Push model using huggingface_hub.

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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Extend social protection to informal workers along agrifood value chains through
	portable cash transfers, micro-insurance products, and inclusive retirement and
	disability benefits.
	- text: strategy for sustainable agriculture and rural development in the 2021-2030
	period has been approved together with with the list of the 41 national programmes
	and action plans. the common goal of the strategy is of building a commodity-producing
	agriculture and developing agriculture based on local advantages, in the direction
	of modernity, with high productivity, quality, efficiency, sustainability and
	competitiveness, firmly ensuring national food security, making an important contribution
	to socio-economic stability, preventing and combating natural disasters and epidemics,
	protecting the environment, responding to climate change, effectively implementing
	international commitments on reducing greenhouse gas emissions; the strategy also
	aims to improve income, quality of life, role and position of people involved
	in agricultural production; create non-agricultural jobs to develop diversified
	livelihoods, reduce poverty sustainably for rural people, ensure equal development
	opportunities among regions; comprehensive and modern rural development associated
	with the process of urbanization, with synchronous infrastructure and social services
	and close to urban areas; the strategy looks to preserve and promote the national
	cultural identity; building green, clean, beautiful countryside, security and
	order are ensured; to develop agriculture and rural economy in association with
	new rural construction in the direction of highly efficient ecological agriculture,
	modern rural areas and civilized farmers. specific objectives are, by 2030, the
	gdp growth rate of agro-forestry-fishery will reach an average of 2.5 - 3 percent
	per year, the productivity growth rate of agricultural, forestry and fishery workers
	will reach an average of 5.5 - 6 percent per year. the growth rate of the export
	value of agro-forestry-fishery products will reach an average of 5-6 percent per
	year. fisheries sector is selected as the strategic production sector.
	- text: 'the uganda npdp works with the two overall critical variables, the population
	and urbanization projections and the national land use balance sheet. it provides
	a basis for integrating the physical and spatial with the economic and social
	issues of national development planning. its core elements are the pattern of
	human settlements, the land uses and natural resources for economic activity and
	the infrastructure networks which connect and service them. the strategic orientations
	are: strategic orientation 1: maximizing national economic growth strategic orientation
	2: favoring social and regional equality. strategic orientation 3: maximizing
	supply of agricultural lands. strategic orientation 4: maximizing environmental
	sustainability strategic orientation 5: maximizing urbanization and urbanity strategic
	orientation 6: maximizing national and international connectivity.'
	- text: Mechanization investment will be integrated with soil health and water management
	programs, ensuring appropriate machinery selection to avoid soil compaction and
	water wastage.
	- text: The policy will publish annual import-dependency indices by commodity and
	translate these insights into prioritized investment in domestic production, feed
	safety, and regional trade integration to reduce vulnerability and improve food
	security.
	metrics:
	- accuracy
	pipeline_tag: text-classification
	library_name: setfit
	inference: false
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	---

	# SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
	- Classification head: a OneVsRestClassifier instance
	- Maximum Sequence Length: 128 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)

	## 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("faodl/model_cca_multilabel_MiniLM-L12-70prop-data-augmented-v02")
	# Run inference
	preds = model("Mechanization investment will be integrated with soil health and water management programs, ensuring appropriate machinery selection to avoid soil compaction and water wastage.")
	```

	<!--
	### 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 \| 55.4334 \| 951 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (2, 2)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 20
	- body_learning_rate: (2e-05, 2e-05)
	- head_learning_rate: 2e-05
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- l2_weight: 0.01
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0001 \| 1 \| 0.2114 \| - \|
	\| 0.0045 \| 50 \| 0.2069 \| - \|
	\| 0.0091 \| 100 \| 0.2029 \| - \|
	\| 0.0136 \| 150 \| 0.2025 \| - \|
	\| 0.0181 \| 200 \| 0.1984 \| - \|
	\| 0.0226 \| 250 \| 0.1848 \| - \|
	\| 0.0272 \| 300 \| 0.1784 \| - \|
	\| 0.0317 \| 350 \| 0.176 \| - \|
	\| 0.0362 \| 400 \| 0.1743 \| - \|
	\| 0.0408 \| 450 \| 0.1579 \| - \|
	\| 0.0453 \| 500 \| 0.149 \| - \|
	\| 0.0498 \| 550 \| 0.1532 \| - \|
	\| 0.0543 \| 600 \| 0.1551 \| - \|
	\| 0.0589 \| 650 \| 0.1483 \| - \|
	\| 0.0634 \| 700 \| 0.1474 \| - \|
	\| 0.0679 \| 750 \| 0.1444 \| - \|
	\| 0.0725 \| 800 \| 0.1363 \| - \|
	\| 0.0770 \| 850 \| 0.1269 \| - \|
	\| 0.0815 \| 900 \| 0.1541 \| - \|
	\| 0.0861 \| 950 \| 0.1256 \| - \|
	\| 0.0906 \| 1000 \| 0.1457 \| - \|
	\| 0.0951 \| 1050 \| 0.131 \| - \|
	\| 0.0996 \| 1100 \| 0.1224 \| - \|
	\| 0.1042 \| 1150 \| 0.1357 \| - \|
	\| 0.1087 \| 1200 \| 0.1341 \| - \|
	\| 0.1132 \| 1250 \| 0.1371 \| - \|
	\| 0.1178 \| 1300 \| 0.1305 \| - \|
	\| 0.1223 \| 1350 \| 0.1165 \| - \|
	\| 0.1268 \| 1400 \| 0.1191 \| - \|
	\| 0.1313 \| 1450 \| 0.1247 \| - \|
	\| 0.1359 \| 1500 \| 0.1209 \| - \|
	\| 0.1404 \| 1550 \| 0.129 \| - \|
	\| 0.1449 \| 1600 \| 0.1161 \| - \|
	\| 0.1495 \| 1650 \| 0.1215 \| - \|
	\| 0.1540 \| 1700 \| 0.1213 \| - \|
	\| 0.1585 \| 1750 \| 0.1193 \| - \|
	\| 0.1630 \| 1800 \| 0.1126 \| - \|
	\| 0.1676 \| 1850 \| 0.1253 \| - \|
	\| 0.1721 \| 1900 \| 0.1135 \| - \|
	\| 0.1766 \| 1950 \| 0.1032 \| - \|
	\| 0.1812 \| 2000 \| 0.0998 \| - \|
	\| 0.1857 \| 2050 \| 0.116 \| - \|
	\| 0.1902 \| 2100 \| 0.1088 \| - \|
	\| 0.1947 \| 2150 \| 0.104 \| - \|
	\| 0.1993 \| 2200 \| 0.1139 \| - \|
	\| 0.2038 \| 2250 \| 0.1084 \| - \|
	\| 0.2083 \| 2300 \| 0.1043 \| - \|
	\| 0.2129 \| 2350 \| 0.1149 \| - \|
	\| 0.2174 \| 2400 \| 0.1022 \| - \|
	\| 0.2219 \| 2450 \| 0.1106 \| - \|
	\| 0.2264 \| 2500 \| 0.1028 \| - \|
	\| 0.2310 \| 2550 \| 0.0986 \| - \|
	\| 0.2355 \| 2600 \| 0.0965 \| - \|
	\| 0.2400 \| 2650 \| 0.1047 \| - \|
	\| 0.2446 \| 2700 \| 0.1007 \| - \|
	\| 0.2491 \| 2750 \| 0.0979 \| - \|
	\| 0.2536 \| 2800 \| 0.0967 \| - \|
	\| 0.2582 \| 2850 \| 0.0999 \| - \|
	\| 0.2627 \| 2900 \| 0.1025 \| - \|
	\| 0.2672 \| 2950 \| 0.0938 \| - \|
	\| 0.2717 \| 3000 \| 0.0923 \| - \|
	\| 0.2763 \| 3050 \| 0.0885 \| - \|
	\| 0.2808 \| 3100 \| 0.0953 \| - \|
	\| 0.2853 \| 3150 \| 0.0931 \| - \|
	\| 0.2899 \| 3200 \| 0.095 \| - \|
	\| 0.2944 \| 3250 \| 0.0945 \| - \|
	\| 0.2989 \| 3300 \| 0.0919 \| - \|
	\| 0.3034 \| 3350 \| 0.0975 \| - \|
	\| 0.3080 \| 3400 \| 0.0906 \| - \|
	\| 0.3125 \| 3450 \| 0.0977 \| - \|
	\| 0.3170 \| 3500 \| 0.0952 \| - \|
	\| 0.3216 \| 3550 \| 0.0851 \| - \|
	\| 0.3261 \| 3600 \| 0.0883 \| - \|
	\| 0.3306 \| 3650 \| 0.0852 \| - \|
	\| 0.3351 \| 3700 \| 0.082 \| - \|
	\| 0.3397 \| 3750 \| 0.0901 \| - \|
	\| 0.3442 \| 3800 \| 0.0778 \| - \|
	\| 0.3487 \| 3850 \| 0.0819 \| - \|
	\| 0.3533 \| 3900 \| 0.0804 \| - \|
	\| 0.3578 \| 3950 \| 0.083 \| - \|
	\| 0.3623 \| 4000 \| 0.0855 \| - \|
	\| 0.3668 \| 4050 \| 0.0828 \| - \|
	\| 0.3714 \| 4100 \| 0.0899 \| - \|
	\| 0.3759 \| 4150 \| 0.0875 \| - \|
	\| 0.3804 \| 4200 \| 0.0816 \| - \|
	\| 0.3850 \| 4250 \| 0.09 \| - \|
	\| 0.3895 \| 4300 \| 0.0782 \| - \|
	\| 0.3940 \| 4350 \| 0.0831 \| - \|
	\| 0.3986 \| 4400 \| 0.0795 \| - \|
	\| 0.4031 \| 4450 \| 0.0807 \| - \|
	\| 0.4076 \| 4500 \| 0.0809 \| - \|
	\| 0.4121 \| 4550 \| 0.0763 \| - \|
	\| 0.4167 \| 4600 \| 0.08 \| - \|
	\| 0.4212 \| 4650 \| 0.0731 \| - \|
	\| 0.4257 \| 4700 \| 0.0759 \| - \|
	\| 0.4303 \| 4750 \| 0.0758 \| - \|
	\| 0.4348 \| 4800 \| 0.0791 \| - \|
	\| 0.4393 \| 4850 \| 0.0731 \| - \|
	\| 0.4438 \| 4900 \| 0.0774 \| - \|
	\| 0.4484 \| 4950 \| 0.0781 \| - \|
	\| 0.4529 \| 5000 \| 0.0783 \| - \|
	\| 0.4574 \| 5050 \| 0.0852 \| - \|
	\| 0.4620 \| 5100 \| 0.0771 \| - \|
	\| 0.4665 \| 5150 \| 0.0813 \| - \|
	\| 0.4710 \| 5200 \| 0.0795 \| - \|
	\| 0.4755 \| 5250 \| 0.0725 \| - \|
	\| 0.4801 \| 5300 \| 0.0751 \| - \|
	\| 0.4846 \| 5350 \| 0.0756 \| - \|
	\| 0.4891 \| 5400 \| 0.0715 \| - \|
	\| 0.4937 \| 5450 \| 0.0643 \| - \|
	\| 0.4982 \| 5500 \| 0.0675 \| - \|
	\| 0.5027 \| 5550 \| 0.0769 \| - \|
	\| 0.5072 \| 5600 \| 0.0761 \| - \|
	\| 0.5118 \| 5650 \| 0.0739 \| - \|
	\| 0.5163 \| 5700 \| 0.0716 \| - \|
	\| 0.5208 \| 5750 \| 0.0706 \| - \|
	\| 0.5254 \| 5800 \| 0.0719 \| - \|
	\| 0.5299 \| 5850 \| 0.0721 \| - \|
	\| 0.5344 \| 5900 \| 0.068 \| - \|
	\| 0.5389 \| 5950 \| 0.0626 \| - \|
	\| 0.5435 \| 6000 \| 0.0679 \| - \|
	\| 0.5480 \| 6050 \| 0.0713 \| - \|
	\| 0.5525 \| 6100 \| 0.0692 \| - \|
	\| 0.5571 \| 6150 \| 0.0728 \| - \|
	\| 0.5616 \| 6200 \| 0.0622 \| - \|
	\| 0.5661 \| 6250 \| 0.0686 \| - \|
	\| 0.5707 \| 6300 \| 0.073 \| - \|
	\| 0.5752 \| 6350 \| 0.0563 \| - \|
	\| 0.5797 \| 6400 \| 0.0621 \| - \|
	\| 0.5842 \| 6450 \| 0.0699 \| - \|
	\| 0.5888 \| 6500 \| 0.0691 \| - \|
	\| 0.5933 \| 6550 \| 0.0676 \| - \|
	\| 0.5978 \| 6600 \| 0.0621 \| - \|
	\| 0.6024 \| 6650 \| 0.0693 \| - \|
	\| 0.6069 \| 6700 \| 0.0708 \| - \|
	\| 0.6114 \| 6750 \| 0.0672 \| - \|
	\| 0.6159 \| 6800 \| 0.0728 \| - \|
	\| 0.6205 \| 6850 \| 0.0629 \| - \|
	\| 0.625 \| 6900 \| 0.0694 \| - \|
	\| 0.6295 \| 6950 \| 0.063 \| - \|
	\| 0.6341 \| 7000 \| 0.0591 \| - \|
	\| 0.6386 \| 7050 \| 0.0663 \| - \|
	\| 0.6431 \| 7100 \| 0.0722 \| - \|
	\| 0.6476 \| 7150 \| 0.0576 \| - \|
	\| 0.6522 \| 7200 \| 0.0604 \| - \|
	\| 0.6567 \| 7250 \| 0.0632 \| - \|
	\| 0.6612 \| 7300 \| 0.0709 \| - \|
	\| 0.6658 \| 7350 \| 0.0649 \| - \|
	\| 0.6703 \| 7400 \| 0.0611 \| - \|
	\| 0.6748 \| 7450 \| 0.0597 \| - \|
	\| 0.6793 \| 7500 \| 0.0712 \| - \|
	\| 0.6839 \| 7550 \| 0.0668 \| - \|
	\| 0.6884 \| 7600 \| 0.0664 \| - \|
	\| 0.6929 \| 7650 \| 0.0664 \| - \|
	\| 0.6975 \| 7700 \| 0.0622 \| - \|
	\| 0.7020 \| 7750 \| 0.0601 \| - \|
	\| 0.7065 \| 7800 \| 0.0582 \| - \|
	\| 0.7111 \| 7850 \| 0.0622 \| - \|
	\| 0.7156 \| 7900 \| 0.0648 \| - \|
	\| 0.7201 \| 7950 \| 0.064 \| - \|
	\| 0.7246 \| 8000 \| 0.0624 \| - \|
	\| 0.7292 \| 8050 \| 0.0622 \| - \|
	\| 0.7337 \| 8100 \| 0.0596 \| - \|
	\| 0.7382 \| 8150 \| 0.0633 \| - \|
	\| 0.7428 \| 8200 \| 0.0532 \| - \|
	\| 0.7473 \| 8250 \| 0.0565 \| - \|
	\| 0.7518 \| 8300 \| 0.0724 \| - \|
	\| 0.7563 \| 8350 \| 0.0559 \| - \|
	\| 0.7609 \| 8400 \| 0.064 \| - \|
	\| 0.7654 \| 8450 \| 0.0603 \| - \|
	\| 0.7699 \| 8500 \| 0.059 \| - \|
	\| 0.7745 \| 8550 \| 0.0543 \| - \|
	\| 0.7790 \| 8600 \| 0.0568 \| - \|
	\| 0.7835 \| 8650 \| 0.0638 \| - \|
	\| 0.7880 \| 8700 \| 0.0578 \| - \|
	\| 0.7926 \| 8750 \| 0.0692 \| - \|
	\| 0.7971 \| 8800 \| 0.0608 \| - \|
	\| 0.8016 \| 8850 \| 0.0652 \| - \|
	\| 0.8062 \| 8900 \| 0.061 \| - \|
	\| 0.8107 \| 8950 \| 0.0581 \| - \|
	\| 0.8152 \| 9000 \| 0.0627 \| - \|
	\| 0.8197 \| 9050 \| 0.0656 \| - \|
	\| 0.8243 \| 9100 \| 0.0579 \| - \|
	\| 0.8288 \| 9150 \| 0.0626 \| - \|
	\| 0.8333 \| 9200 \| 0.0587 \| - \|
	\| 0.8379 \| 9250 \| 0.0625 \| - \|
	\| 0.8424 \| 9300 \| 0.051 \| - \|
	\| 0.8469 \| 9350 \| 0.0553 \| - \|
	\| 0.8514 \| 9400 \| 0.0507 \| - \|
	\| 0.8560 \| 9450 \| 0.0521 \| - \|
	\| 0.8605 \| 9500 \| 0.0548 \| - \|
	\| 0.8650 \| 9550 \| 0.0536 \| - \|
	\| 0.8696 \| 9600 \| 0.0517 \| - \|
	\| 0.8741 \| 9650 \| 0.0569 \| - \|
	\| 0.8786 \| 9700 \| 0.0572 \| - \|
	\| 0.8832 \| 9750 \| 0.0553 \| - \|
	\| 0.8877 \| 9800 \| 0.0567 \| - \|
	\| 0.8922 \| 9850 \| 0.0594 \| - \|
	\| 0.8967 \| 9900 \| 0.0598 \| - \|
	\| 0.9013 \| 9950 \| 0.0548 \| - \|
	\| 0.9058 \| 10000 \| 0.0563 \| - \|
	\| 0.9103 \| 10050 \| 0.0466 \| - \|
	\| 0.9149 \| 10100 \| 0.0561 \| - \|
	\| 0.9194 \| 10150 \| 0.0533 \| - \|
	\| 0.9239 \| 10200 \| 0.0569 \| - \|
	\| 0.9284 \| 10250 \| 0.0484 \| - \|
	\| 0.9330 \| 10300 \| 0.0563 \| - \|
	\| 0.9375 \| 10350 \| 0.0597 \| - \|
	\| 0.9420 \| 10400 \| 0.0556 \| - \|
	\| 0.9466 \| 10450 \| 0.0542 \| - \|
	\| 0.9511 \| 10500 \| 0.0528 \| - \|
	\| 0.9556 \| 10550 \| 0.0652 \| - \|
	\| 0.9601 \| 10600 \| 0.0541 \| - \|
	\| 0.9647 \| 10650 \| 0.0581 \| - \|
	\| 0.9692 \| 10700 \| 0.0529 \| - \|
	\| 0.9737 \| 10750 \| 0.0497 \| - \|
	\| 0.9783 \| 10800 \| 0.0591 \| - \|
	\| 0.9828 \| 10850 \| 0.055 \| - \|
	\| 0.9873 \| 10900 \| 0.0464 \| - \|
	\| 0.9918 \| 10950 \| 0.0529 \| - \|
	\| 0.9964 \| 11000 \| 0.0562 \| - \|
	\| 1.0009 \| 11050 \| 0.0508 \| - \|
	\| 1.0054 \| 11100 \| 0.0447 \| - \|
	\| 1.0100 \| 11150 \| 0.0493 \| - \|
	\| 1.0145 \| 11200 \| 0.0526 \| - \|
	\| 1.0190 \| 11250 \| 0.0476 \| - \|
	\| 1.0236 \| 11300 \| 0.0469 \| - \|
	\| 1.0281 \| 11350 \| 0.0465 \| - \|
	\| 1.0326 \| 11400 \| 0.0484 \| - \|
	\| 1.0371 \| 11450 \| 0.0492 \| - \|
	\| 1.0417 \| 11500 \| 0.0518 \| - \|
	\| 1.0462 \| 11550 \| 0.0554 \| - \|
	\| 1.0507 \| 11600 \| 0.0533 \| - \|
	\| 1.0553 \| 11650 \| 0.0558 \| - \|
	\| 1.0598 \| 11700 \| 0.0517 \| - \|
	\| 1.0643 \| 11750 \| 0.0536 \| - \|
	\| 1.0688 \| 11800 \| 0.051 \| - \|
	\| 1.0734 \| 11850 \| 0.0512 \| - \|
	\| 1.0779 \| 11900 \| 0.0531 \| - \|
	\| 1.0824 \| 11950 \| 0.0496 \| - \|
	\| 1.0870 \| 12000 \| 0.0471 \| - \|
	\| 1.0915 \| 12050 \| 0.0492 \| - \|
	\| 1.0960 \| 12100 \| 0.0444 \| - \|
	\| 1.1005 \| 12150 \| 0.0441 \| - \|
	\| 1.1051 \| 12200 \| 0.053 \| - \|
	\| 1.1096 \| 12250 \| 0.048 \| - \|
	\| 1.1141 \| 12300 \| 0.044 \| - \|
	\| 1.1187 \| 12350 \| 0.0482 \| - \|
	\| 1.1232 \| 12400 \| 0.0486 \| - \|
	\| 1.1277 \| 12450 \| 0.0475 \| - \|
	\| 1.1322 \| 12500 \| 0.0484 \| - \|
	\| 1.1368 \| 12550 \| 0.0512 \| - \|
	\| 1.1413 \| 12600 \| 0.0534 \| - \|
	\| 1.1458 \| 12650 \| 0.0532 \| - \|
	\| 1.1504 \| 12700 \| 0.0422 \| - \|
	\| 1.1549 \| 12750 \| 0.0487 \| - \|
	\| 1.1594 \| 12800 \| 0.0489 \| - \|
	\| 1.1639 \| 12850 \| 0.0558 \| - \|
	\| 1.1685 \| 12900 \| 0.0506 \| - \|
	\| 1.1730 \| 12950 \| 0.0497 \| - \|
	\| 1.1775 \| 13000 \| 0.0593 \| - \|
	\| 1.1821 \| 13050 \| 0.0476 \| - \|
	\| 1.1866 \| 13100 \| 0.0435 \| - \|
	\| 1.1911 \| 13150 \| 0.0553 \| - \|
	\| 1.1957 \| 13200 \| 0.0527 \| - \|
	\| 1.2002 \| 13250 \| 0.0535 \| - \|
	\| 1.2047 \| 13300 \| 0.0469 \| - \|
	\| 1.2092 \| 13350 \| 0.054 \| - \|
	\| 1.2138 \| 13400 \| 0.0476 \| - \|
	\| 1.2183 \| 13450 \| 0.0507 \| - \|
	\| 1.2228 \| 13500 \| 0.0497 \| - \|
	\| 1.2274 \| 13550 \| 0.0519 \| - \|
	\| 1.2319 \| 13600 \| 0.0444 \| - \|
	\| 1.2364 \| 13650 \| 0.0516 \| - \|
	\| 1.2409 \| 13700 \| 0.0494 \| - \|
	\| 1.2455 \| 13750 \| 0.0425 \| - \|
	\| 1.25 \| 13800 \| 0.0487 \| - \|
	\| 1.2545 \| 13850 \| 0.0509 \| - \|
	\| 1.2591 \| 13900 \| 0.0523 \| - \|
	\| 1.2636 \| 13950 \| 0.0446 \| - \|
	\| 1.2681 \| 14000 \| 0.0478 \| - \|
	\| 1.2726 \| 14050 \| 0.0439 \| - \|
	\| 1.2772 \| 14100 \| 0.0518 \| - \|
	\| 1.2817 \| 14150 \| 0.0505 \| - \|
	\| 1.2862 \| 14200 \| 0.0497 \| - \|
	\| 1.2908 \| 14250 \| 0.0554 \| - \|
	\| 1.2953 \| 14300 \| 0.0482 \| - \|
	\| 1.2998 \| 14350 \| 0.0427 \| - \|
	\| 1.3043 \| 14400 \| 0.05 \| - \|
	\| 1.3089 \| 14450 \| 0.0477 \| - \|
	\| 1.3134 \| 14500 \| 0.0463 \| - \|
	\| 1.3179 \| 14550 \| 0.0448 \| - \|
	\| 1.3225 \| 14600 \| 0.045 \| - \|
	\| 1.3270 \| 14650 \| 0.0437 \| - \|
	\| 1.3315 \| 14700 \| 0.0467 \| - \|
	\| 1.3361 \| 14750 \| 0.0435 \| - \|
	\| 1.3406 \| 14800 \| 0.043 \| - \|
	\| 1.3451 \| 14850 \| 0.0486 \| - \|
	\| 1.3496 \| 14900 \| 0.049 \| - \|
	\| 1.3542 \| 14950 \| 0.0471 \| - \|
	\| 1.3587 \| 15000 \| 0.0455 \| - \|
	\| 1.3632 \| 15050 \| 0.0428 \| - \|
	\| 1.3678 \| 15100 \| 0.0425 \| - \|
	\| 1.3723 \| 15150 \| 0.0465 \| - \|
	\| 1.3768 \| 15200 \| 0.0452 \| - \|
	\| 1.3813 \| 15250 \| 0.0487 \| - \|
	\| 1.3859 \| 15300 \| 0.045 \| - \|
	\| 1.3904 \| 15350 \| 0.0431 \| - \|
	\| 1.3949 \| 15400 \| 0.0512 \| - \|
	\| 1.3995 \| 15450 \| 0.0411 \| - \|
	\| 1.4040 \| 15500 \| 0.0495 \| - \|
	\| 1.4085 \| 15550 \| 0.0406 \| - \|
	\| 1.4130 \| 15600 \| 0.0445 \| - \|
	\| 1.4176 \| 15650 \| 0.0496 \| - \|
	\| 1.4221 \| 15700 \| 0.0497 \| - \|
	\| 1.4266 \| 15750 \| 0.0466 \| - \|
	\| 1.4312 \| 15800 \| 0.0479 \| - \|
	\| 1.4357 \| 15850 \| 0.0448 \| - \|
	\| 1.4402 \| 15900 \| 0.0453 \| - \|
	\| 1.4447 \| 15950 \| 0.0486 \| - \|
	\| 1.4493 \| 16000 \| 0.0519 \| - \|
	\| 1.4538 \| 16050 \| 0.06 \| - \|
	\| 1.4583 \| 16100 \| 0.045 \| - \|
	\| 1.4629 \| 16150 \| 0.0421 \| - \|
	\| 1.4674 \| 16200 \| 0.0501 \| - \|
	\| 1.4719 \| 16250 \| 0.0525 \| - \|
	\| 1.4764 \| 16300 \| 0.0444 \| - \|
	\| 1.4810 \| 16350 \| 0.0422 \| - \|
	\| 1.4855 \| 16400 \| 0.0428 \| - \|
	\| 1.4900 \| 16450 \| 0.0398 \| - \|
	\| 1.4946 \| 16500 \| 0.0412 \| - \|
	\| 1.4991 \| 16550 \| 0.0482 \| - \|
	\| 1.5036 \| 16600 \| 0.046 \| - \|
	\| 1.5082 \| 16650 \| 0.0502 \| - \|
	\| 1.5127 \| 16700 \| 0.048 \| - \|
	\| 1.5172 \| 16750 \| 0.0447 \| - \|
	\| 1.5217 \| 16800 \| 0.0419 \| - \|
	\| 1.5263 \| 16850 \| 0.0429 \| - \|
	\| 1.5308 \| 16900 \| 0.0533 \| - \|
	\| 1.5353 \| 16950 \| 0.0482 \| - \|
	\| 1.5399 \| 17000 \| 0.0519 \| - \|
	\| 1.5444 \| 17050 \| 0.0503 \| - \|
	\| 1.5489 \| 17100 \| 0.0432 \| - \|
	\| 1.5534 \| 17150 \| 0.0388 \| - \|
	\| 1.5580 \| 17200 \| 0.0537 \| - \|
	\| 1.5625 \| 17250 \| 0.0477 \| - \|
	\| 1.5670 \| 17300 \| 0.0444 \| - \|
	\| 1.5716 \| 17350 \| 0.0407 \| - \|
	\| 1.5761 \| 17400 \| 0.0463 \| - \|
	\| 1.5806 \| 17450 \| 0.0417 \| - \|
	\| 1.5851 \| 17500 \| 0.0403 \| - \|
	\| 1.5897 \| 17550 \| 0.0481 \| - \|
	\| 1.5942 \| 17600 \| 0.0485 \| - \|
	\| 1.5987 \| 17650 \| 0.0462 \| - \|
	\| 1.6033 \| 17700 \| 0.0383 \| - \|
	\| 1.6078 \| 17750 \| 0.0429 \| - \|
	\| 1.6123 \| 17800 \| 0.0413 \| - \|
	\| 1.6168 \| 17850 \| 0.0421 \| - \|
	\| 1.6214 \| 17900 \| 0.0409 \| - \|
	\| 1.6259 \| 17950 \| 0.0436 \| - \|
	\| 1.6304 \| 18000 \| 0.0468 \| - \|
	\| 1.6350 \| 18050 \| 0.0446 \| - \|
	\| 1.6395 \| 18100 \| 0.0389 \| - \|
	\| 1.6440 \| 18150 \| 0.0443 \| - \|
	\| 1.6486 \| 18200 \| 0.0489 \| - \|
	\| 1.6531 \| 18250 \| 0.0489 \| - \|
	\| 1.6576 \| 18300 \| 0.0498 \| - \|
	\| 1.6621 \| 18350 \| 0.044 \| - \|
	\| 1.6667 \| 18400 \| 0.0392 \| - \|
	\| 1.6712 \| 18450 \| 0.0441 \| - \|
	\| 1.6757 \| 18500 \| 0.0429 \| - \|
	\| 1.6803 \| 18550 \| 0.0369 \| - \|
	\| 1.6848 \| 18600 \| 0.0409 \| - \|
	\| 1.6893 \| 18650 \| 0.0496 \| - \|
	\| 1.6938 \| 18700 \| 0.052 \| - \|
	\| 1.6984 \| 18750 \| 0.0377 \| - \|
	\| 1.7029 \| 18800 \| 0.0403 \| - \|
	\| 1.7074 \| 18850 \| 0.0473 \| - \|
	\| 1.7120 \| 18900 \| 0.0474 \| - \|
	\| 1.7165 \| 18950 \| 0.0447 \| - \|
	\| 1.7210 \| 19000 \| 0.0498 \| - \|
	\| 1.7255 \| 19050 \| 0.0427 \| - \|
	\| 1.7301 \| 19100 \| 0.0454 \| - \|
	\| 1.7346 \| 19150 \| 0.0478 \| - \|
	\| 1.7391 \| 19200 \| 0.0438 \| - \|
	\| 1.7437 \| 19250 \| 0.0378 \| - \|
	\| 1.7482 \| 19300 \| 0.0442 \| - \|
	\| 1.7527 \| 19350 \| 0.0453 \| - \|
	\| 1.7572 \| 19400 \| 0.0407 \| - \|
	\| 1.7618 \| 19450 \| 0.0487 \| - \|
	\| 1.7663 \| 19500 \| 0.0408 \| - \|
	\| 1.7708 \| 19550 \| 0.0444 \| - \|
	\| 1.7754 \| 19600 \| 0.0426 \| - \|
	\| 1.7799 \| 19650 \| 0.044 \| - \|
	\| 1.7844 \| 19700 \| 0.04 \| - \|
	\| 1.7889 \| 19750 \| 0.0484 \| - \|
	\| 1.7935 \| 19800 \| 0.0478 \| - \|
	\| 1.7980 \| 19850 \| 0.0495 \| - \|
	\| 1.8025 \| 19900 \| 0.037 \| - \|
	\| 1.8071 \| 19950 \| 0.0472 \| - \|
	\| 1.8116 \| 20000 \| 0.0411 \| - \|
	\| 1.8161 \| 20050 \| 0.0368 \| - \|
	\| 1.8207 \| 20100 \| 0.042 \| - \|
	\| 1.8252 \| 20150 \| 0.0359 \| - \|
	\| 1.8297 \| 20200 \| 0.0452 \| - \|
	\| 1.8342 \| 20250 \| 0.0504 \| - \|
	\| 1.8388 \| 20300 \| 0.0513 \| - \|
	\| 1.8433 \| 20350 \| 0.048 \| - \|
	\| 1.8478 \| 20400 \| 0.0497 \| - \|
	\| 1.8524 \| 20450 \| 0.0408 \| - \|
	\| 1.8569 \| 20500 \| 0.0448 \| - \|
	\| 1.8614 \| 20550 \| 0.0411 \| - \|
	\| 1.8659 \| 20600 \| 0.0401 \| - \|
	\| 1.8705 \| 20650 \| 0.0445 \| - \|
	\| 1.875 \| 20700 \| 0.0468 \| - \|
	\| 1.8795 \| 20750 \| 0.0488 \| - \|
	\| 1.8841 \| 20800 \| 0.0401 \| - \|
	\| 1.8886 \| 20850 \| 0.0408 \| - \|
	\| 1.8931 \| 20900 \| 0.0384 \| - \|
	\| 1.8976 \| 20950 \| 0.0496 \| - \|
	\| 1.9022 \| 21000 \| 0.0436 \| - \|
	\| 1.9067 \| 21050 \| 0.0434 \| - \|
	\| 1.9112 \| 21100 \| 0.041 \| - \|
	\| 1.9158 \| 21150 \| 0.0452 \| - \|
	\| 1.9203 \| 21200 \| 0.0454 \| - \|
	\| 1.9248 \| 21250 \| 0.0447 \| - \|
	\| 1.9293 \| 21300 \| 0.0476 \| - \|
	\| 1.9339 \| 21350 \| 0.0399 \| - \|
	\| 1.9384 \| 21400 \| 0.0397 \| - \|
	\| 1.9429 \| 21450 \| 0.0399 \| - \|
	\| 1.9475 \| 21500 \| 0.0462 \| - \|
	\| 1.9520 \| 21550 \| 0.0452 \| - \|
	\| 1.9565 \| 21600 \| 0.046 \| - \|
	\| 1.9611 \| 21650 \| 0.0368 \| - \|
	\| 1.9656 \| 21700 \| 0.043 \| - \|
	\| 1.9701 \| 21750 \| 0.0413 \| - \|
	\| 1.9746 \| 21800 \| 0.0459 \| - \|
	\| 1.9792 \| 21850 \| 0.0442 \| - \|
	\| 1.9837 \| 21900 \| 0.0381 \| - \|
	\| 1.9882 \| 21950 \| 0.0411 \| - \|
	\| 1.9928 \| 22000 \| 0.0519 \| - \|
	\| 1.9973 \| 22050 \| 0.0445 \| - \|

	### Framework Versions
	- Python: 3.12.12
	- SetFit: 1.1.3
	- Sentence Transformers: 5.1.2
	- Transformers: 4.57.1
	- PyTorch: 2.8.0+cu126
	- Datasets: 4.0.0
	- Tokenizers: 0.22.1

	## 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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