Push model using huggingface_hub.

b3a2b81 verified 8 months ago

39.1 kB

	---
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: This expenditure has financed projects in road works, energy, agriculture
	and water. Madam Speaker, priority allocations are being made to power generation,
	road networks, irrigation schemes, schools and improvement of health infrastructure.
	Addressing constraints in transport, energy and health and education and improving
	service delivery, will accord Ugandans a better quality of life.
	- text: interoperability, acceptance) that are not exclusively related to G2P programs
	and that need to be addressed to realize digital payments’ benefits. Unemployment
	benefits Social security contributions Labor Markets Activation measures Labor
	market regulations Reduced work time Wage subsidies 418 (back to the top) Sudan
	Social Assistance Cash-based transfers Cash transfers (conditional and unconditional)
	One-off cash transfers Childcare support Social pensions In-kind transfers Food,
	vouchers, others • The ministry of labor and social development will provide in
	kind support to poor households, informal workers, teachers, and casual workers
	(total 2,050,000 households). A total of 100,000 Bahraini will benefit from the
	measure (cost of BD 215 million)54 55 Social security contributions Labor Markets
	Activation measures Labor market regulations Reduced work time Wage subsidies
	54 https://www.moh.gov.bh/COVID19/Details/3969 55 https://www.moh.gov.bh/COVID19/Details/3982
	70 (back to the top) Bangladesh Social Assistance Cash-based transfers Cash transfers
	(conditional and unconditional) • Benefit under key safety net programs will be
	increased (amount not determined yet).
	- text: National Food and Nutrition Strategic Plan 2011-2015 55 7) Promote practices
	that enhance sustainable availability, accessibility and consumption of a variety
	of foods at household level. National Food and Nutrition Strategic Plan 2011-2015
	54 5.11 Strategic Direction 11 Expanding and Developing Communication and Advocacy
	Support for Food and Nutrition Interventions at Various Levels. National Food
	and Nutrition Strategic Plan 2011-2015 18 3.
	- text: 13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali
	sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea)
	1. 4 (Disciplina delle aree sanitarie temporanee) 1.
	- text: Furthermore, there is a need for improvements in forecasting, distribution
	and funding of micronutrient commodities, as well as the provision of adequate
	resources to ensure universal coverage. The National Nutrition Program is also
	responsible for estimating the demand of nutrition commodities, such as vitamin
	A capsules, iron/folic acid tablets, and Mebedazole for deworming. It is therefore
	limited in scope to address the full spectrum of causes of undernutrition, which
	requires a broad coalition of multisectoral interventions.
	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_g20_multilabel_MiniLM-L12-v2_15_sample")
	# Run inference
	preds = model("13 (Deroga delle norme in materia di riconoscimento delle qualifiche professionali sanitarie) 1. 93 (Disposizioni in materia di autoservizi pubblici non di linea) 1. 4 (Disciplina delle aree sanitarie temporanee) 1.")
	```

	<!--
	### 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 \| 3 \| 93.9143 \| 1651 \|

	### Training Hyperparameters
	- batch_size: (8, 8)
	- num_epochs: (4, 4)
	- 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 \| 0.2185 \| - \|
	\| 0.0068 \| 50 \| 0.1579 \| - \|
	\| 0.0136 \| 100 \| 0.1625 \| - \|
	\| 0.0204 \| 150 \| 0.1649 \| - \|
	\| 0.0272 \| 200 \| 0.1511 \| - \|
	\| 0.0340 \| 250 \| 0.1263 \| - \|
	\| 0.0408 \| 300 \| 0.1335 \| - \|
	\| 0.0476 \| 350 \| 0.1276 \| - \|
	\| 0.0544 \| 400 \| 0.1143 \| - \|
	\| 0.0612 \| 450 \| 0.1095 \| - \|
	\| 0.0680 \| 500 \| 0.1029 \| - \|
	\| 0.0748 \| 550 \| 0.1161 \| - \|
	\| 0.0816 \| 600 \| 0.114 \| - \|
	\| 0.0884 \| 650 \| 0.0945 \| - \|
	\| 0.0952 \| 700 \| 0.0903 \| - \|
	\| 0.1020 \| 750 \| 0.0793 \| - \|
	\| 0.1088 \| 800 \| 0.0848 \| - \|
	\| 0.1156 \| 850 \| 0.0802 \| - \|
	\| 0.1224 \| 900 \| 0.0819 \| - \|
	\| 0.1293 \| 950 \| 0.0802 \| - \|
	\| 0.1361 \| 1000 \| 0.0879 \| - \|
	\| 0.1429 \| 1050 \| 0.0738 \| - \|
	\| 0.1497 \| 1100 \| 0.0737 \| - \|
	\| 0.1565 \| 1150 \| 0.0761 \| - \|
	\| 0.1633 \| 1200 \| 0.0715 \| - \|
	\| 0.1701 \| 1250 \| 0.0633 \| - \|
	\| 0.1769 \| 1300 \| 0.06 \| - \|
	\| 0.1837 \| 1350 \| 0.06 \| - \|
	\| 0.1905 \| 1400 \| 0.0641 \| - \|
	\| 0.1973 \| 1450 \| 0.057 \| - \|
	\| 0.2041 \| 1500 \| 0.0554 \| - \|
	\| 0.2109 \| 1550 \| 0.0552 \| - \|
	\| 0.2177 \| 1600 \| 0.0447 \| - \|
	\| 0.2245 \| 1650 \| 0.0442 \| - \|
	\| 0.2313 \| 1700 \| 0.0547 \| - \|
	\| 0.2381 \| 1750 \| 0.0358 \| - \|
	\| 0.2449 \| 1800 \| 0.0503 \| - \|
	\| 0.2517 \| 1850 \| 0.0366 \| - \|
	\| 0.2585 \| 1900 \| 0.0421 \| - \|
	\| 0.2653 \| 1950 \| 0.0332 \| - \|
	\| 0.2721 \| 2000 \| 0.0429 \| - \|
	\| 0.2789 \| 2050 \| 0.0316 \| - \|
	\| 0.2857 \| 2100 \| 0.0382 \| - \|
	\| 0.2925 \| 2150 \| 0.0456 \| - \|
	\| 0.2993 \| 2200 \| 0.0327 \| - \|
	\| 0.3061 \| 2250 \| 0.0286 \| - \|
	\| 0.3129 \| 2300 \| 0.0295 \| - \|
	\| 0.3197 \| 2350 \| 0.0305 \| - \|
	\| 0.3265 \| 2400 \| 0.0223 \| - \|
	\| 0.3333 \| 2450 \| 0.0228 \| - \|
	\| 0.3401 \| 2500 \| 0.0305 \| - \|
	\| 0.3469 \| 2550 \| 0.0294 \| - \|
	\| 0.3537 \| 2600 \| 0.0342 \| - \|
	\| 0.3605 \| 2650 \| 0.0275 \| - \|
	\| 0.3673 \| 2700 \| 0.0181 \| - \|
	\| 0.3741 \| 2750 \| 0.0267 \| - \|
	\| 0.3810 \| 2800 \| 0.0229 \| - \|
	\| 0.3878 \| 2850 \| 0.0213 \| - \|
	\| 0.3946 \| 2900 \| 0.0203 \| - \|
	\| 0.4014 \| 2950 \| 0.0281 \| - \|
	\| 0.4082 \| 3000 \| 0.025 \| - \|
	\| 0.4150 \| 3050 \| 0.0233 \| - \|
	\| 0.4218 \| 3100 \| 0.0306 \| - \|
	\| 0.4286 \| 3150 \| 0.0159 \| - \|
	\| 0.4354 \| 3200 \| 0.0246 \| - \|
	\| 0.4422 \| 3250 \| 0.0266 \| - \|
	\| 0.4490 \| 3300 \| 0.0242 \| - \|
	\| 0.4558 \| 3350 \| 0.0103 \| - \|
	\| 0.4626 \| 3400 \| 0.0191 \| - \|
	\| 0.4694 \| 3450 \| 0.0237 \| - \|
	\| 0.4762 \| 3500 \| 0.0216 \| - \|
	\| 0.4830 \| 3550 \| 0.0103 \| - \|
	\| 0.4898 \| 3600 \| 0.0097 \| - \|
	\| 0.4966 \| 3650 \| 0.0158 \| - \|
	\| 0.5034 \| 3700 \| 0.0156 \| - \|
	\| 0.5102 \| 3750 \| 0.0152 \| - \|
	\| 0.5170 \| 3800 \| 0.0187 \| - \|
	\| 0.5238 \| 3850 \| 0.0129 \| - \|
	\| 0.5306 \| 3900 \| 0.0157 \| - \|
	\| 0.5374 \| 3950 \| 0.0161 \| - \|
	\| 0.5442 \| 4000 \| 0.0131 \| - \|
	\| 0.5510 \| 4050 \| 0.0119 \| - \|
	\| 0.5578 \| 4100 \| 0.0213 \| - \|
	\| 0.5646 \| 4150 \| 0.0086 \| - \|
	\| 0.5714 \| 4200 \| 0.0086 \| - \|
	\| 0.5782 \| 4250 \| 0.0121 \| - \|
	\| 0.5850 \| 4300 \| 0.0168 \| - \|
	\| 0.5918 \| 4350 \| 0.0147 \| - \|
	\| 0.5986 \| 4400 \| 0.019 \| - \|
	\| 0.6054 \| 4450 \| 0.0151 \| - \|
	\| 0.6122 \| 4500 \| 0.0298 \| - \|
	\| 0.6190 \| 4550 \| 0.0187 \| - \|
	\| 0.6259 \| 4600 \| 0.013 \| - \|
	\| 0.6327 \| 4650 \| 0.0184 \| - \|
	\| 0.6395 \| 4700 \| 0.0249 \| - \|
	\| 0.6463 \| 4750 \| 0.0157 \| - \|
	\| 0.6531 \| 4800 \| 0.0081 \| - \|
	\| 0.6599 \| 4850 \| 0.0229 \| - \|
	\| 0.6667 \| 4900 \| 0.0227 \| - \|
	\| 0.6735 \| 4950 \| 0.0166 \| - \|
	\| 0.6803 \| 5000 \| 0.0222 \| - \|
	\| 0.6871 \| 5050 \| 0.0066 \| - \|
	\| 0.6939 \| 5100 \| 0.0135 \| - \|
	\| 0.7007 \| 5150 \| 0.0134 \| - \|
	\| 0.7075 \| 5200 \| 0.0134 \| - \|
	\| 0.7143 \| 5250 \| 0.0077 \| - \|
	\| 0.7211 \| 5300 \| 0.0106 \| - \|
	\| 0.7279 \| 5350 \| 0.0086 \| - \|
	\| 0.7347 \| 5400 \| 0.0169 \| - \|
	\| 0.7415 \| 5450 \| 0.0123 \| - \|
	\| 0.7483 \| 5500 \| 0.0085 \| - \|
	\| 0.7551 \| 5550 \| 0.0087 \| - \|
	\| 0.7619 \| 5600 \| 0.0143 \| - \|
	\| 0.7687 \| 5650 \| 0.0112 \| - \|
	\| 0.7755 \| 5700 \| 0.0185 \| - \|
	\| 0.7823 \| 5750 \| 0.0064 \| - \|
	\| 0.7891 \| 5800 \| 0.0077 \| - \|
	\| 0.7959 \| 5850 \| 0.0116 \| - \|
	\| 0.8027 \| 5900 \| 0.0063 \| - \|
	\| 0.8095 \| 5950 \| 0.0166 \| - \|
	\| 0.8163 \| 6000 \| 0.01 \| - \|
	\| 0.8231 \| 6050 \| 0.0088 \| - \|
	\| 0.8299 \| 6100 \| 0.0121 \| - \|
	\| 0.8367 \| 6150 \| 0.0214 \| - \|
	\| 0.8435 \| 6200 \| 0.009 \| - \|
	\| 0.8503 \| 6250 \| 0.0133 \| - \|
	\| 0.8571 \| 6300 \| 0.0062 \| - \|
	\| 0.8639 \| 6350 \| 0.0077 \| - \|
	\| 0.8707 \| 6400 \| 0.0201 \| - \|
	\| 0.8776 \| 6450 \| 0.0163 \| - \|
	\| 0.8844 \| 6500 \| 0.0071 \| - \|
	\| 0.8912 \| 6550 \| 0.0138 \| - \|
	\| 0.8980 \| 6600 \| 0.0131 \| - \|
	\| 0.9048 \| 6650 \| 0.0126 \| - \|
	\| 0.9116 \| 6700 \| 0.0042 \| - \|
	\| 0.9184 \| 6750 \| 0.0152 \| - \|
	\| 0.9252 \| 6800 \| 0.0194 \| - \|
	\| 0.9320 \| 6850 \| 0.0068 \| - \|
	\| 0.9388 \| 6900 \| 0.0154 \| - \|
	\| 0.9456 \| 6950 \| 0.0077 \| - \|
	\| 0.9524 \| 7000 \| 0.009 \| - \|
	\| 0.9592 \| 7050 \| 0.0053 \| - \|
	\| 0.9660 \| 7100 \| 0.0128 \| - \|
	\| 0.9728 \| 7150 \| 0.011 \| - \|
	\| 0.9796 \| 7200 \| 0.0039 \| - \|
	\| 0.9864 \| 7250 \| 0.0076 \| - \|
	\| 0.9932 \| 7300 \| 0.018 \| - \|
	\| 1.0 \| 7350 \| 0.0215 \| - \|
	\| 1.0068 \| 7400 \| 0.0022 \| - \|
	\| 1.0136 \| 7450 \| 0.01 \| - \|
	\| 1.0204 \| 7500 \| 0.0061 \| - \|
	\| 1.0272 \| 7550 \| 0.0039 \| - \|
	\| 1.0340 \| 7600 \| 0.0052 \| - \|
	\| 1.0408 \| 7650 \| 0.0053 \| - \|
	\| 1.0476 \| 7700 \| 0.0093 \| - \|
	\| 1.0544 \| 7750 \| 0.0099 \| - \|
	\| 1.0612 \| 7800 \| 0.0076 \| - \|
	\| 1.0680 \| 7850 \| 0.0094 \| - \|
	\| 1.0748 \| 7900 \| 0.0065 \| - \|
	\| 1.0816 \| 7950 \| 0.0083 \| - \|
	\| 1.0884 \| 8000 \| 0.007 \| - \|
	\| 1.0952 \| 8050 \| 0.0056 \| - \|
	\| 1.1020 \| 8100 \| 0.0112 \| - \|
	\| 1.1088 \| 8150 \| 0.0087 \| - \|
	\| 1.1156 \| 8200 \| 0.0055 \| - \|
	\| 1.1224 \| 8250 \| 0.0051 \| - \|
	\| 1.1293 \| 8300 \| 0.0096 \| - \|
	\| 1.1361 \| 8350 \| 0.0038 \| - \|
	\| 1.1429 \| 8400 \| 0.0055 \| - \|
	\| 1.1497 \| 8450 \| 0.0051 \| - \|
	\| 1.1565 \| 8500 \| 0.01 \| - \|
	\| 1.1633 \| 8550 \| 0.0058 \| - \|
	\| 1.1701 \| 8600 \| 0.0112 \| - \|
	\| 1.1769 \| 8650 \| 0.003 \| - \|
	\| 1.1837 \| 8700 \| 0.0094 \| - \|
	\| 1.1905 \| 8750 \| 0.0069 \| - \|
	\| 1.1973 \| 8800 \| 0.0131 \| - \|
	\| 1.2041 \| 8850 \| 0.0089 \| - \|
	\| 1.2109 \| 8900 \| 0.0061 \| - \|
	\| 1.2177 \| 8950 \| 0.0109 \| - \|
	\| 1.2245 \| 9000 \| 0.008 \| - \|
	\| 1.2313 \| 9050 \| 0.0122 \| - \|
	\| 1.2381 \| 9100 \| 0.0081 \| - \|
	\| 1.2449 \| 9150 \| 0.0014 \| - \|
	\| 1.2517 \| 9200 \| 0.0046 \| - \|
	\| 1.2585 \| 9250 \| 0.0049 \| - \|
	\| 1.2653 \| 9300 \| 0.0147 \| - \|
	\| 1.2721 \| 9350 \| 0.0105 \| - \|
	\| 1.2789 \| 9400 \| 0.0126 \| - \|
	\| 1.2857 \| 9450 \| 0.0031 \| - \|
	\| 1.2925 \| 9500 \| 0.0039 \| - \|
	\| 1.2993 \| 9550 \| 0.0038 \| - \|
	\| 1.3061 \| 9600 \| 0.0047 \| - \|
	\| 1.3129 \| 9650 \| 0.0037 \| - \|
	\| 1.3197 \| 9700 \| 0.0103 \| - \|
	\| 1.3265 \| 9750 \| 0.0007 \| - \|
	\| 1.3333 \| 9800 \| 0.0053 \| - \|
	\| 1.3401 \| 9850 \| 0.0018 \| - \|
	\| 1.3469 \| 9900 \| 0.0057 \| - \|
	\| 1.3537 \| 9950 \| 0.0044 \| - \|
	\| 1.3605 \| 10000 \| 0.0109 \| - \|
	\| 1.3673 \| 10050 \| 0.0056 \| - \|
	\| 1.3741 \| 10100 \| 0.0081 \| - \|
	\| 1.3810 \| 10150 \| 0.008 \| - \|
	\| 1.3878 \| 10200 \| 0.0081 \| - \|
	\| 1.3946 \| 10250 \| 0.0033 \| - \|
	\| 1.4014 \| 10300 \| 0.0055 \| - \|
	\| 1.4082 \| 10350 \| 0.0019 \| - \|
	\| 1.4150 \| 10400 \| 0.0033 \| - \|
	\| 1.4218 \| 10450 \| 0.0033 \| - \|
	\| 1.4286 \| 10500 \| 0.0058 \| - \|
	\| 1.4354 \| 10550 \| 0.0047 \| - \|
	\| 1.4422 \| 10600 \| 0.0068 \| - \|
	\| 1.4490 \| 10650 \| 0.0052 \| - \|
	\| 1.4558 \| 10700 \| 0.0033 \| - \|
	\| 1.4626 \| 10750 \| 0.001 \| - \|
	\| 1.4694 \| 10800 \| 0.0101 \| - \|
	\| 1.4762 \| 10850 \| 0.0011 \| - \|
	\| 1.4830 \| 10900 \| 0.008 \| - \|
	\| 1.4898 \| 10950 \| 0.0038 \| - \|
	\| 1.4966 \| 11000 \| 0.0033 \| - \|
	\| 1.5034 \| 11050 \| 0.0031 \| - \|
	\| 1.5102 \| 11100 \| 0.0107 \| - \|
	\| 1.5170 \| 11150 \| 0.004 \| - \|
	\| 1.5238 \| 11200 \| 0.0009 \| - \|
	\| 1.5306 \| 11250 \| 0.0034 \| - \|
	\| 1.5374 \| 11300 \| 0.0033 \| - \|
	\| 1.5442 \| 11350 \| 0.0011 \| - \|
	\| 1.5510 \| 11400 \| 0.0081 \| - \|
	\| 1.5578 \| 11450 \| 0.0025 \| - \|
	\| 1.5646 \| 11500 \| 0.0065 \| - \|
	\| 1.5714 \| 11550 \| 0.0069 \| - \|
	\| 1.5782 \| 11600 \| 0.0053 \| - \|
	\| 1.5850 \| 11650 \| 0.0031 \| - \|
	\| 1.5918 \| 11700 \| 0.0059 \| - \|
	\| 1.5986 \| 11750 \| 0.006 \| - \|
	\| 1.6054 \| 11800 \| 0.0007 \| - \|
	\| 1.6122 \| 11850 \| 0.0027 \| - \|
	\| 1.6190 \| 11900 \| 0.003 \| - \|
	\| 1.6259 \| 11950 \| 0.0052 \| - \|
	\| 1.6327 \| 12000 \| 0.0065 \| - \|
	\| 1.6395 \| 12050 \| 0.0032 \| - \|
	\| 1.6463 \| 12100 \| 0.0054 \| - \|
	\| 1.6531 \| 12150 \| 0.0063 \| - \|
	\| 1.6599 \| 12200 \| 0.0155 \| - \|
	\| 1.6667 \| 12250 \| 0.0105 \| - \|
	\| 1.6735 \| 12300 \| 0.0067 \| - \|
	\| 1.6803 \| 12350 \| 0.0034 \| - \|
	\| 1.6871 \| 12400 \| 0.0076 \| - \|
	\| 1.6939 \| 12450 \| 0.0042 \| - \|
	\| 1.7007 \| 12500 \| 0.003 \| - \|
	\| 1.7075 \| 12550 \| 0.0096 \| - \|
	\| 1.7143 \| 12600 \| 0.0054 \| - \|
	\| 1.7211 \| 12650 \| 0.005 \| - \|
	\| 1.7279 \| 12700 \| 0.0039 \| - \|
	\| 1.7347 \| 12750 \| 0.0061 \| - \|
	\| 1.7415 \| 12800 \| 0.0027 \| - \|
	\| 1.7483 \| 12850 \| 0.0033 \| - \|
	\| 1.7551 \| 12900 \| 0.0028 \| - \|
	\| 1.7619 \| 12950 \| 0.0038 \| - \|
	\| 1.7687 \| 13000 \| 0.0083 \| - \|
	\| 1.7755 \| 13050 \| 0.0074 \| - \|
	\| 1.7823 \| 13100 \| 0.0015 \| - \|
	\| 1.7891 \| 13150 \| 0.0037 \| - \|
	\| 1.7959 \| 13200 \| 0.0041 \| - \|
	\| 1.8027 \| 13250 \| 0.0007 \| - \|
	\| 1.8095 \| 13300 \| 0.0046 \| - \|
	\| 1.8163 \| 13350 \| 0.0007 \| - \|
	\| 1.8231 \| 13400 \| 0.0019 \| - \|
	\| 1.8299 \| 13450 \| 0.0051 \| - \|
	\| 1.8367 \| 13500 \| 0.0007 \| - \|
	\| 1.8435 \| 13550 \| 0.0013 \| - \|
	\| 1.8503 \| 13600 \| 0.0045 \| - \|
	\| 1.8571 \| 13650 \| 0.0006 \| - \|
	\| 1.8639 \| 13700 \| 0.0028 \| - \|
	\| 1.8707 \| 13750 \| 0.0028 \| - \|
	\| 1.8776 \| 13800 \| 0.001 \| - \|
	\| 1.8844 \| 13850 \| 0.001 \| - \|
	\| 1.8912 \| 13900 \| 0.0075 \| - \|
	\| 1.8980 \| 13950 \| 0.0041 \| - \|
	\| 1.9048 \| 14000 \| 0.0115 \| - \|
	\| 1.9116 \| 14050 \| 0.0007 \| - \|
	\| 1.9184 \| 14100 \| 0.0069 \| - \|
	\| 1.9252 \| 14150 \| 0.0017 \| - \|
	\| 1.9320 \| 14200 \| 0.005 \| - \|
	\| 1.9388 \| 14250 \| 0.0028 \| - \|
	\| 1.9456 \| 14300 \| 0.0029 \| - \|
	\| 1.9524 \| 14350 \| 0.0052 \| - \|
	\| 1.9592 \| 14400 \| 0.0023 \| - \|
	\| 1.9660 \| 14450 \| 0.0046 \| - \|
	\| 1.9728 \| 14500 \| 0.001 \| - \|
	\| 1.9796 \| 14550 \| 0.0009 \| - \|
	\| 1.9864 \| 14600 \| 0.0059 \| - \|
	\| 1.9932 \| 14650 \| 0.0075 \| - \|
	\| 2.0 \| 14700 \| 0.003 \| - \|
	\| 2.0068 \| 14750 \| 0.0088 \| - \|
	\| 2.0136 \| 14800 \| 0.0073 \| - \|
	\| 2.0204 \| 14850 \| 0.0023 \| - \|
	\| 2.0272 \| 14900 \| 0.0104 \| - \|
	\| 2.0340 \| 14950 \| 0.0024 \| - \|
	\| 2.0408 \| 15000 \| 0.0059 \| - \|
	\| 2.0476 \| 15050 \| 0.0041 \| - \|
	\| 2.0544 \| 15100 \| 0.0079 \| - \|
	\| 2.0612 \| 15150 \| 0.0011 \| - \|
	\| 2.0680 \| 15200 \| 0.0038 \| - \|
	\| 2.0748 \| 15250 \| 0.0009 \| - \|
	\| 2.0816 \| 15300 \| 0.0057 \| - \|
	\| 2.0884 \| 15350 \| 0.0025 \| - \|
	\| 2.0952 \| 15400 \| 0.0033 \| - \|
	\| 2.1020 \| 15450 \| 0.0093 \| - \|
	\| 2.1088 \| 15500 \| 0.0006 \| - \|
	\| 2.1156 \| 15550 \| 0.0024 \| - \|
	\| 2.1224 \| 15600 \| 0.0044 \| - \|
	\| 2.1293 \| 15650 \| 0.0069 \| - \|
	\| 2.1361 \| 15700 \| 0.0051 \| - \|
	\| 2.1429 \| 15750 \| 0.008 \| - \|
	\| 2.1497 \| 15800 \| 0.0047 \| - \|
	\| 2.1565 \| 15850 \| 0.0012 \| - \|
	\| 2.1633 \| 15900 \| 0.001 \| - \|
	\| 2.1701 \| 15950 \| 0.0019 \| - \|
	\| 2.1769 \| 16000 \| 0.0024 \| - \|
	\| 2.1837 \| 16050 \| 0.0066 \| - \|
	\| 2.1905 \| 16100 \| 0.0025 \| - \|
	\| 2.1973 \| 16150 \| 0.0037 \| - \|
	\| 2.2041 \| 16200 \| 0.0033 \| - \|
	\| 2.2109 \| 16250 \| 0.0023 \| - \|
	\| 2.2177 \| 16300 \| 0.0013 \| - \|
	\| 2.2245 \| 16350 \| 0.0033 \| - \|
	\| 2.2313 \| 16400 \| 0.0029 \| - \|
	\| 2.2381 \| 16450 \| 0.0038 \| - \|
	\| 2.2449 \| 16500 \| 0.0015 \| - \|
	\| 2.2517 \| 16550 \| 0.0007 \| - \|
	\| 2.2585 \| 16600 \| 0.0031 \| - \|
	\| 2.2653 \| 16650 \| 0.0061 \| - \|
	\| 2.2721 \| 16700 \| 0.0011 \| - \|
	\| 2.2789 \| 16750 \| 0.0049 \| - \|
	\| 2.2857 \| 16800 \| 0.0012 \| - \|
	\| 2.2925 \| 16850 \| 0.0036 \| - \|
	\| 2.2993 \| 16900 \| 0.004 \| - \|
	\| 2.3061 \| 16950 \| 0.0005 \| - \|
	\| 2.3129 \| 17000 \| 0.0019 \| - \|
	\| 2.3197 \| 17050 \| 0.003 \| - \|
	\| 2.3265 \| 17100 \| 0.0006 \| - \|
	\| 2.3333 \| 17150 \| 0.0009 \| - \|
	\| 2.3401 \| 17200 \| 0.0013 \| - \|
	\| 2.3469 \| 17250 \| 0.0018 \| - \|
	\| 2.3537 \| 17300 \| 0.0007 \| - \|
	\| 2.3605 \| 17350 \| 0.001 \| - \|
	\| 2.3673 \| 17400 \| 0.0054 \| - \|
	\| 2.3741 \| 17450 \| 0.0004 \| - \|
	\| 2.3810 \| 17500 \| 0.0028 \| - \|
	\| 2.3878 \| 17550 \| 0.0005 \| - \|
	\| 2.3946 \| 17600 \| 0.0003 \| - \|
	\| 2.4014 \| 17650 \| 0.0004 \| - \|
	\| 2.4082 \| 17700 \| 0.0031 \| - \|
	\| 2.4150 \| 17750 \| 0.0004 \| - \|
	\| 2.4218 \| 17800 \| 0.0013 \| - \|
	\| 2.4286 \| 17850 \| 0.0017 \| - \|
	\| 2.4354 \| 17900 \| 0.0013 \| - \|
	\| 2.4422 \| 17950 \| 0.0025 \| - \|
	\| 2.4490 \| 18000 \| 0.0004 \| - \|
	\| 2.4558 \| 18050 \| 0.0029 \| - \|
	\| 2.4626 \| 18100 \| 0.0023 \| - \|
	\| 2.4694 \| 18150 \| 0.0027 \| - \|
	\| 2.4762 \| 18200 \| 0.0015 \| - \|
	\| 2.4830 \| 18250 \| 0.0006 \| - \|
	\| 2.4898 \| 18300 \| 0.0024 \| - \|
	\| 2.4966 \| 18350 \| 0.0021 \| - \|
	\| 2.5034 \| 18400 \| 0.0005 \| - \|
	\| 2.5102 \| 18450 \| 0.0004 \| - \|
	\| 2.5170 \| 18500 \| 0.0042 \| - \|
	\| 2.5238 \| 18550 \| 0.0005 \| - \|
	\| 2.5306 \| 18600 \| 0.0012 \| - \|
	\| 2.5374 \| 18650 \| 0.005 \| - \|
	\| 2.5442 \| 18700 \| 0.0032 \| - \|
	\| 2.5510 \| 18750 \| 0.0079 \| - \|
	\| 2.5578 \| 18800 \| 0.001 \| - \|
	\| 2.5646 \| 18850 \| 0.0008 \| - \|
	\| 2.5714 \| 18900 \| 0.0042 \| - \|
	\| 2.5782 \| 18950 \| 0.001 \| - \|
	\| 2.5850 \| 19000 \| 0.001 \| - \|
	\| 2.5918 \| 19050 \| 0.0009 \| - \|
	\| 2.5986 \| 19100 \| 0.0003 \| - \|
	\| 2.6054 \| 19150 \| 0.0003 \| - \|
	\| 2.6122 \| 19200 \| 0.0003 \| - \|
	\| 2.6190 \| 19250 \| 0.0035 \| - \|
	\| 2.6259 \| 19300 \| 0.0006 \| - \|
	\| 2.6327 \| 19350 \| 0.0035 \| - \|
	\| 2.6395 \| 19400 \| 0.0003 \| - \|
	\| 2.6463 \| 19450 \| 0.0021 \| - \|
	\| 2.6531 \| 19500 \| 0.0005 \| - \|
	\| 2.6599 \| 19550 \| 0.004 \| - \|
	\| 2.6667 \| 19600 \| 0.0023 \| - \|
	\| 2.6735 \| 19650 \| 0.0006 \| - \|
	\| 2.6803 \| 19700 \| 0.004 \| - \|
	\| 2.6871 \| 19750 \| 0.0015 \| - \|
	\| 2.6939 \| 19800 \| 0.0008 \| - \|
	\| 2.7007 \| 19850 \| 0.0022 \| - \|
	\| 2.7075 \| 19900 \| 0.001 \| - \|
	\| 2.7143 \| 19950 \| 0.0007 \| - \|
	\| 2.7211 \| 20000 \| 0.0013 \| - \|
	\| 2.7279 \| 20050 \| 0.0004 \| - \|
	\| 2.7347 \| 20100 \| 0.001 \| - \|
	\| 2.7415 \| 20150 \| 0.0013 \| - \|
	\| 2.7483 \| 20200 \| 0.0004 \| - \|
	\| 2.7551 \| 20250 \| 0.0035 \| - \|
	\| 2.7619 \| 20300 \| 0.0006 \| - \|
	\| 2.7687 \| 20350 \| 0.001 \| - \|
	\| 2.7755 \| 20400 \| 0.0003 \| - \|
	\| 2.7823 \| 20450 \| 0.0006 \| - \|
	\| 2.7891 \| 20500 \| 0.0012 \| - \|
	\| 2.7959 \| 20550 \| 0.0003 \| - \|
	\| 2.8027 \| 20600 \| 0.0031 \| - \|
	\| 2.8095 \| 20650 \| 0.0005 \| - \|
	\| 2.8163 \| 20700 \| 0.0008 \| - \|
	\| 2.8231 \| 20750 \| 0.0006 \| - \|
	\| 2.8299 \| 20800 \| 0.0005 \| - \|
	\| 2.8367 \| 20850 \| 0.0004 \| - \|
	\| 2.8435 \| 20900 \| 0.0002 \| - \|
	\| 2.8503 \| 20950 \| 0.0011 \| - \|
	\| 2.8571 \| 21000 \| 0.0002 \| - \|
	\| 2.8639 \| 21050 \| 0.0033 \| - \|
	\| 2.8707 \| 21100 \| 0.0024 \| - \|
	\| 2.8776 \| 21150 \| 0.0004 \| - \|
	\| 2.8844 \| 21200 \| 0.0002 \| - \|
	\| 2.8912 \| 21250 \| 0.0029 \| - \|
	\| 2.8980 \| 21300 \| 0.0004 \| - \|
	\| 2.9048 \| 21350 \| 0.0003 \| - \|
	\| 2.9116 \| 21400 \| 0.0024 \| - \|
	\| 2.9184 \| 21450 \| 0.0027 \| - \|
	\| 2.9252 \| 21500 \| 0.0003 \| - \|
	\| 2.9320 \| 21550 \| 0.0006 \| - \|
	\| 2.9388 \| 21600 \| 0.0002 \| - \|
	\| 2.9456 \| 21650 \| 0.0021 \| - \|
	\| 2.9524 \| 21700 \| 0.0011 \| - \|
	\| 2.9592 \| 21750 \| 0.0006 \| - \|
	\| 2.9660 \| 21800 \| 0.0002 \| - \|
	\| 2.9728 \| 21850 \| 0.0004 \| - \|
	\| 2.9796 \| 21900 \| 0.0008 \| - \|
	\| 2.9864 \| 21950 \| 0.0028 \| - \|
	\| 2.9932 \| 22000 \| 0.0004 \| - \|
	\| 3.0 \| 22050 \| 0.0002 \| - \|
	\| 3.0068 \| 22100 \| 0.0002 \| - \|
	\| 3.0136 \| 22150 \| 0.0026 \| - \|
	\| 3.0204 \| 22200 \| 0.0002 \| - \|
	\| 3.0272 \| 22250 \| 0.0004 \| - \|
	\| 3.0340 \| 22300 \| 0.0005 \| - \|
	\| 3.0408 \| 22350 \| 0.0005 \| - \|
	\| 3.0476 \| 22400 \| 0.0022 \| - \|
	\| 3.0544 \| 22450 \| 0.0006 \| - \|
	\| 3.0612 \| 22500 \| 0.0009 \| - \|
	\| 3.0680 \| 22550 \| 0.0004 \| - \|
	\| 3.0748 \| 22600 \| 0.0002 \| - \|
	\| 3.0816 \| 22650 \| 0.0003 \| - \|
	\| 3.0884 \| 22700 \| 0.0002 \| - \|
	\| 3.0952 \| 22750 \| 0.0002 \| - \|
	\| 3.1020 \| 22800 \| 0.0002 \| - \|
	\| 3.1088 \| 22850 \| 0.0041 \| - \|
	\| 3.1156 \| 22900 \| 0.0014 \| - \|
	\| 3.1224 \| 22950 \| 0.0019 \| - \|
	\| 3.1293 \| 23000 \| 0.0023 \| - \|
	\| 3.1361 \| 23050 \| 0.0003 \| - \|
	\| 3.1429 \| 23100 \| 0.0005 \| - \|
	\| 3.1497 \| 23150 \| 0.0003 \| - \|
	\| 3.1565 \| 23200 \| 0.0009 \| - \|
	\| 3.1633 \| 23250 \| 0.0023 \| - \|
	\| 3.1701 \| 23300 \| 0.0003 \| - \|
	\| 3.1769 \| 23350 \| 0.0002 \| - \|
	\| 3.1837 \| 23400 \| 0.0003 \| - \|
	\| 3.1905 \| 23450 \| 0.0003 \| - \|
	\| 3.1973 \| 23500 \| 0.0015 \| - \|
	\| 3.2041 \| 23550 \| 0.0002 \| - \|
	\| 3.2109 \| 23600 \| 0.0004 \| - \|
	\| 3.2177 \| 23650 \| 0.0004 \| - \|
	\| 3.2245 \| 23700 \| 0.0009 \| - \|
	\| 3.2313 \| 23750 \| 0.0002 \| - \|
	\| 3.2381 \| 23800 \| 0.0003 \| - \|
	\| 3.2449 \| 23850 \| 0.0002 \| - \|
	\| 3.2517 \| 23900 \| 0.0001 \| - \|
	\| 3.2585 \| 23950 \| 0.0003 \| - \|
	\| 3.2653 \| 24000 \| 0.0002 \| - \|
	\| 3.2721 \| 24050 \| 0.0019 \| - \|
	\| 3.2789 \| 24100 \| 0.0002 \| - \|
	\| 3.2857 \| 24150 \| 0.0002 \| - \|
	\| 3.2925 \| 24200 \| 0.0002 \| - \|
	\| 3.2993 \| 24250 \| 0.0002 \| - \|
	\| 3.3061 \| 24300 \| 0.0003 \| - \|
	\| 3.3129 \| 24350 \| 0.0007 \| - \|
	\| 3.3197 \| 24400 \| 0.0009 \| - \|
	\| 3.3265 \| 24450 \| 0.0006 \| - \|
	\| 3.3333 \| 24500 \| 0.0003 \| - \|
	\| 3.3401 \| 24550 \| 0.0008 \| - \|
	\| 3.3469 \| 24600 \| 0.0007 \| - \|
	\| 3.3537 \| 24650 \| 0.0003 \| - \|
	\| 3.3605 \| 24700 \| 0.0002 \| - \|
	\| 3.3673 \| 24750 \| 0.0001 \| - \|
	\| 3.3741 \| 24800 \| 0.0001 \| - \|
	\| 3.3810 \| 24850 \| 0.0002 \| - \|
	\| 3.3878 \| 24900 \| 0.0009 \| - \|
	\| 3.3946 \| 24950 \| 0.0005 \| - \|
	\| 3.4014 \| 25000 \| 0.0001 \| - \|
	\| 3.4082 \| 25050 \| 0.0003 \| - \|
	\| 3.4150 \| 25100 \| 0.0001 \| - \|
	\| 3.4218 \| 25150 \| 0.0002 \| - \|
	\| 3.4286 \| 25200 \| 0.0002 \| - \|
	\| 3.4354 \| 25250 \| 0.0003 \| - \|
	\| 3.4422 \| 25300 \| 0.0002 \| - \|
	\| 3.4490 \| 25350 \| 0.0004 \| - \|
	\| 3.4558 \| 25400 \| 0.0005 \| - \|
	\| 3.4626 \| 25450 \| 0.0005 \| - \|
	\| 3.4694 \| 25500 \| 0.0002 \| - \|
	\| 3.4762 \| 25550 \| 0.0003 \| - \|
	\| 3.4830 \| 25600 \| 0.0001 \| - \|
	\| 3.4898 \| 25650 \| 0.0003 \| - \|
	\| 3.4966 \| 25700 \| 0.0006 \| - \|
	\| 3.5034 \| 25750 \| 0.0002 \| - \|
	\| 3.5102 \| 25800 \| 0.0003 \| - \|
	\| 3.5170 \| 25850 \| 0.0005 \| - \|
	\| 3.5238 \| 25900 \| 0.0002 \| - \|
	\| 3.5306 \| 25950 \| 0.0003 \| - \|
	\| 3.5374 \| 26000 \| 0.0002 \| - \|
	\| 3.5442 \| 26050 \| 0.0004 \| - \|
	\| 3.5510 \| 26100 \| 0.0001 \| - \|
	\| 3.5578 \| 26150 \| 0.0001 \| - \|
	\| 3.5646 \| 26200 \| 0.0002 \| - \|
	\| 3.5714 \| 26250 \| 0.0001 \| - \|
	\| 3.5782 \| 26300 \| 0.0005 \| - \|
	\| 3.5850 \| 26350 \| 0.0002 \| - \|
	\| 3.5918 \| 26400 \| 0.0001 \| - \|
	\| 3.5986 \| 26450 \| 0.0001 \| - \|
	\| 3.6054 \| 26500 \| 0.0003 \| - \|
	\| 3.6122 \| 26550 \| 0.0002 \| - \|
	\| 3.6190 \| 26600 \| 0.0002 \| - \|
	\| 3.6259 \| 26650 \| 0.0001 \| - \|
	\| 3.6327 \| 26700 \| 0.0001 \| - \|
	\| 3.6395 \| 26750 \| 0.0001 \| - \|
	\| 3.6463 \| 26800 \| 0.0005 \| - \|
	\| 3.6531 \| 26850 \| 0.0001 \| - \|
	\| 3.6599 \| 26900 \| 0.0002 \| - \|
	\| 3.6667 \| 26950 \| 0.0001 \| - \|
	\| 3.6735 \| 27000 \| 0.0001 \| - \|
	\| 3.6803 \| 27050 \| 0.0002 \| - \|
	\| 3.6871 \| 27100 \| 0.0002 \| - \|
	\| 3.6939 \| 27150 \| 0.0001 \| - \|
	\| 3.7007 \| 27200 \| 0.0001 \| - \|
	\| 3.7075 \| 27250 \| 0.0002 \| - \|
	\| 3.7143 \| 27300 \| 0.0002 \| - \|
	\| 3.7211 \| 27350 \| 0.0001 \| - \|
	\| 3.7279 \| 27400 \| 0.0008 \| - \|
	\| 3.7347 \| 27450 \| 0.0002 \| - \|
	\| 3.7415 \| 27500 \| 0.0008 \| - \|
	\| 3.7483 \| 27550 \| 0.0005 \| - \|
	\| 3.7551 \| 27600 \| 0.0002 \| - \|
	\| 3.7619 \| 27650 \| 0.0003 \| - \|
	\| 3.7687 \| 27700 \| 0.0002 \| - \|
	\| 3.7755 \| 27750 \| 0.0007 \| - \|
	\| 3.7823 \| 27800 \| 0.0003 \| - \|
	\| 3.7891 \| 27850 \| 0.0001 \| - \|
	\| 3.7959 \| 27900 \| 0.0006 \| - \|
	\| 3.8027 \| 27950 \| 0.0002 \| - \|
	\| 3.8095 \| 28000 \| 0.0001 \| - \|
	\| 3.8163 \| 28050 \| 0.0001 \| - \|
	\| 3.8231 \| 28100 \| 0.0002 \| - \|
	\| 3.8299 \| 28150 \| 0.0001 \| - \|
	\| 3.8367 \| 28200 \| 0.0001 \| - \|
	\| 3.8435 \| 28250 \| 0.0004 \| - \|
	\| 3.8503 \| 28300 \| 0.0001 \| - \|
	\| 3.8571 \| 28350 \| 0.0001 \| - \|
	\| 3.8639 \| 28400 \| 0.0001 \| - \|
	\| 3.8707 \| 28450 \| 0.0005 \| - \|
	\| 3.8776 \| 28500 \| 0.0004 \| - \|
	\| 3.8844 \| 28550 \| 0.0001 \| - \|
	\| 3.8912 \| 28600 \| 0.0002 \| - \|
	\| 3.8980 \| 28650 \| 0.0002 \| - \|
	\| 3.9048 \| 28700 \| 0.0003 \| - \|
	\| 3.9116 \| 28750 \| 0.0001 \| - \|
	\| 3.9184 \| 28800 \| 0.0002 \| - \|
	\| 3.9252 \| 28850 \| 0.0001 \| - \|
	\| 3.9320 \| 28900 \| 0.0001 \| - \|
	\| 3.9388 \| 28950 \| 0.0002 \| - \|
	\| 3.9456 \| 29000 \| 0.0002 \| - \|
	\| 3.9524 \| 29050 \| 0.0001 \| - \|
	\| 3.9592 \| 29100 \| 0.0001 \| - \|
	\| 3.9660 \| 29150 \| 0.0002 \| - \|
	\| 3.9728 \| 29200 \| 0.0002 \| - \|
	\| 3.9796 \| 29250 \| 0.0003 \| - \|
	\| 3.9864 \| 29300 \| 0.0001 \| - \|
	\| 3.9932 \| 29350 \| 0.0007 \| - \|
	\| 4.0 \| 29400 \| 0.0007 \| - \|

	### Framework Versions
	- Python: 3.11.13
	- SetFit: 1.1.2
	- Sentence Transformers: 4.1.0
	- Transformers: 4.52.4
	- PyTorch: 2.6.0+cu124
	- Datasets: 3.6.0
	- Tokenizers: 0.21.2

	## 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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->