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	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Encourage interoperability of farm-management systems with national tax and
	regulatory reporting to reduce administrative burden.
	- text: Support critical infrastructure investments for rural bioenergy supply chains,
	including feedstock storage, processing facilities, and logistics, to reduce post-harvest
	losses and strengthen resilience.
	- text: Policy coherence will be strengthened by aligning agricultural, forestry,
	and fisheries policies with international instruments on biodiversity and sustainable
	use of ecosystems, ensuring that area restoration and sustainable fishing goals
	are mutually reinforcing. The approach will be backed by sectoral budgets and
	performance-based support to encourage early adoption.
	- text: Financing windows will be created to de-risk early-stage bioenergy ventures,
	including blended finance and concessional lending.
	- text: Foster regional integration to broaden market access, reduce dependence on
	a narrow product mix, and enhance resilience of the agrifood trade profile in
	the face of global price volatility.
	metrics:
	- accuracy
	pipeline_tag: text-classification
	library_name: setfit
	inference: false
	base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
	---

	# SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
	- Classification head: a OneVsRestClassifier instance
	- Maximum Sequence Length: 128 tokens
	- Number of Classes: 96 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)

	## 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_mpnet-65max-data-augmented-v03")
	# Run inference
	preds = model("Financing windows will be created to de-risk early-stage bioenergy ventures, including blended finance and concessional lending.")
	```

	<!--
	### 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 \| 47.2721 \| 947 \|

	### Training Hyperparameters
	- batch_size: (8, 8)
	- num_epochs: (2, 2)
	- max_steps: -1
	- sampling_strategy: oversampling
	- num_iterations: 10
	- 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 \| 0.3187 \| - \|
	\| 0.0032 \| 50 \| 0.2107 \| - \|
	\| 0.0065 \| 100 \| 0.2079 \| - \|
	\| 0.0097 \| 150 \| 0.2015 \| - \|
	\| 0.0130 \| 200 \| 0.2011 \| - \|
	\| 0.0162 \| 250 \| 0.1917 \| - \|
	\| 0.0194 \| 300 \| 0.187 \| - \|
	\| 0.0227 \| 350 \| 0.1892 \| - \|
	\| 0.0259 \| 400 \| 0.1726 \| - \|
	\| 0.0291 \| 450 \| 0.1776 \| - \|
	\| 0.0324 \| 500 \| 0.1685 \| - \|
	\| 0.0356 \| 550 \| 0.176 \| - \|
	\| 0.0389 \| 600 \| 0.1646 \| - \|
	\| 0.0421 \| 650 \| 0.1689 \| - \|
	\| 0.0453 \| 700 \| 0.1577 \| - \|
	\| 0.0486 \| 750 \| 0.1466 \| - \|
	\| 0.0518 \| 800 \| 0.1534 \| - \|
	\| 0.0551 \| 850 \| 0.1606 \| - \|
	\| 0.0583 \| 900 \| 0.149 \| - \|
	\| 0.0615 \| 950 \| 0.1414 \| - \|
	\| 0.0648 \| 1000 \| 0.1357 \| - \|
	\| 0.0680 \| 1050 \| 0.1483 \| - \|
	\| 0.0713 \| 1100 \| 0.1302 \| - \|
	\| 0.0745 \| 1150 \| 0.14 \| - \|
	\| 0.0777 \| 1200 \| 0.1479 \| - \|
	\| 0.0810 \| 1250 \| 0.1496 \| - \|
	\| 0.0842 \| 1300 \| 0.1308 \| - \|
	\| 0.0874 \| 1350 \| 0.1509 \| - \|
	\| 0.0907 \| 1400 \| 0.15 \| - \|
	\| 0.0939 \| 1450 \| 0.1516 \| - \|
	\| 0.0972 \| 1500 \| 0.1319 \| - \|
	\| 0.1004 \| 1550 \| 0.1349 \| - \|
	\| 0.1036 \| 1600 \| 0.1398 \| - \|
	\| 0.1069 \| 1650 \| 0.1276 \| - \|
	\| 0.1101 \| 1700 \| 0.1309 \| - \|
	\| 0.1134 \| 1750 \| 0.1408 \| - \|
	\| 0.1166 \| 1800 \| 0.1416 \| - \|
	\| 0.1198 \| 1850 \| 0.1371 \| - \|
	\| 0.1231 \| 1900 \| 0.1266 \| - \|
	\| 0.1263 \| 1950 \| 0.1257 \| - \|
	\| 0.1296 \| 2000 \| 0.1337 \| - \|
	\| 0.1328 \| 2050 \| 0.1475 \| - \|
	\| 0.1360 \| 2100 \| 0.1412 \| - \|
	\| 0.1393 \| 2150 \| 0.1412 \| - \|
	\| 0.1425 \| 2200 \| 0.1281 \| - \|
	\| 0.1457 \| 2250 \| 0.1293 \| - \|
	\| 0.1490 \| 2300 \| 0.1186 \| - \|
	\| 0.1522 \| 2350 \| 0.142 \| - \|
	\| 0.1555 \| 2400 \| 0.1327 \| - \|
	\| 0.1587 \| 2450 \| 0.1356 \| - \|
	\| 0.1619 \| 2500 \| 0.1357 \| - \|
	\| 0.1652 \| 2550 \| 0.1235 \| - \|
	\| 0.1684 \| 2600 \| 0.1448 \| - \|
	\| 0.1717 \| 2650 \| 0.1274 \| - \|
	\| 0.1749 \| 2700 \| 0.1138 \| - \|
	\| 0.1781 \| 2750 \| 0.13 \| - \|
	\| 0.1814 \| 2800 \| 0.1231 \| - \|
	\| 0.1846 \| 2850 \| 0.1258 \| - \|
	\| 0.1878 \| 2900 \| 0.1148 \| - \|
	\| 0.1911 \| 2950 \| 0.1249 \| - \|
	\| 0.1943 \| 3000 \| 0.1281 \| - \|
	\| 0.1976 \| 3050 \| 0.1239 \| - \|
	\| 0.2008 \| 3100 \| 0.1205 \| - \|
	\| 0.2040 \| 3150 \| 0.1265 \| - \|
	\| 0.2073 \| 3200 \| 0.1371 \| - \|
	\| 0.2105 \| 3250 \| 0.1285 \| - \|
	\| 0.2138 \| 3300 \| 0.1365 \| - \|
	\| 0.2170 \| 3350 \| 0.1344 \| - \|
	\| 0.2202 \| 3400 \| 0.1329 \| - \|
	\| 0.2235 \| 3450 \| 0.1393 \| - \|
	\| 0.2267 \| 3500 \| 0.1313 \| - \|
	\| 0.2300 \| 3550 \| 0.1141 \| - \|
	\| 0.2332 \| 3600 \| 0.1255 \| - \|
	\| 0.2364 \| 3650 \| 0.1239 \| - \|
	\| 0.2397 \| 3700 \| 0.1215 \| - \|
	\| 0.2429 \| 3750 \| 0.1208 \| - \|
	\| 0.2461 \| 3800 \| 0.1339 \| - \|
	\| 0.2494 \| 3850 \| 0.1298 \| - \|
	\| 0.2526 \| 3900 \| 0.1275 \| - \|
	\| 0.2559 \| 3950 \| 0.126 \| - \|
	\| 0.2591 \| 4000 \| 0.1106 \| - \|
	\| 0.2623 \| 4050 \| 0.1301 \| - \|
	\| 0.2656 \| 4100 \| 0.1066 \| - \|
	\| 0.2688 \| 4150 \| 0.1309 \| - \|
	\| 0.2721 \| 4200 \| 0.1205 \| - \|
	\| 0.2753 \| 4250 \| 0.1371 \| - \|
	\| 0.2785 \| 4300 \| 0.1215 \| - \|
	\| 0.2818 \| 4350 \| 0.1204 \| - \|
	\| 0.2850 \| 4400 \| 0.1183 \| - \|
	\| 0.2882 \| 4450 \| 0.1189 \| - \|
	\| 0.2915 \| 4500 \| 0.1129 \| - \|
	\| 0.2947 \| 4550 \| 0.115 \| - \|
	\| 0.2980 \| 4600 \| 0.1152 \| - \|
	\| 0.3012 \| 4650 \| 0.1122 \| - \|
	\| 0.3044 \| 4700 \| 0.1217 \| - \|
	\| 0.3077 \| 4750 \| 0.103 \| - \|
	\| 0.3109 \| 4800 \| 0.1203 \| - \|
	\| 0.3142 \| 4850 \| 0.1253 \| - \|
	\| 0.3174 \| 4900 \| 0.1123 \| - \|
	\| 0.3206 \| 4950 \| 0.1262 \| - \|
	\| 0.3239 \| 5000 \| 0.1115 \| - \|
	\| 0.3271 \| 5050 \| 0.1219 \| - \|
	\| 0.3304 \| 5100 \| 0.1185 \| - \|
	\| 0.3336 \| 5150 \| 0.1242 \| - \|
	\| 0.3368 \| 5200 \| 0.123 \| - \|
	\| 0.3401 \| 5250 \| 0.1055 \| - \|
	\| 0.3433 \| 5300 \| 0.116 \| - \|
	\| 0.3465 \| 5350 \| 0.1173 \| - \|
	\| 0.3498 \| 5400 \| 0.1116 \| - \|
	\| 0.3530 \| 5450 \| 0.1173 \| - \|
	\| 0.3563 \| 5500 \| 0.107 \| - \|
	\| 0.3595 \| 5550 \| 0.1052 \| - \|
	\| 0.3627 \| 5600 \| 0.1119 \| - \|
	\| 0.3660 \| 5650 \| 0.1116 \| - \|
	\| 0.3692 \| 5700 \| 0.1153 \| - \|
	\| 0.3725 \| 5750 \| 0.1039 \| - \|
	\| 0.3757 \| 5800 \| 0.1187 \| - \|
	\| 0.3789 \| 5850 \| 0.1106 \| - \|
	\| 0.3822 \| 5900 \| 0.111 \| - \|
	\| 0.3854 \| 5950 \| 0.1018 \| - \|
	\| 0.3887 \| 6000 \| 0.1065 \| - \|
	\| 0.3919 \| 6050 \| 0.1044 \| - \|
	\| 0.3951 \| 6100 \| 0.1037 \| - \|
	\| 0.3984 \| 6150 \| 0.0991 \| - \|
	\| 0.4016 \| 6200 \| 0.0984 \| - \|
	\| 0.4048 \| 6250 \| 0.1058 \| - \|
	\| 0.4081 \| 6300 \| 0.0954 \| - \|
	\| 0.4113 \| 6350 \| 0.0883 \| - \|
	\| 0.4146 \| 6400 \| 0.1077 \| - \|
	\| 0.4178 \| 6450 \| 0.1134 \| - \|
	\| 0.4210 \| 6500 \| 0.1079 \| - \|
	\| 0.4243 \| 6550 \| 0.0996 \| - \|
	\| 0.4275 \| 6600 \| 0.1045 \| - \|
	\| 0.4308 \| 6650 \| 0.1151 \| - \|
	\| 0.4340 \| 6700 \| 0.1062 \| - \|
	\| 0.4372 \| 6750 \| 0.1077 \| - \|
	\| 0.4405 \| 6800 \| 0.1133 \| - \|
	\| 0.4437 \| 6850 \| 0.1096 \| - \|
	\| 0.4469 \| 6900 \| 0.1017 \| - \|
	\| 0.4502 \| 6950 \| 0.0972 \| - \|
	\| 0.4534 \| 7000 \| 0.0955 \| - \|
	\| 0.4567 \| 7050 \| 0.0986 \| - \|
	\| 0.4599 \| 7100 \| 0.0942 \| - \|
	\| 0.4631 \| 7150 \| 0.1093 \| - \|
	\| 0.4664 \| 7200 \| 0.0908 \| - \|
	\| 0.4696 \| 7250 \| 0.1165 \| - \|
	\| 0.4729 \| 7300 \| 0.0979 \| - \|
	\| 0.4761 \| 7350 \| 0.0915 \| - \|
	\| 0.4793 \| 7400 \| 0.0824 \| - \|
	\| 0.4826 \| 7450 \| 0.0988 \| - \|
	\| 0.4858 \| 7500 \| 0.112 \| - \|
	\| 0.4891 \| 7550 \| 0.0997 \| - \|
	\| 0.4923 \| 7600 \| 0.1013 \| - \|
	\| 0.4955 \| 7650 \| 0.1119 \| - \|
	\| 0.4988 \| 7700 \| 0.1087 \| - \|
	\| 0.5020 \| 7750 \| 0.1037 \| - \|
	\| 0.5052 \| 7800 \| 0.0995 \| - \|
	\| 0.5085 \| 7850 \| 0.0913 \| - \|
	\| 0.5117 \| 7900 \| 0.1006 \| - \|
	\| 0.5150 \| 7950 \| 0.0916 \| - \|
	\| 0.5182 \| 8000 \| 0.0861 \| - \|
	\| 0.5214 \| 8050 \| 0.1135 \| - \|
	\| 0.5247 \| 8100 \| 0.0956 \| - \|
	\| 0.5279 \| 8150 \| 0.1007 \| - \|
	\| 0.5312 \| 8200 \| 0.0898 \| - \|
	\| 0.5344 \| 8250 \| 0.1079 \| - \|
	\| 0.5376 \| 8300 \| 0.093 \| - \|
	\| 0.5409 \| 8350 \| 0.0957 \| - \|
	\| 0.5441 \| 8400 \| 0.0945 \| - \|
	\| 0.5474 \| 8450 \| 0.0929 \| - \|
	\| 0.5506 \| 8500 \| 0.0933 \| - \|
	\| 0.5538 \| 8550 \| 0.0948 \| - \|
	\| 0.5571 \| 8600 \| 0.0793 \| - \|
	\| 0.5603 \| 8650 \| 0.0888 \| - \|
	\| 0.5635 \| 8700 \| 0.0835 \| - \|
	\| 0.5668 \| 8750 \| 0.0809 \| - \|
	\| 0.5700 \| 8800 \| 0.1069 \| - \|
	\| 0.5733 \| 8850 \| 0.0885 \| - \|
	\| 0.5765 \| 8900 \| 0.089 \| - \|
	\| 0.5797 \| 8950 \| 0.1028 \| - \|
	\| 0.5830 \| 9000 \| 0.0842 \| - \|
	\| 0.5862 \| 9050 \| 0.0946 \| - \|
	\| 0.5895 \| 9100 \| 0.0989 \| - \|
	\| 0.5927 \| 9150 \| 0.0827 \| - \|
	\| 0.5959 \| 9200 \| 0.0798 \| - \|
	\| 0.5992 \| 9250 \| 0.0855 \| - \|
	\| 0.6024 \| 9300 \| 0.091 \| - \|
	\| 0.6056 \| 9350 \| 0.0905 \| - \|
	\| 0.6089 \| 9400 \| 0.0844 \| - \|
	\| 0.6121 \| 9450 \| 0.0783 \| - \|
	\| 0.6154 \| 9500 \| 0.0838 \| - \|
	\| 0.6186 \| 9550 \| 0.0992 \| - \|
	\| 0.6218 \| 9600 \| 0.0954 \| - \|
	\| 0.6251 \| 9650 \| 0.0817 \| - \|
	\| 0.6283 \| 9700 \| 0.0895 \| - \|
	\| 0.6316 \| 9750 \| 0.0818 \| - \|
	\| 0.6348 \| 9800 \| 0.0806 \| - \|
	\| 0.6380 \| 9850 \| 0.0895 \| - \|
	\| 0.6413 \| 9900 \| 0.0925 \| - \|
	\| 0.6445 \| 9950 \| 0.0865 \| - \|
	\| 0.6478 \| 10000 \| 0.0881 \| - \|
	\| 0.6510 \| 10050 \| 0.0804 \| - \|
	\| 0.6542 \| 10100 \| 0.0951 \| - \|
	\| 0.6575 \| 10150 \| 0.0998 \| - \|
	\| 0.6607 \| 10200 \| 0.0892 \| - \|
	\| 0.6639 \| 10250 \| 0.0824 \| - \|
	\| 0.6672 \| 10300 \| 0.0856 \| - \|
	\| 0.6704 \| 10350 \| 0.0821 \| - \|
	\| 0.6737 \| 10400 \| 0.0949 \| - \|
	\| 0.6769 \| 10450 \| 0.0918 \| - \|
	\| 0.6801 \| 10500 \| 0.0868 \| - \|
	\| 0.6834 \| 10550 \| 0.0922 \| - \|
	\| 0.6866 \| 10600 \| 0.0845 \| - \|
	\| 0.6899 \| 10650 \| 0.0752 \| - \|
	\| 0.6931 \| 10700 \| 0.0904 \| - \|
	\| 0.6963 \| 10750 \| 0.0837 \| - \|
	\| 0.6996 \| 10800 \| 0.0846 \| - \|
	\| 0.7028 \| 10850 \| 0.0904 \| - \|
	\| 0.7061 \| 10900 \| 0.0819 \| - \|
	\| 0.7093 \| 10950 \| 0.0851 \| - \|
	\| 0.7125 \| 11000 \| 0.0755 \| - \|
	\| 0.7158 \| 11050 \| 0.0856 \| - \|
	\| 0.7190 \| 11100 \| 0.0978 \| - \|
	\| 0.7222 \| 11150 \| 0.0764 \| - \|
	\| 0.7255 \| 11200 \| 0.0837 \| - \|
	\| 0.7287 \| 11250 \| 0.0896 \| - \|
	\| 0.7320 \| 11300 \| 0.0878 \| - \|
	\| 0.7352 \| 11350 \| 0.0799 \| - \|
	\| 0.7384 \| 11400 \| 0.0819 \| - \|
	\| 0.7417 \| 11450 \| 0.0864 \| - \|
	\| 0.7449 \| 11500 \| 0.085 \| - \|
	\| 0.7482 \| 11550 \| 0.092 \| - \|
	\| 0.7514 \| 11600 \| 0.08 \| - \|
	\| 0.7546 \| 11650 \| 0.0828 \| - \|
	\| 0.7579 \| 11700 \| 0.078 \| - \|
	\| 0.7611 \| 11750 \| 0.0787 \| - \|
	\| 0.7643 \| 11800 \| 0.0818 \| - \|
	\| 0.7676 \| 11850 \| 0.0872 \| - \|
	\| 0.7708 \| 11900 \| 0.0857 \| - \|
	\| 0.7741 \| 11950 \| 0.0891 \| - \|
	\| 0.7773 \| 12000 \| 0.0731 \| - \|
	\| 0.7805 \| 12050 \| 0.0881 \| - \|
	\| 0.7838 \| 12100 \| 0.0735 \| - \|
	\| 0.7870 \| 12150 \| 0.0825 \| - \|
	\| 0.7903 \| 12200 \| 0.0799 \| - \|
	\| 0.7935 \| 12250 \| 0.0783 \| - \|
	\| 0.7967 \| 12300 \| 0.081 \| - \|
	\| 0.8000 \| 12350 \| 0.0847 \| - \|
	\| 0.8032 \| 12400 \| 0.0851 \| - \|
	\| 0.8065 \| 12450 \| 0.0783 \| - \|
	\| 0.8097 \| 12500 \| 0.0634 \| - \|
	\| 0.8129 \| 12550 \| 0.0767 \| - \|
	\| 0.8162 \| 12600 \| 0.0836 \| - \|
	\| 0.8194 \| 12650 \| 0.0871 \| - \|
	\| 0.8226 \| 12700 \| 0.0787 \| - \|
	\| 0.8259 \| 12750 \| 0.0755 \| - \|
	\| 0.8291 \| 12800 \| 0.0787 \| - \|
	\| 0.8324 \| 12850 \| 0.0764 \| - \|
	\| 0.8356 \| 12900 \| 0.077 \| - \|
	\| 0.8388 \| 12950 \| 0.0821 \| - \|
	\| 0.8421 \| 13000 \| 0.0756 \| - \|
	\| 0.8453 \| 13050 \| 0.0798 \| - \|
	\| 0.8486 \| 13100 \| 0.0699 \| - \|
	\| 0.8518 \| 13150 \| 0.0823 \| - \|
	\| 0.8550 \| 13200 \| 0.0783 \| - \|
	\| 0.8583 \| 13250 \| 0.078 \| - \|
	\| 0.8615 \| 13300 \| 0.0742 \| - \|
	\| 0.8647 \| 13350 \| 0.078 \| - \|
	\| 0.8680 \| 13400 \| 0.0835 \| - \|
	\| 0.8712 \| 13450 \| 0.0719 \| - \|
	\| 0.8745 \| 13500 \| 0.0774 \| - \|
	\| 0.8777 \| 13550 \| 0.0855 \| - \|
	\| 0.8809 \| 13600 \| 0.0873 \| - \|
	\| 0.8842 \| 13650 \| 0.084 \| - \|
	\| 0.8874 \| 13700 \| 0.0853 \| - \|
	\| 0.8907 \| 13750 \| 0.0833 \| - \|
	\| 0.8939 \| 13800 \| 0.0811 \| - \|
	\| 0.8971 \| 13850 \| 0.0727 \| - \|
	\| 0.9004 \| 13900 \| 0.0677 \| - \|
	\| 0.9036 \| 13950 \| 0.0666 \| - \|
	\| 0.9069 \| 14000 \| 0.0764 \| - \|
	\| 0.9101 \| 14050 \| 0.0729 \| - \|
	\| 0.9133 \| 14100 \| 0.0781 \| - \|
	\| 0.9166 \| 14150 \| 0.0917 \| - \|
	\| 0.9198 \| 14200 \| 0.0878 \| - \|
	\| 0.9230 \| 14250 \| 0.0734 \| - \|
	\| 0.9263 \| 14300 \| 0.0825 \| - \|
	\| 0.9295 \| 14350 \| 0.0799 \| - \|
	\| 0.9328 \| 14400 \| 0.0817 \| - \|
	\| 0.9360 \| 14450 \| 0.0757 \| - \|
	\| 0.9392 \| 14500 \| 0.0755 \| - \|
	\| 0.9425 \| 14550 \| 0.062 \| - \|
	\| 0.9457 \| 14600 \| 0.0829 \| - \|
	\| 0.9490 \| 14650 \| 0.0718 \| - \|
	\| 0.9522 \| 14700 \| 0.0776 \| - \|
	\| 0.9554 \| 14750 \| 0.0744 \| - \|
	\| 0.9587 \| 14800 \| 0.0881 \| - \|
	\| 0.9619 \| 14850 \| 0.0813 \| - \|
	\| 0.9652 \| 14900 \| 0.0673 \| - \|
	\| 0.9684 \| 14950 \| 0.0819 \| - \|
	\| 0.9716 \| 15000 \| 0.0566 \| - \|
	\| 0.9749 \| 15050 \| 0.0849 \| - \|
	\| 0.9781 \| 15100 \| 0.0736 \| - \|
	\| 0.9813 \| 15150 \| 0.0661 \| - \|
	\| 0.9846 \| 15200 \| 0.0731 \| - \|
	\| 0.9878 \| 15250 \| 0.0779 \| - \|
	\| 0.9911 \| 15300 \| 0.0723 \| - \|
	\| 0.9943 \| 15350 \| 0.0606 \| - \|
	\| 0.9975 \| 15400 \| 0.0801 \| - \|
	\| 1.0008 \| 15450 \| 0.0675 \| - \|
	\| 1.0040 \| 15500 \| 0.0743 \| - \|
	\| 1.0073 \| 15550 \| 0.0655 \| - \|
	\| 1.0105 \| 15600 \| 0.0594 \| - \|
	\| 1.0137 \| 15650 \| 0.0642 \| - \|
	\| 1.0170 \| 15700 \| 0.059 \| - \|
	\| 1.0202 \| 15750 \| 0.0628 \| - \|
	\| 1.0234 \| 15800 \| 0.0569 \| - \|
	\| 1.0267 \| 15850 \| 0.0725 \| - \|
	\| 1.0299 \| 15900 \| 0.0744 \| - \|
	\| 1.0332 \| 15950 \| 0.056 \| - \|
	\| 1.0364 \| 16000 \| 0.0754 \| - \|
	\| 1.0396 \| 16050 \| 0.0694 \| - \|
	\| 1.0429 \| 16100 \| 0.057 \| - \|
	\| 1.0461 \| 16150 \| 0.0706 \| - \|
	\| 1.0494 \| 16200 \| 0.0675 \| - \|
	\| 1.0526 \| 16250 \| 0.0679 \| - \|
	\| 1.0558 \| 16300 \| 0.0745 \| - \|
	\| 1.0591 \| 16350 \| 0.0539 \| - \|
	\| 1.0623 \| 16400 \| 0.0708 \| - \|
	\| 1.0656 \| 16450 \| 0.0629 \| - \|
	\| 1.0688 \| 16500 \| 0.0699 \| - \|
	\| 1.0720 \| 16550 \| 0.0706 \| - \|
	\| 1.0753 \| 16600 \| 0.0717 \| - \|
	\| 1.0785 \| 16650 \| 0.0676 \| - \|
	\| 1.0817 \| 16700 \| 0.0619 \| - \|
	\| 1.0850 \| 16750 \| 0.07 \| - \|
	\| 1.0882 \| 16800 \| 0.0569 \| - \|
	\| 1.0915 \| 16850 \| 0.0615 \| - \|
	\| 1.0947 \| 16900 \| 0.0646 \| - \|
	\| 1.0979 \| 16950 \| 0.0651 \| - \|
	\| 1.1012 \| 17000 \| 0.072 \| - \|
	\| 1.1044 \| 17050 \| 0.0693 \| - \|
	\| 1.1077 \| 17100 \| 0.0681 \| - \|
	\| 1.1109 \| 17150 \| 0.0509 \| - \|
	\| 1.1141 \| 17200 \| 0.0604 \| - \|
	\| 1.1174 \| 17250 \| 0.0723 \| - \|
	\| 1.1206 \| 17300 \| 0.0726 \| - \|
	\| 1.1239 \| 17350 \| 0.062 \| - \|
	\| 1.1271 \| 17400 \| 0.0608 \| - \|
	\| 1.1303 \| 17450 \| 0.0649 \| - \|
	\| 1.1336 \| 17500 \| 0.0631 \| - \|
	\| 1.1368 \| 17550 \| 0.0623 \| - \|
	\| 1.1400 \| 17600 \| 0.0707 \| - \|
	\| 1.1433 \| 17650 \| 0.0708 \| - \|
	\| 1.1465 \| 17700 \| 0.0736 \| - \|
	\| 1.1498 \| 17750 \| 0.0674 \| - \|
	\| 1.1530 \| 17800 \| 0.0759 \| - \|
	\| 1.1562 \| 17850 \| 0.0614 \| - \|
	\| 1.1595 \| 17900 \| 0.0626 \| - \|
	\| 1.1627 \| 17950 \| 0.0741 \| - \|
	\| 1.1660 \| 18000 \| 0.065 \| - \|
	\| 1.1692 \| 18050 \| 0.069 \| - \|
	\| 1.1724 \| 18100 \| 0.0749 \| - \|
	\| 1.1757 \| 18150 \| 0.0554 \| - \|
	\| 1.1789 \| 18200 \| 0.068 \| - \|
	\| 1.1821 \| 18250 \| 0.0676 \| - \|
	\| 1.1854 \| 18300 \| 0.067 \| - \|
	\| 1.1886 \| 18350 \| 0.0682 \| - \|
	\| 1.1919 \| 18400 \| 0.0546 \| - \|
	\| 1.1951 \| 18450 \| 0.068 \| - \|
	\| 1.1983 \| 18500 \| 0.0633 \| - \|
	\| 1.2016 \| 18550 \| 0.0627 \| - \|
	\| 1.2048 \| 18600 \| 0.0608 \| - \|
	\| 1.2081 \| 18650 \| 0.0625 \| - \|
	\| 1.2113 \| 18700 \| 0.0652 \| - \|
	\| 1.2145 \| 18750 \| 0.0555 \| - \|
	\| 1.2178 \| 18800 \| 0.0615 \| - \|
	\| 1.2210 \| 18850 \| 0.0599 \| - \|
	\| 1.2243 \| 18900 \| 0.0664 \| - \|
	\| 1.2275 \| 18950 \| 0.0653 \| - \|
	\| 1.2307 \| 19000 \| 0.0481 \| - \|
	\| 1.2340 \| 19050 \| 0.055 \| - \|
	\| 1.2372 \| 19100 \| 0.0681 \| - \|
	\| 1.2404 \| 19150 \| 0.0589 \| - \|
	\| 1.2437 \| 19200 \| 0.0774 \| - \|
	\| 1.2469 \| 19250 \| 0.0624 \| - \|
	\| 1.2502 \| 19300 \| 0.0609 \| - \|
	\| 1.2534 \| 19350 \| 0.0545 \| - \|
	\| 1.2566 \| 19400 \| 0.0546 \| - \|
	\| 1.2599 \| 19450 \| 0.087 \| - \|
	\| 1.2631 \| 19500 \| 0.061 \| - \|
	\| 1.2664 \| 19550 \| 0.068 \| - \|
	\| 1.2696 \| 19600 \| 0.0708 \| - \|
	\| 1.2728 \| 19650 \| 0.0651 \| - \|
	\| 1.2761 \| 19700 \| 0.0713 \| - \|
	\| 1.2793 \| 19750 \| 0.0646 \| - \|
	\| 1.2825 \| 19800 \| 0.0559 \| - \|
	\| 1.2858 \| 19850 \| 0.0486 \| - \|
	\| 1.2890 \| 19900 \| 0.0583 \| - \|
	\| 1.2923 \| 19950 \| 0.0549 \| - \|
	\| 1.2955 \| 20000 \| 0.073 \| - \|
	\| 1.2987 \| 20050 \| 0.0633 \| - \|
	\| 1.3020 \| 20100 \| 0.072 \| - \|
	\| 1.3052 \| 20150 \| 0.0623 \| - \|
	\| 1.3085 \| 20200 \| 0.0725 \| - \|
	\| 1.3117 \| 20250 \| 0.0629 \| - \|
	\| 1.3149 \| 20300 \| 0.0614 \| - \|
	\| 1.3182 \| 20350 \| 0.0607 \| - \|
	\| 1.3214 \| 20400 \| 0.0624 \| - \|
	\| 1.3247 \| 20450 \| 0.0627 \| - \|
	\| 1.3279 \| 20500 \| 0.0602 \| - \|
	\| 1.3311 \| 20550 \| 0.062 \| - \|
	\| 1.3344 \| 20600 \| 0.066 \| - \|
	\| 1.3376 \| 20650 \| 0.0596 \| - \|
	\| 1.3408 \| 20700 \| 0.0517 \| - \|
	\| 1.3441 \| 20750 \| 0.057 \| - \|
	\| 1.3473 \| 20800 \| 0.0584 \| - \|
	\| 1.3506 \| 20850 \| 0.0576 \| - \|
	\| 1.3538 \| 20900 \| 0.0667 \| - \|
	\| 1.3570 \| 20950 \| 0.0672 \| - \|
	\| 1.3603 \| 21000 \| 0.0641 \| - \|
	\| 1.3635 \| 21050 \| 0.0545 \| - \|
	\| 1.3668 \| 21100 \| 0.0609 \| - \|
	\| 1.3700 \| 21150 \| 0.0639 \| - \|
	\| 1.3732 \| 21200 \| 0.0612 \| - \|
	\| 1.3765 \| 21250 \| 0.0623 \| - \|
	\| 1.3797 \| 21300 \| 0.0645 \| - \|
	\| 1.3830 \| 21350 \| 0.0676 \| - \|
	\| 1.3862 \| 21400 \| 0.0704 \| - \|
	\| 1.3894 \| 21450 \| 0.0569 \| - \|
	\| 1.3927 \| 21500 \| 0.066 \| - \|
	\| 1.3959 \| 21550 \| 0.0632 \| - \|
	\| 1.3991 \| 21600 \| 0.0682 \| - \|
	\| 1.4024 \| 21650 \| 0.0694 \| - \|
	\| 1.4056 \| 21700 \| 0.0713 \| - \|
	\| 1.4089 \| 21750 \| 0.0508 \| - \|
	\| 1.4121 \| 21800 \| 0.0613 \| - \|
	\| 1.4153 \| 21850 \| 0.0512 \| - \|
	\| 1.4186 \| 21900 \| 0.0481 \| - \|
	\| 1.4218 \| 21950 \| 0.0578 \| - \|
	\| 1.4251 \| 22000 \| 0.0553 \| - \|
	\| 1.4283 \| 22050 \| 0.0599 \| - \|
	\| 1.4315 \| 22100 \| 0.0626 \| - \|
	\| 1.4348 \| 22150 \| 0.0577 \| - \|
	\| 1.4380 \| 22200 \| 0.0611 \| - \|
	\| 1.4412 \| 22250 \| 0.0579 \| - \|
	\| 1.4445 \| 22300 \| 0.0678 \| - \|
	\| 1.4477 \| 22350 \| 0.0627 \| - \|
	\| 1.4510 \| 22400 \| 0.0582 \| - \|
	\| 1.4542 \| 22450 \| 0.0613 \| - \|
	\| 1.4574 \| 22500 \| 0.0584 \| - \|
	\| 1.4607 \| 22550 \| 0.0586 \| - \|
	\| 1.4639 \| 22600 \| 0.0589 \| - \|
	\| 1.4672 \| 22650 \| 0.0535 \| - \|
	\| 1.4704 \| 22700 \| 0.0526 \| - \|
	\| 1.4736 \| 22750 \| 0.0599 \| - \|
	\| 1.4769 \| 22800 \| 0.0549 \| - \|
	\| 1.4801 \| 22850 \| 0.0598 \| - \|
	\| 1.4834 \| 22900 \| 0.0584 \| - \|
	\| 1.4866 \| 22950 \| 0.0657 \| - \|
	\| 1.4898 \| 23000 \| 0.056 \| - \|
	\| 1.4931 \| 23050 \| 0.061 \| - \|
	\| 1.4963 \| 23100 \| 0.0567 \| - \|
	\| 1.4995 \| 23150 \| 0.0604 \| - \|
	\| 1.5028 \| 23200 \| 0.053 \| - \|
	\| 1.5060 \| 23250 \| 0.0577 \| - \|
	\| 1.5093 \| 23300 \| 0.0552 \| - \|
	\| 1.5125 \| 23350 \| 0.0675 \| - \|
	\| 1.5157 \| 23400 \| 0.0698 \| - \|
	\| 1.5190 \| 23450 \| 0.0577 \| - \|
	\| 1.5222 \| 23500 \| 0.0518 \| - \|
	\| 1.5255 \| 23550 \| 0.0552 \| - \|
	\| 1.5287 \| 23600 \| 0.0606 \| - \|
	\| 1.5319 \| 23650 \| 0.0598 \| - \|
	\| 1.5352 \| 23700 \| 0.0586 \| - \|
	\| 1.5384 \| 23750 \| 0.0562 \| - \|
	\| 1.5417 \| 23800 \| 0.0571 \| - \|
	\| 1.5449 \| 23850 \| 0.0525 \| - \|
	\| 1.5481 \| 23900 \| 0.0619 \| - \|
	\| 1.5514 \| 23950 \| 0.0558 \| - \|
	\| 1.5546 \| 24000 \| 0.0651 \| - \|
	\| 1.5578 \| 24050 \| 0.0595 \| - \|
	\| 1.5611 \| 24100 \| 0.0669 \| - \|
	\| 1.5643 \| 24150 \| 0.0576 \| - \|
	\| 1.5676 \| 24200 \| 0.0498 \| - \|
	\| 1.5708 \| 24250 \| 0.0613 \| - \|
	\| 1.5740 \| 24300 \| 0.0544 \| - \|
	\| 1.5773 \| 24350 \| 0.0566 \| - \|
	\| 1.5805 \| 24400 \| 0.0613 \| - \|
	\| 1.5838 \| 24450 \| 0.0597 \| - \|
	\| 1.5870 \| 24500 \| 0.0525 \| - \|
	\| 1.5902 \| 24550 \| 0.0537 \| - \|
	\| 1.5935 \| 24600 \| 0.0613 \| - \|
	\| 1.5967 \| 24650 \| 0.0446 \| - \|
	\| 1.5999 \| 24700 \| 0.0597 \| - \|
	\| 1.6032 \| 24750 \| 0.0601 \| - \|
	\| 1.6064 \| 24800 \| 0.0521 \| - \|
	\| 1.6097 \| 24850 \| 0.0584 \| - \|
	\| 1.6129 \| 24900 \| 0.0591 \| - \|
	\| 1.6161 \| 24950 \| 0.0593 \| - \|
	\| 1.6194 \| 25000 \| 0.0562 \| - \|
	\| 1.6226 \| 25050 \| 0.0586 \| - \|
	\| 1.6259 \| 25100 \| 0.0593 \| - \|
	\| 1.6291 \| 25150 \| 0.0615 \| - \|
	\| 1.6323 \| 25200 \| 0.0492 \| - \|
	\| 1.6356 \| 25250 \| 0.0573 \| - \|
	\| 1.6388 \| 25300 \| 0.0631 \| - \|
	\| 1.6421 \| 25350 \| 0.0444 \| - \|
	\| 1.6453 \| 25400 \| 0.0587 \| - \|
	\| 1.6485 \| 25450 \| 0.0601 \| - \|
	\| 1.6518 \| 25500 \| 0.0565 \| - \|
	\| 1.6550 \| 25550 \| 0.0654 \| - \|
	\| 1.6582 \| 25600 \| 0.0558 \| - \|
	\| 1.6615 \| 25650 \| 0.0537 \| - \|
	\| 1.6647 \| 25700 \| 0.0504 \| - \|
	\| 1.6680 \| 25750 \| 0.0549 \| - \|
	\| 1.6712 \| 25800 \| 0.0517 \| - \|
	\| 1.6744 \| 25850 \| 0.0621 \| - \|
	\| 1.6777 \| 25900 \| 0.0468 \| - \|
	\| 1.6809 \| 25950 \| 0.059 \| - \|
	\| 1.6842 \| 26000 \| 0.0607 \| - \|
	\| 1.6874 \| 26050 \| 0.0616 \| - \|
	\| 1.6906 \| 26100 \| 0.0536 \| - \|
	\| 1.6939 \| 26150 \| 0.0619 \| - \|
	\| 1.6971 \| 26200 \| 0.0615 \| - \|
	\| 1.7003 \| 26250 \| 0.0497 \| - \|
	\| 1.7036 \| 26300 \| 0.0595 \| - \|
	\| 1.7068 \| 26350 \| 0.0563 \| - \|
	\| 1.7101 \| 26400 \| 0.0572 \| - \|
	\| 1.7133 \| 26450 \| 0.0525 \| - \|
	\| 1.7165 \| 26500 \| 0.0592 \| - \|
	\| 1.7198 \| 26550 \| 0.0645 \| - \|
	\| 1.7230 \| 26600 \| 0.0586 \| - \|
	\| 1.7263 \| 26650 \| 0.0511 \| - \|
	\| 1.7295 \| 26700 \| 0.0508 \| - \|
	\| 1.7327 \| 26750 \| 0.0572 \| - \|
	\| 1.7360 \| 26800 \| 0.0466 \| - \|
	\| 1.7392 \| 26850 \| 0.0532 \| - \|
	\| 1.7425 \| 26900 \| 0.0546 \| - \|
	\| 1.7457 \| 26950 \| 0.0594 \| - \|
	\| 1.7489 \| 27000 \| 0.0544 \| - \|
	\| 1.7522 \| 27050 \| 0.0543 \| - \|
	\| 1.7554 \| 27100 \| 0.0588 \| - \|
	\| 1.7586 \| 27150 \| 0.0552 \| - \|
	\| 1.7619 \| 27200 \| 0.0529 \| - \|
	\| 1.7651 \| 27250 \| 0.0558 \| - \|
	\| 1.7684 \| 27300 \| 0.0491 \| - \|
	\| 1.7716 \| 27350 \| 0.0669 \| - \|
	\| 1.7748 \| 27400 \| 0.056 \| - \|
	\| 1.7781 \| 27450 \| 0.0553 \| - \|
	\| 1.7813 \| 27500 \| 0.0565 \| - \|
	\| 1.7846 \| 27550 \| 0.063 \| - \|
	\| 1.7878 \| 27600 \| 0.0548 \| - \|
	\| 1.7910 \| 27650 \| 0.0541 \| - \|
	\| 1.7943 \| 27700 \| 0.0469 \| - \|
	\| 1.7975 \| 27750 \| 0.0493 \| - \|
	\| 1.8008 \| 27800 \| 0.0644 \| - \|
	\| 1.8040 \| 27850 \| 0.0557 \| - \|
	\| 1.8072 \| 27900 \| 0.0582 \| - \|
	\| 1.8105 \| 27950 \| 0.0517 \| - \|
	\| 1.8137 \| 28000 \| 0.0564 \| - \|
	\| 1.8169 \| 28050 \| 0.0591 \| - \|
	\| 1.8202 \| 28100 \| 0.0545 \| - \|
	\| 1.8234 \| 28150 \| 0.0486 \| - \|
	\| 1.8267 \| 28200 \| 0.0568 \| - \|
	\| 1.8299 \| 28250 \| 0.0461 \| - \|
	\| 1.8331 \| 28300 \| 0.0622 \| - \|
	\| 1.8364 \| 28350 \| 0.0499 \| - \|
	\| 1.8396 \| 28400 \| 0.0634 \| - \|
	\| 1.8429 \| 28450 \| 0.0584 \| - \|
	\| 1.8461 \| 28500 \| 0.0648 \| - \|
	\| 1.8493 \| 28550 \| 0.0628 \| - \|
	\| 1.8526 \| 28600 \| 0.057 \| - \|
	\| 1.8558 \| 28650 \| 0.0528 \| - \|
	\| 1.8590 \| 28700 \| 0.0521 \| - \|
	\| 1.8623 \| 28750 \| 0.0527 \| - \|
	\| 1.8655 \| 28800 \| 0.0457 \| - \|
	\| 1.8688 \| 28850 \| 0.0505 \| - \|
	\| 1.8720 \| 28900 \| 0.0508 \| - \|
	\| 1.8752 \| 28950 \| 0.0595 \| - \|
	\| 1.8785 \| 29000 \| 0.0558 \| - \|
	\| 1.8817 \| 29050 \| 0.0521 \| - \|
	\| 1.8850 \| 29100 \| 0.0475 \| - \|
	\| 1.8882 \| 29150 \| 0.054 \| - \|
	\| 1.8914 \| 29200 \| 0.0497 \| - \|
	\| 1.8947 \| 29250 \| 0.0637 \| - \|
	\| 1.8979 \| 29300 \| 0.0484 \| - \|
	\| 1.9012 \| 29350 \| 0.0649 \| - \|
	\| 1.9044 \| 29400 \| 0.0643 \| - \|
	\| 1.9076 \| 29450 \| 0.0484 \| - \|
	\| 1.9109 \| 29500 \| 0.0531 \| - \|
	\| 1.9141 \| 29550 \| 0.0527 \| - \|
	\| 1.9173 \| 29600 \| 0.0617 \| - \|
	\| 1.9206 \| 29650 \| 0.0469 \| - \|
	\| 1.9238 \| 29700 \| 0.0615 \| - \|
	\| 1.9271 \| 29750 \| 0.055 \| - \|
	\| 1.9303 \| 29800 \| 0.055 \| - \|
	\| 1.9335 \| 29850 \| 0.0658 \| - \|
	\| 1.9368 \| 29900 \| 0.0483 \| - \|
	\| 1.9400 \| 29950 \| 0.0559 \| - \|
	\| 1.9433 \| 30000 \| 0.0481 \| - \|
	\| 1.9465 \| 30050 \| 0.0719 \| - \|
	\| 1.9497 \| 30100 \| 0.0589 \| - \|
	\| 1.9530 \| 30150 \| 0.0498 \| - \|
	\| 1.9562 \| 30200 \| 0.049 \| - \|
	\| 1.9595 \| 30250 \| 0.0456 \| - \|
	\| 1.9627 \| 30300 \| 0.0551 \| - \|
	\| 1.9659 \| 30350 \| 0.0415 \| - \|
	\| 1.9692 \| 30400 \| 0.0607 \| - \|
	\| 1.9724 \| 30450 \| 0.0521 \| - \|
	\| 1.9756 \| 30500 \| 0.0545 \| - \|
	\| 1.9789 \| 30550 \| 0.0544 \| - \|
	\| 1.9821 \| 30600 \| 0.0535 \| - \|
	\| 1.9854 \| 30650 \| 0.0637 \| - \|
	\| 1.9886 \| 30700 \| 0.0555 \| - \|
	\| 1.9918 \| 30750 \| 0.0472 \| - \|
	\| 1.9951 \| 30800 \| 0.0544 \| - \|
	\| 1.9983 \| 30850 \| 0.0592 \| - \|

	### 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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