23700 \| 0.0 \| - \|
	\| 3.6787 \| 23750 \| 0.0001 \| - \|
	\| 3.6865 \| 23800 \| 0.0 \| - \|
	\| 3.6942 \| 23850 \| 0.0001 \| - \|
	\| 3.7020 \| 23900 \| 0.0002 \| - \|
	\| 3.7097 \| 23950 \| 0.0 \| - \|
	\| 3.7175 \| 24000 \| 0.0 \| - \|
	\| 3.7252 \| 24050 \| 0.0 \| - \|
	\| 3.7330 \| 24100 \| 0.0 \| - \|
	\| 3.7407 \| 24150 \| 0.0001 \| - \|
	\| 3.7485 \| 24200 \| 0.0 \| - \|
	\| 3.7562 \| 24250 \| 0.0 \| - \|
	\| 3.7639 \| 24300 \| 0.0 \| - \|
	\| 3.7717 \| 24350 \| 0.0 \| - \|
	\| 3.7794 \| 24400 \| 0.0 \| - \|
	\| 3.7872 \| 24450 \| 0.0 \| - \|
	\| 3.7949 \| 24500 \| 0.0001 \| - \|
	\| 3.8027 \| 24550 \| 0.0001 \| - \|
	\| 3.8104 \| 24600 \| 0.0 \| - \|
	\| 3.8182 \| 24650 \| 0.0 \| - \|
	\| 3.8259 \| 24700 \| 0.0 \| - \|
	\| 3.8336 \| 24750 \| 0.0 \| - \|
	\| 3.8414 \| 24800 \| 0.0001 \| - \|
	\| 3.8491 \| 24850 \| 0.0 \| - \|
	\| 3.8569 \| 24900 \| 0.0 \| - \|
	\| 3.8646 \| 24950 \| 0.0 \| - \|
	\| 3.8724 \| 25000 \| 0.0 \| - \|
	\| 3.8801 \| 25050 \| 0.0 \| - \|
	\| 3.8879 \| 25100 \| 0.0 \| - \|
	\| 3.8956 \| 25150 \| 0.0001 \| - \|
	\| 3.9033 \| 25200 \| 0.0 \| - \|
	\| 3.9111 \| 25250 \| 0.0002 \| - \|
	\| 3.9188 \| 25300 \| 0.0001 \| - \|
	\| 3.9266 \| 25350 \| 0.0 \| - \|
	\| 3.9343 \| 25400 \| 0.0 \| - \|
	\| 3.9421 \| 25450 \| 0.0 \| - \|
	\| 3.9498 \| 25500 \| 0.0001 \| - \|
	\| 3.9576 \| 25550 \| 0.0 \| - \|
	\| 3.9653 \| 25600 \| 0.0 \| - \|
	\| 3.9730 \| 25650 \| 0.0001 \| - \|
	\| 3.9808 \| 25700 \| 0.0 \| - \|
	\| 3.9885 \| 25750 \| 0.0 \| - \|
	\| 3.9963 \| 25800 \| 0.0 \| - \|
	\| 4.0 \| 25824 \| - \| 2.3576 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.39.0
	- PyTorch: 2.3.0+cu121
	- Datasets: 2.20.0
	- Tokenizers: 0.15.2

	## 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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