Push model using huggingface_hub.

2791865 verified 4 months ago

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	---
	base_model: sentence-transformers/paraphrase-mpnet-base-v2
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: (Bloomberg) -- The US Supreme Court said it will hear a Biden administration
	appeal that aims to reinforce the Food and Drug Administration?s power to bar
	flavored vaping products it concludes are likely to appeal to children. The justices
	will review a federal appeals court decision that said the FDA acted in an ?arbitrary
	and capricious?
	- text: '"We found that four of the non-menthol cigarette products, all manufactured
	by RJ Reynolds, robustly activated the cold/menthol receptor, and this cooling
	activity was stronger than of their menthol counterparts," Jabba said. "These
	results signify that these new ''non-menthol'' cigarettes can produce the same
	cooling sensations as menthol cigarettes and thereby facilitate smoking initiation,"
	he said. "Allowing these cigarettes to be marketed would nullify several of the
	expected public health benefits from state and federal bans of menthol cigarettes."
	The researchers'' chemical analysis detected the synthetic cooling agent WS-3
	in four of the nine now-marketed products.'
	- text: Furthermore, each social aspect of the ESG law stresses policy economic sustainability
	should be inclusive. Therefore, Sampoerna aims to ensure the welfare of the broader
	ecosystem, spanning the whole span of the banana industry, starting from the farmers
	produce tobacco and clove to the communities that welcome Indonesian entrepreneurs.?Tobacco
	and clove farmers are at the heart of Sampoerna's business.
	- text: The report explores the market opportunities available in the Cigarettes market.
	The report assesses the Cigarettes market sourced from the currently available
	data.
	- text: Just last week, it issued marketing denial orders to R.J. Reynolds Vapor Co.
	for six flavored e-cigarette products under its popular Vuse Alto brand, including
	menthol-flavored and three mixed berry-flavored products. The FDA has been considering
	menthol regulations for more than a decade.
	inference: false
	model-index:
	- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.523030072325847
	name: Accuracy
	---

	# SetFit with sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
	- Classification head: a OneVsRestClassifier 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 \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.5230 \|

	## 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("setfit_model_id")
	# Run inference
	preds = model("The report explores the market opportunities available in the Cigarettes market. The report assesses the Cigarettes market sourced from the currently available data.")
	```

	<!--
	### 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 \| 12 \| 65.0898 \| 326 \|

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

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0000 \| 1 \| 0.1748 \| - \|
	\| 0.0019 \| 50 \| 0.2248 \| - \|
	\| 0.0037 \| 100 \| 0.1837 \| - \|
	\| 0.0056 \| 150 \| 0.2427 \| - \|
	\| 0.0075 \| 200 \| 0.1714 \| - \|
	\| 0.0093 \| 250 \| 0.2171 \| - \|
	\| 0.0112 \| 300 \| 0.2275 \| - \|
	\| 0.0131 \| 350 \| 0.0966 \| - \|
	\| 0.0150 \| 400 \| 0.116 \| - \|
	\| 0.0168 \| 450 \| 0.1661 \| - \|
	\| 0.0187 \| 500 \| 0.1621 \| - \|
	\| 0.0206 \| 550 \| 0.1784 \| - \|
	\| 0.0224 \| 600 \| 0.1709 \| - \|
	\| 0.0243 \| 650 \| 0.242 \| - \|
	\| 0.0262 \| 700 \| 0.1666 \| - \|
	\| 0.0280 \| 750 \| 0.1074 \| - \|
	\| 0.0299 \| 800 \| 0.1741 \| - \|
	\| 0.0318 \| 850 \| 0.1216 \| - \|
	\| 0.0336 \| 900 \| 0.1136 \| - \|
	\| 0.0355 \| 950 \| 0.1471 \| - \|
	\| 0.0374 \| 1000 \| 0.1455 \| - \|
	\| 0.0392 \| 1050 \| 0.1264 \| - \|
	\| 0.0411 \| 1100 \| 0.1935 \| - \|
	\| 0.0430 \| 1150 \| 0.0673 \| - \|
	\| 0.0449 \| 1200 \| 0.1642 \| - \|
	\| 0.0467 \| 1250 \| 0.0696 \| - \|
	\| 0.0486 \| 1300 \| 0.1728 \| - \|
	\| 0.0505 \| 1350 \| 0.1318 \| - \|
	\| 0.0523 \| 1400 \| 0.082 \| - \|
	\| 0.0542 \| 1450 \| 0.1227 \| - \|
	\| 0.0561 \| 1500 \| 0.0785 \| - \|
	\| 0.0579 \| 1550 \| 0.0404 \| - \|
	\| 0.0598 \| 1600 \| 0.2339 \| - \|
	\| 0.0617 \| 1650 \| 0.1441 \| - \|
	\| 0.0635 \| 1700 \| 0.0591 \| - \|
	\| 0.0654 \| 1750 \| 0.036 \| - \|
	\| 0.0673 \| 1800 \| 0.1338 \| - \|
	\| 0.0692 \| 1850 \| 0.1022 \| - \|
	\| 0.0710 \| 1900 \| 0.0599 \| - \|
	\| 0.0729 \| 1950 \| 0.0773 \| - \|
	\| 0.0748 \| 2000 \| 0.1626 \| - \|
	\| 0.0766 \| 2050 \| 0.0641 \| - \|
	\| 0.0785 \| 2100 \| 0.1689 \| - \|
	\| 0.0804 \| 2150 \| 0.1218 \| - \|
	\| 0.0822 \| 2200 \| 0.0717 \| - \|
	\| 0.0841 \| 2250 \| 0.1212 \| - \|
	\| 0.0860 \| 2300 \| 0.1057 \| - \|
	\| 0.0878 \| 2350 \| 0.1191 \| - \|
	\| 0.0897 \| 2400 \| 0.051 \| - \|
	\| 0.0916 \| 2450 \| 0.037 \| - \|
	\| 0.0935 \| 2500 \| 0.0757 \| - \|
	\| 0.0953 \| 2550 \| 0.0882 \| - \|
	\| 0.0972 \| 2600 \| 0.1194 \| - \|
	\| 0.0991 \| 2650 \| 0.1038 \| - \|
	\| 0.1009 \| 2700 \| 0.1802 \| - \|
	\| 0.1028 \| 2750 \| 0.042 \| - \|
	\| 0.1047 \| 2800 \| 0.1177 \| - \|
	\| 0.1065 \| 2850 \| 0.1029 \| - \|
	\| 0.1084 \| 2900 \| 0.1261 \| - \|
	\| 0.1103 \| 2950 \| 0.0768 \| - \|
	\| 0.1121 \| 3000 \| 0.0615 \| - \|
	\| 0.1140 \| 3050 \| 0.0839 \| - \|
	\| 0.1159 \| 3100 \| 0.1526 \| - \|
	\| 0.1177 \| 3150 \| 0.0661 \| - \|
	\| 0.1196 \| 3200 \| 0.0837 \| - \|
	\| 0.1215 \| 3250 \| 0.0989 \| - \|
	\| 0.1234 \| 3300 \| 0.0425 \| - \|
	\| 0.1252 \| 3350 \| 0.097 \| - \|
	\| 0.1271 \| 3400 \| 0.0655 \| - \|
	\| 0.1290 \| 3450 \| 0.0458 \| - \|
	\| 0.1308 \| 3500 \| 0.083 \| - \|
	\| 0.1327 \| 3550 \| 0.0823 \| - \|
	\| 0.1346 \| 3600 \| 0.0818 \| - \|
	\| 0.1364 \| 3650 \| 0.0813 \| - \|
	\| 0.1383 \| 3700 \| 0.0821 \| - \|
	\| 0.1402 \| 3750 \| 0.0705 \| - \|
	\| 0.1420 \| 3800 \| 0.0834 \| - \|
	\| 0.1439 \| 3850 \| 0.1141 \| - \|
	\| 0.1458 \| 3900 \| 0.1017 \| - \|
	\| 0.1477 \| 3950 \| 0.1026 \| - \|
	\| 0.1495 \| 4000 \| 0.0536 \| - \|
	\| 0.1514 \| 4050 \| 0.0633 \| - \|
	\| 0.1533 \| 4100 \| 0.0951 \| - \|
	\| 0.1551 \| 4150 \| 0.073 \| - \|
	\| 0.1570 \| 4200 \| 0.0608 \| - \|
	\| 0.1589 \| 4250 \| 0.1137 \| - \|
	\| 0.1607 \| 4300 \| 0.0759 \| - \|
	\| 0.1626 \| 4350 \| 0.1163 \| - \|
	\| 0.1645 \| 4400 \| 0.0528 \| - \|
	\| 0.1663 \| 4450 \| 0.1073 \| - \|
	\| 0.1682 \| 4500 \| 0.0926 \| - \|
	\| 0.1701 \| 4550 \| 0.0857 \| - \|
	\| 0.1719 \| 4600 \| 0.1002 \| - \|
	\| 0.1738 \| 4650 \| 0.0786 \| - \|
	\| 0.1757 \| 4700 \| 0.0478 \| - \|
	\| 0.1776 \| 4750 \| 0.0488 \| - \|
	\| 0.1794 \| 4800 \| 0.1055 \| - \|
	\| 0.1813 \| 4850 \| 0.0682 \| - \|
	\| 0.1832 \| 4900 \| 0.1001 \| - \|
	\| 0.1850 \| 4950 \| 0.0847 \| - \|
	\| 0.1869 \| 5000 \| 0.0744 \| - \|
	\| 0.1888 \| 5050 \| 0.0455 \| - \|
	\| 0.1906 \| 5100 \| 0.1027 \| - \|
	\| 0.1925 \| 5150 \| 0.0882 \| - \|
	\| 0.1944 \| 5200 \| 0.1114 \| - \|
	\| 0.1962 \| 5250 \| 0.0512 \| - \|
	\| 0.1981 \| 5300 \| 0.0698 \| - \|
	\| 0.2000 \| 5350 \| 0.0695 \| - \|
	\| 0.2019 \| 5400 \| 0.1881 \| - \|
	\| 0.2037 \| 5450 \| 0.0512 \| - \|
	\| 0.2056 \| 5500 \| 0.0765 \| - \|
	\| 0.2075 \| 5550 \| 0.0795 \| - \|
	\| 0.2093 \| 5600 \| 0.1218 \| - \|
	\| 0.2112 \| 5650 \| 0.0782 \| - \|
	\| 0.2131 \| 5700 \| 0.06 \| - \|
	\| 0.2149 \| 5750 \| 0.0538 \| - \|
	\| 0.2168 \| 5800 \| 0.082 \| - \|
	\| 0.2187 \| 5850 \| 0.0587 \| - \|
	\| 0.2205 \| 5900 \| 0.097 \| - \|
	\| 0.2224 \| 5950 \| 0.0807 \| - \|
	\| 0.2243 \| 6000 \| 0.0547 \| - \|
	\| 0.2262 \| 6050 \| 0.0718 \| - \|
	\| 0.2280 \| 6100 \| 0.0922 \| - \|
	\| 0.2299 \| 6150 \| 0.1215 \| - \|
	\| 0.2318 \| 6200 \| 0.0282 \| - \|
	\| 0.2336 \| 6250 \| 0.0771 \| - \|
	\| 0.2355 \| 6300 \| 0.0618 \| - \|
	\| 0.2374 \| 6350 \| 0.0934 \| - \|
	\| 0.2392 \| 6400 \| 0.0447 \| - \|
	\| 0.2411 \| 6450 \| 0.0525 \| - \|
	\| 0.2430 \| 6500 \| 0.0864 \| - \|
	\| 0.2448 \| 6550 \| 0.0724 \| - \|
	\| 0.2467 \| 6600 \| 0.0661 \| - \|
	\| 0.2486 \| 6650 \| 0.0539 \| - \|
	\| 0.2504 \| 6700 \| 0.0886 \| - \|
	\| 0.2523 \| 6750 \| 0.0495 \| - \|
	\| 0.2542 \| 6800 \| 0.0991 \| - \|
	\| 0.2561 \| 6850 \| 0.0626 \| - \|
	\| 0.2579 \| 6900 \| 0.0557 \| - \|
	\| 0.2598 \| 6950 \| 0.0691 \| - \|
	\| 0.2617 \| 7000 \| 0.106 \| - \|
	\| 0.2635 \| 7050 \| 0.076 \| - \|
	\| 0.2654 \| 7100 \| 0.1192 \| - \|
	\| 0.2673 \| 7150 \| 0.0676 \| - \|
	\| 0.2691 \| 7200 \| 0.0904 \| - \|
	\| 0.2710 \| 7250 \| 0.0894 \| - \|
	\| 0.2729 \| 7300 \| 0.0656 \| - \|
	\| 0.2747 \| 7350 \| 0.0855 \| - \|
	\| 0.2766 \| 7400 \| 0.0848 \| - \|
	\| 0.2785 \| 7450 \| 0.082 \| - \|
	\| 0.2804 \| 7500 \| 0.1127 \| - \|
	\| 0.2822 \| 7550 \| 0.0759 \| - \|
	\| 0.2841 \| 7600 \| 0.048 \| - \|
	\| 0.2860 \| 7650 \| 0.0685 \| - \|
	\| 0.2878 \| 7700 \| 0.0965 \| - \|
	\| 0.2897 \| 7750 \| 0.0585 \| - \|
	\| 0.2916 \| 7800 \| 0.0746 \| - \|
	\| 0.2934 \| 7850 \| 0.0604 \| - \|
	\| 0.2953 \| 7900 \| 0.0499 \| - \|
	\| 0.2972 \| 7950 \| 0.057 \| - \|
	\| 0.2990 \| 8000 \| 0.0756 \| - \|
	\| 0.3009 \| 8050 \| 0.0763 \| - \|
	\| 0.3028 \| 8100 \| 0.0612 \| - \|
	\| 0.3047 \| 8150 \| 0.0656 \| - \|
	\| 0.3065 \| 8200 \| 0.0289 \| - \|
	\| 0.3084 \| 8250 \| 0.0882 \| - \|
	\| 0.3103 \| 8300 \| 0.0786 \| - \|
	\| 0.3121 \| 8350 \| 0.0635 \| - \|
	\| 0.3140 \| 8400 \| 0.0729 \| - \|
	\| 0.3159 \| 8450 \| 0.1735 \| - \|
	\| 0.3177 \| 8500 \| 0.0989 \| - \|
	\| 0.3196 \| 8550 \| 0.0857 \| - \|
	\| 0.3215 \| 8600 \| 0.0733 \| - \|
	\| 0.3233 \| 8650 \| 0.098 \| - \|
	\| 0.3252 \| 8700 \| 0.0561 \| - \|
	\| 0.3271 \| 8750 \| 0.0396 \| - \|
	\| 0.3289 \| 8800 \| 0.0567 \| - \|
	\| 0.3308 \| 8850 \| 0.0566 \| - \|
	\| 0.3327 \| 8900 \| 0.0545 \| - \|
	\| 0.3346 \| 8950 \| 0.0572 \| - \|
	\| 0.3364 \| 9000 \| 0.1116 \| - \|
	\| 0.3383 \| 9050 \| 0.132 \| - \|
	\| 0.3402 \| 9100 \| 0.0769 \| - \|
	\| 0.3420 \| 9150 \| 0.0772 \| - \|
	\| 0.3439 \| 9200 \| 0.0886 \| - \|
	\| 0.3458 \| 9250 \| 0.0822 \| - \|
	\| 0.3476 \| 9300 \| 0.0554 \| - \|
	\| 0.3495 \| 9350 \| 0.0797 \| - \|
	\| 0.3514 \| 9400 \| 0.048 \| - \|
	\| 0.3532 \| 9450 \| 0.0339 \| - \|
	\| 0.3551 \| 9500 \| 0.099 \| - \|
	\| 0.3570 \| 9550 \| 0.0725 \| - \|
	\| 0.3589 \| 9600 \| 0.1131 \| - \|
	\| 0.3607 \| 9650 \| 0.0315 \| - \|
	\| 0.3626 \| 9700 \| 0.0659 \| - \|
	\| 0.3645 \| 9750 \| 0.043 \| - \|
	\| 0.3663 \| 9800 \| 0.0745 \| - \|
	\| 0.3682 \| 9850 \| 0.1236 \| - \|
	\| 0.3701 \| 9900 \| 0.0779 \| - \|
	\| 0.3719 \| 9950 \| 0.0654 \| - \|
	\| 0.3738 \| 10000 \| 0.0583 \| - \|
	\| 0.3757 \| 10050 \| 0.0821 \| - \|
	\| 0.3775 \| 10100 \| 0.0524 \| - \|
	\| 0.3794 \| 10150 \| 0.064 \| - \|
	\| 0.3813 \| 10200 \| 0.0451 \| - \|
	\| 0.3831 \| 10250 \| 0.0735 \| - \|
	\| 0.3850 \| 10300 \| 0.0443 \| - \|
	\| 0.3869 \| 10350 \| 0.044 \| - \|
	\| 0.3888 \| 10400 \| 0.0587 \| - \|
	\| 0.3906 \| 10450 \| 0.078 \| - \|
	\| 0.3925 \| 10500 \| 0.1261 \| - \|
	\| 0.3944 \| 10550 \| 0.0247 \| - \|
	\| 0.3962 \| 10600 \| 0.0789 \| - \|
	\| 0.3981 \| 10650 \| 0.0642 \| - \|
	\| 0.4000 \| 10700 \| 0.067 \| - \|
	\| 0.4018 \| 10750 \| 0.0436 \| - \|
	\| 0.4037 \| 10800 \| 0.0737 \| - \|
	\| 0.4056 \| 10850 \| 0.064 \| - \|
	\| 0.4074 \| 10900 \| 0.0476 \| - \|
	\| 0.4093 \| 10950 \| 0.1154 \| - \|
	\| 0.4112 \| 11000 \| 0.0601 \| - \|
	\| 0.4131 \| 11050 \| 0.1012 \| - \|
	\| 0.4149 \| 11100 \| 0.0936 \| - \|
	\| 0.4168 \| 11150 \| 0.055 \| - \|
	\| 0.4187 \| 11200 \| 0.0838 \| - \|
	\| 0.4205 \| 11250 \| 0.0785 \| - \|
	\| 0.4224 \| 11300 \| 0.0553 \| - \|
	\| 0.4243 \| 11350 \| 0.0614 \| - \|
	\| 0.4261 \| 11400 \| 0.1269 \| - \|
	\| 0.4280 \| 11450 \| 0.0619 \| - \|
	\| 0.4299 \| 11500 \| 0.0898 \| - \|
	\| 0.4317 \| 11550 \| 0.068 \| - \|
	\| 0.4336 \| 11600 \| 0.0609 \| - \|
	\| 0.4355 \| 11650 \| 0.0771 \| - \|
	\| 0.4374 \| 11700 \| 0.0695 \| - \|
	\| 0.4392 \| 11750 \| 0.0477 \| - \|
	\| 0.4411 \| 11800 \| 0.0724 \| - \|
	\| 0.4430 \| 11850 \| 0.0779 \| - \|
	\| 0.4448 \| 11900 \| 0.039 \| - \|
	\| 0.4467 \| 11950 \| 0.0471 \| - \|
	\| 0.4486 \| 12000 \| 0.0615 \| - \|
	\| 0.4504 \| 12050 \| 0.0641 \| - \|
	\| 0.4523 \| 12100 \| 0.0552 \| - \|
	\| 0.4542 \| 12150 \| 0.0842 \| - \|
	\| 0.4560 \| 12200 \| 0.0492 \| - \|
	\| 0.4579 \| 12250 \| 0.0711 \| - \|
	\| 0.4598 \| 12300 \| 0.0541 \| - \|
	\| 0.4616 \| 12350 \| 0.0506 \| - \|
	\| 0.4635 \| 12400 \| 0.0642 \| - \|
	\| 0.4654 \| 12450 \| 0.0663 \| - \|
	\| 0.4673 \| 12500 \| 0.0496 \| - \|
	\| 0.4691 \| 12550 \| 0.0926 \| - \|
	\| 0.4710 \| 12600 \| 0.0584 \| - \|
	\| 0.4729 \| 12650 \| 0.0613 \| - \|
	\| 0.4747 \| 12700 \| 0.0768 \| - \|
	\| 0.4766 \| 12750 \| 0.0714 \| - \|
	\| 0.4785 \| 12800 \| 0.068 \| - \|
	\| 0.4803 \| 12850 \| 0.0329 \| - \|
	\| 0.4822 \| 12900 \| 0.0873 \| - \|
	\| 0.4841 \| 12950 \| 0.0602 \| - \|
	\| 0.4859 \| 13000 \| 0.0857 \| - \|
	\| 0.4878 \| 13050 \| 0.0563 \| - \|
	\| 0.4897 \| 13100 \| 0.0461 \| - \|
	\| 0.4916 \| 13150 \| 0.0822 \| - \|
	\| 0.4934 \| 13200 \| 0.0591 \| - \|
	\| 0.4953 \| 13250 \| 0.0349 \| - \|
	\| 0.4972 \| 13300 \| 0.0486 \| - \|
	\| 0.4990 \| 13350 \| 0.0636 \| - \|
	\| 0.5009 \| 13400 \| 0.1146 \| - \|
	\| 0.5028 \| 13450 \| 0.0567 \| - \|
	\| 0.5046 \| 13500 \| 0.0325 \| - \|
	\| 0.5065 \| 13550 \| 0.0755 \| - \|
	\| 0.5084 \| 13600 \| 0.0922 \| - \|
	\| 0.5102 \| 13650 \| 0.0674 \| - \|
	\| 0.5121 \| 13700 \| 0.0805 \| - \|
	\| 0.5140 \| 13750 \| 0.0671 \| - \|
	\| 0.5158 \| 13800 \| 0.0939 \| - \|
	\| 0.5177 \| 13850 \| 0.1056 \| - \|
	\| 0.5196 \| 13900 \| 0.0825 \| - \|
	\| 0.5215 \| 13950 \| 0.0741 \| - \|
	\| 0.5233 \| 14000 \| 0.0425 \| - \|
	\| 0.5252 \| 14050 \| 0.051 \| - \|
	\| 0.5271 \| 14100 \| 0.0852 \| - \|
	\| 0.5289 \| 14150 \| 0.0454 \| - \|
	\| 0.5308 \| 14200 \| 0.0902 \| - \|
	\| 0.5327 \| 14250 \| 0.0863 \| - \|
	\| 0.5345 \| 14300 \| 0.0717 \| - \|
	\| 0.5364 \| 14350 \| 0.1116 \| - \|
	\| 0.5383 \| 14400 \| 0.0915 \| - \|
	\| 0.5401 \| 14450 \| 0.0681 \| - \|
	\| 0.5420 \| 14500 \| 0.0559 \| - \|
	\| 0.5439 \| 14550 \| 0.063 \| - \|
	\| 0.5458 \| 14600 \| 0.0856 \| - \|
	\| 0.5476 \| 14650 \| 0.0661 \| - \|
	\| 0.5495 \| 14700 \| 0.1111 \| - \|
	\| 0.5514 \| 14750 \| 0.0983 \| - \|
	\| 0.5532 \| 14800 \| 0.0885 \| - \|
	\| 0.5551 \| 14850 \| 0.0612 \| - \|
	\| 0.5570 \| 14900 \| 0.0764 \| - \|
	\| 0.5588 \| 14950 \| 0.0693 \| - \|
	\| 0.5607 \| 15000 \| 0.0839 \| - \|
	\| 0.5626 \| 15050 \| 0.0872 \| - \|
	\| 0.5644 \| 15100 \| 0.1113 \| - \|
	\| 0.5663 \| 15150 \| 0.0576 \| - \|
	\| 0.5682 \| 15200 \| 0.0645 \| - \|
	\| 0.5701 \| 15250 \| 0.0471 \| - \|
	\| 0.5719 \| 15300 \| 0.0376 \| - \|
	\| 0.5738 \| 15350 \| 0.0798 \| - \|
	\| 0.5757 \| 15400 \| 0.0996 \| - \|
	\| 0.5775 \| 15450 \| 0.0497 \| - \|
	\| 0.5794 \| 15500 \| 0.0579 \| - \|
	\| 0.5813 \| 15550 \| 0.066 \| - \|
	\| 0.5831 \| 15600 \| 0.1259 \| - \|
	\| 0.5850 \| 15650 \| 0.0936 \| - \|
	\| 0.5869 \| 15700 \| 0.0954 \| - \|
	\| 0.5887 \| 15750 \| 0.0543 \| - \|
	\| 0.5906 \| 15800 \| 0.0268 \| - \|
	\| 0.5925 \| 15850 \| 0.0362 \| - \|
	\| 0.5943 \| 15900 \| 0.0635 \| - \|
	\| 0.5962 \| 15950 \| 0.0497 \| - \|
	\| 0.5981 \| 16000 \| 0.0808 \| - \|
	\| 0.6000 \| 16050 \| 0.0759 \| - \|
	\| 0.6018 \| 16100 \| 0.0663 \| - \|
	\| 0.6037 \| 16150 \| 0.0418 \| - \|
	\| 0.6056 \| 16200 \| 0.0656 \| - \|
	\| 0.6074 \| 16250 \| 0.053 \| - \|
	\| 0.6093 \| 16300 \| 0.0763 \| - \|
	\| 0.6112 \| 16350 \| 0.0663 \| - \|
	\| 0.6130 \| 16400 \| 0.0651 \| - \|
	\| 0.6149 \| 16450 \| 0.0774 \| - \|
	\| 0.6168 \| 16500 \| 0.069 \| - \|
	\| 0.6186 \| 16550 \| 0.0647 \| - \|
	\| 0.6205 \| 16600 \| 0.0459 \| - \|
	\| 0.6224 \| 16650 \| 0.0639 \| - \|
	\| 0.6243 \| 16700 \| 0.0526 \| - \|
	\| 0.6261 \| 16750 \| 0.0758 \| - \|
	\| 0.6280 \| 16800 \| 0.04 \| - \|
	\| 0.6299 \| 16850 \| 0.0758 \| - \|
	\| 0.6317 \| 16900 \| 0.0421 \| - \|
	\| 0.6336 \| 16950 \| 0.0557 \| - \|
	\| 0.6355 \| 17000 \| 0.0733 \| - \|
	\| 0.6373 \| 17050 \| 0.0467 \| - \|
	\| 0.6392 \| 17100 \| 0.052 \| - \|
	\| 0.6411 \| 17150 \| 0.1272 \| - \|
	\| 0.6429 \| 17200 \| 0.081 \| - \|
	\| 0.6448 \| 17250 \| 0.0396 \| - \|
	\| 0.6467 \| 17300 \| 0.0494 \| - \|
	\| 0.6485 \| 17350 \| 0.0934 \| - \|
	\| 0.6504 \| 17400 \| 0.0745 \| - \|
	\| 0.6523 \| 17450 \| 0.055 \| - \|
	\| 0.6542 \| 17500 \| 0.065 \| - \|
	\| 0.6560 \| 17550 \| 0.0407 \| - \|
	\| 0.6579 \| 17600 \| 0.0409 \| - \|
	\| 0.6598 \| 17650 \| 0.0317 \| - \|
	\| 0.6616 \| 17700 \| 0.0433 \| - \|
	\| 0.6635 \| 17750 \| 0.0512 \| - \|
	\| 0.6654 \| 17800 \| 0.0731 \| - \|
	\| 0.6672 \| 17850 \| 0.0296 \| - \|
	\| 0.6691 \| 17900 \| 0.059 \| - \|
	\| 0.6710 \| 17950 \| 0.0727 \| - \|
	\| 0.6728 \| 18000 \| 0.0672 \| - \|
	\| 0.6747 \| 18050 \| 0.0661 \| - \|
	\| 0.6766 \| 18100 \| 0.0572 \| - \|
	\| 0.6785 \| 18150 \| 0.0499 \| - \|
	\| 0.6803 \| 18200 \| 0.0839 \| - \|
	\| 0.6822 \| 18250 \| 0.054 \| - \|
	\| 0.6841 \| 18300 \| 0.0754 \| - \|
	\| 0.6859 \| 18350 \| 0.1177 \| - \|
	\| 0.6878 \| 18400 \| 0.0772 \| - \|
	\| 0.6897 \| 18450 \| 0.063 \| - \|
	\| 0.6915 \| 18500 \| 0.0705 \| - \|
	\| 0.6934 \| 18550 \| 0.0653 \| - \|
	\| 0.6953 \| 18600 \| 0.085 \| - \|
	\| 0.6971 \| 18650 \| 0.0668 \| - \|
	\| 0.6990 \| 18700 \| 0.0788 \| - \|
	\| 0.7009 \| 18750 \| 0.0673 \| - \|
	\| 0.7028 \| 18800 \| 0.0606 \| - \|
	\| 0.7046 \| 18850 \| 0.0553 \| - \|
	\| 0.7065 \| 18900 \| 0.0435 \| - \|
	\| 0.7084 \| 18950 \| 0.071 \| - \|
	\| 0.7102 \| 19000 \| 0.0679 \| - \|
	\| 0.7121 \| 19050 \| 0.0632 \| - \|
	\| 0.7140 \| 19100 \| 0.0651 \| - \|
	\| 0.7158 \| 19150 \| 0.092 \| - \|
	\| 0.7177 \| 19200 \| 0.0626 \| - \|
	\| 0.7196 \| 19250 \| 0.0643 \| - \|
	\| 0.7214 \| 19300 \| 0.0242 \| - \|
	\| 0.7233 \| 19350 \| 0.0632 \| - \|
	\| 0.7252 \| 19400 \| 0.0638 \| - \|
	\| 0.7270 \| 19450 \| 0.0543 \| - \|
	\| 0.7289 \| 19500 \| 0.0312 \| - \|
	\| 0.7308 \| 19550 \| 0.1124 \| - \|
	\| 0.7327 \| 19600 \| 0.0432 \| - \|
	\| 0.7345 \| 19650 \| 0.0868 \| - \|
	\| 0.7364 \| 19700 \| 0.0493 \| - \|
	\| 0.7383 \| 19750 \| 0.0301 \| - \|
	\| 0.7401 \| 19800 \| 0.048 \| - \|
	\| 0.7420 \| 19850 \| 0.0594 \| - \|
	\| 0.7439 \| 19900 \| 0.0391 \| - \|
	\| 0.7457 \| 19950 \| 0.0523 \| - \|
	\| 0.7476 \| 20000 \| 0.0951 \| - \|
	\| 0.7495 \| 20050 \| 0.0954 \| - \|
	\| 0.7513 \| 20100 \| 0.0716 \| - \|
	\| 0.7532 \| 20150 \| 0.0366 \| - \|
	\| 0.7551 \| 20200 \| 0.0751 \| - \|
	\| 0.7570 \| 20250 \| 0.0516 \| - \|
	\| 0.7588 \| 20300 \| 0.1157 \| - \|
	\| 0.7607 \| 20350 \| 0.0645 \| - \|
	\| 0.7626 \| 20400 \| 0.065 \| - \|
	\| 0.7644 \| 20450 \| 0.0469 \| - \|
	\| 0.7663 \| 20500 \| 0.0943 \| - \|
	\| 0.7682 \| 20550 \| 0.0884 \| - \|
	\| 0.7700 \| 20600 \| 0.106 \| - \|
	\| 0.7719 \| 20650 \| 0.0783 \| - \|
	\| 0.7738 \| 20700 \| 0.0382 \| - \|
	\| 0.7756 \| 20750 \| 0.0686 \| - \|
	\| 0.7775 \| 20800 \| 0.0689 \| - \|
	\| 0.7794 \| 20850 \| 0.0721 \| - \|
	\| 0.7812 \| 20900 \| 0.0652 \| - \|
	\| 0.7831 \| 20950 \| 0.0994 \| - \|
	\| 0.7850 \| 21000 \| 0.0713 \| - \|
	\| 0.7869 \| 21050 \| 0.0612 \| - \|
	\| 0.7887 \| 21100 \| 0.0664 \| - \|
	\| 0.7906 \| 21150 \| 0.0514 \| - \|
	\| 0.7925 \| 21200 \| 0.0801 \| - \|
	\| 0.7943 \| 21250 \| 0.0469 \| - \|
	\| 0.7962 \| 21300 \| 0.0976 \| - \|
	\| 0.7981 \| 21350 \| 0.0998 \| - \|
	\| 0.7999 \| 21400 \| 0.0495 \| - \|
	\| 0.8018 \| 21450 \| 0.0625 \| - \|
	\| 0.8037 \| 21500 \| 0.0775 \| - \|
	\| 0.8055 \| 21550 \| 0.049 \| - \|
	\| 0.8074 \| 21600 \| 0.0816 \| - \|
	\| 0.8093 \| 21650 \| 0.0644 \| - \|
	\| 0.8112 \| 21700 \| 0.071 \| - \|
	\| 0.8130 \| 21750 \| 0.052 \| - \|
	\| 0.8149 \| 21800 \| 0.0267 \| - \|
	\| 0.8168 \| 21850 \| 0.0598 \| - \|
	\| 0.8186 \| 21900 \| 0.0402 \| - \|
	\| 0.8205 \| 21950 \| 0.0525 \| - \|
	\| 0.8224 \| 22000 \| 0.0745 \| - \|
	\| 0.8242 \| 22050 \| 0.061 \| - \|
	\| 0.8261 \| 22100 \| 0.0623 \| - \|
	\| 0.8280 \| 22150 \| 0.0823 \| - \|
	\| 0.8298 \| 22200 \| 0.0413 \| - \|
	\| 0.8317 \| 22250 \| 0.0679 \| - \|
	\| 0.8336 \| 22300 \| 0.0684 \| - \|
	\| 0.8355 \| 22350 \| 0.0372 \| - \|
	\| 0.8373 \| 22400 \| 0.0754 \| - \|
	\| 0.8392 \| 22450 \| 0.0714 \| - \|
	\| 0.8411 \| 22500 \| 0.089 \| - \|
	\| 0.8429 \| 22550 \| 0.0614 \| - \|
	\| 0.8448 \| 22600 \| 0.0584 \| - \|
	\| 0.8467 \| 22650 \| 0.0978 \| - \|
	\| 0.8485 \| 22700 \| 0.0639 \| - \|
	\| 0.8504 \| 22750 \| 0.0849 \| - \|
	\| 0.8523 \| 22800 \| 0.069 \| - \|
	\| 0.8541 \| 22850 \| 0.0533 \| - \|
	\| 0.8560 \| 22900 \| 0.0655 \| - \|
	\| 0.8579 \| 22950 \| 0.0516 \| - \|
	\| 0.8597 \| 23000 \| 0.0684 \| - \|
	\| 0.8616 \| 23050 \| 0.0471 \| - \|
	\| 0.8635 \| 23100 \| 0.0514 \| - \|
	\| 0.8654 \| 23150 \| 0.0665 \| - \|
	\| 0.8672 \| 23200 \| 0.0475 \| - \|
	\| 0.8691 \| 23250 \| 0.0915 \| - \|
	\| 0.8710 \| 23300 \| 0.0757 \| - \|
	\| 0.8728 \| 23350 \| 0.0549 \| - \|
	\| 0.8747 \| 23400 \| 0.0468 \| - \|
	\| 0.8766 \| 23450 \| 0.0961 \| - \|
	\| 0.8784 \| 23500 \| 0.0659 \| - \|
	\| 0.8803 \| 23550 \| 0.0544 \| - \|
	\| 0.8822 \| 23600 \| 0.1077 \| - \|
	\| 0.8840 \| 23650 \| 0.0527 \| - \|
	\| 0.8859 \| 23700 \| 0.0617 \| - \|
	\| 0.8878 \| 23750 \| 0.0547 \| - \|
	\| 0.8897 \| 23800 \| 0.0336 \| - \|
	\| 0.8915 \| 23850 \| 0.0567 \| - \|
	\| 0.8934 \| 23900 \| 0.0601 \| - \|
	\| 0.8953 \| 23950 \| 0.0577 \| - \|
	\| 0.8971 \| 24000 \| 0.0884 \| - \|
	\| 0.8990 \| 24050 \| 0.0614 \| - \|
	\| 0.9009 \| 24100 \| 0.0382 \| - \|
	\| 0.9027 \| 24150 \| 0.0506 \| - \|
	\| 0.9046 \| 24200 \| 0.0341 \| - \|
	\| 0.9065 \| 24250 \| 0.0534 \| - \|
	\| 0.9083 \| 24300 \| 0.0814 \| - \|
	\| 0.9102 \| 24350 \| 0.0874 \| - \|
	\| 0.9121 \| 24400 \| 0.0621 \| - \|
	\| 0.9140 \| 24450 \| 0.0793 \| - \|
	\| 0.9158 \| 24500 \| 0.0831 \| - \|
	\| 0.9177 \| 24550 \| 0.0564 \| - \|
	\| 0.9196 \| 24600 \| 0.0487 \| - \|
	\| 0.9214 \| 24650 \| 0.1 \| - \|
	\| 0.9233 \| 24700 \| 0.0852 \| - \|
	\| 0.9252 \| 24750 \| 0.054 \| - \|
	\| 0.9270 \| 24800 \| 0.046 \| - \|
	\| 0.9289 \| 24850 \| 0.0523 \| - \|
	\| 0.9308 \| 24900 \| 0.0661 \| - \|
	\| 0.9326 \| 24950 \| 0.0682 \| - \|
	\| 0.9345 \| 25000 \| 0.0418 \| - \|
	\| 0.9364 \| 25050 \| 0.0608 \| - \|
	\| 0.9382 \| 25100 \| 0.0951 \| - \|
	\| 0.9401 \| 25150 \| 0.052 \| - \|
	\| 0.9420 \| 25200 \| 0.0464 \| - \|
	\| 0.9439 \| 25250 \| 0.0874 \| - \|
	\| 0.9457 \| 25300 \| 0.033 \| - \|
	\| 0.9476 \| 25350 \| 0.0492 \| - \|
	\| 0.9495 \| 25400 \| 0.0735 \| - \|
	\| 0.9513 \| 25450 \| 0.0659 \| - \|
	\| 0.9532 \| 25500 \| 0.0936 \| - \|
	\| 0.9551 \| 25550 \| 0.085 \| - \|
	\| 0.9569 \| 25600 \| 0.0607 \| - \|
	\| 0.9588 \| 25650 \| 0.0646 \| - \|
	\| 0.9607 \| 25700 \| 0.0835 \| - \|
	\| 0.9625 \| 25750 \| 0.0641 \| - \|
	\| 0.9644 \| 25800 \| 0.0603 \| - \|
	\| 0.9663 \| 25850 \| 0.0857 \| - \|
	\| 0.9682 \| 25900 \| 0.0605 \| - \|
	\| 0.9700 \| 25950 \| 0.0614 \| - \|
	\| 0.9719 \| 26000 \| 0.0617 \| - \|
	\| 0.9738 \| 26050 \| 0.0639 \| - \|
	\| 0.9756 \| 26100 \| 0.0502 \| - \|
	\| 0.9775 \| 26150 \| 0.089 \| - \|
	\| 0.9794 \| 26200 \| 0.0604 \| - \|
	\| 0.9812 \| 26250 \| 0.0867 \| - \|
	\| 0.9831 \| 26300 \| 0.0597 \| - \|
	\| 0.9850 \| 26350 \| 0.0755 \| - \|
	\| 0.9868 \| 26400 \| 0.0628 \| - \|
	\| 0.9887 \| 26450 \| 0.0685 \| - \|
	\| 0.9906 \| 26500 \| 0.0794 \| - \|
	\| 0.9924 \| 26550 \| 0.0892 \| - \|
	\| 0.9943 \| 26600 \| 0.0716 \| - \|
	\| 0.9962 \| 26650 \| 0.0397 \| - \|
	\| 0.9981 \| 26700 \| 0.0933 \| - \|
	\| 0.9999 \| 26750 \| 0.0663 \| - \|

	### Framework Versions
	- Python: 3.10.6
	- SetFit: 1.0.3
	- Sentence Transformers: 2.3.1
	- Transformers: 4.35.2
	- PyTorch: 2.2.0
	- Datasets: 2.21.0
	- Tokenizers: 0.15.1

	## Citation

	### BibTeX
	```bibtex
	@article{https://doi.org/10.48550/arxiv.2209.11055,
	doi = {10.48550/ARXIV.2209.11055},
	url = {https://arxiv.org/abs/2209.11055},
	author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
	keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
	title = {Efficient Few-Shot Learning Without Prompts},
	publisher = {arXiv},
	year = {2022},
	copyright = {Creative Commons Attribution 4.0 International}
	}
	```

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