StanceFit / README.md

Version Jun 20, 2024

6caa984 verified 5 months ago

43.3 kB

	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: We will also discuss our deep concerns with actions by China, including in
	Xinjiang, Hong Kong, Taiwan, cyber attacks on the United States, economic coercion
	toward our allies.
	- text: In the field of bilateral trade and investment, we have agreed that much can
	be done to expand the present level of activity.
	- text: We cannot allow the world's leading sponsor of terrorism to possess the planet's
	most dangerous weapons.
	- text: Because I do think this is not a function of whatever happened in Syria, I
	think this is a function of the sanctions.
	- text: One is to fight inflation, which has been hanging over our head and putting
	a burden on the working people of this country for the last 10 years.
	pipeline_tag: text-classification
	inference: true
	base_model: sentence-transformers/all-mpnet-base-v2
	---

	# SetFit with sentence-transformers/all-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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 384 tokens
	- Number of Classes: 2 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)

	### Model Labels
	\| Label \| Examples \|
	\|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| 0 \| <ul><li>'We in the United States believe if we can promote democracy around the world, there will be more peace.'</li><li>'We recognise the transformative power of technology, including digital public infrastructure, to support sustainable development in the Indo-Pacific and deliver economic and social benefits.'</li><li>'This program strengthens democracy, transparency, and the rule of law in developing nations, and I ask you to fully fund this important initiative.'</li></ul> \|
	\| 1 \| <ul><li>'I do not ever want to ever fight a war that is unconstitutional and I am the dangerous person.'</li><li>"And so, we are at a moment where I really think threats to our democracy, threats to our core freedoms are very much on people's minds."</li><li>'My views in opposition to the cancellation of the war debt are a matter of detailed record in many public statements and in a recent message to the Congress.'</li></ul> \|

	## 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("We cannot allow the world's leading sponsor of terrorism to possess the planet's most dangerous weapons.")
	```

	<!--
	### 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 \| 23.4393 \| 46 \|

	\| Label \| Training Sample Count \|
	\|:------\|:----------------------\|
	\| 0 \| 486 \|
	\| 1 \| 486 \|

	### Training Hyperparameters
	- batch_size: (16, 16)
	- num_epochs: (1, 1)
	- max_steps: -1
	- sampling_strategy: oversampling
	- body_learning_rate: (1.003444469523018e-06, 1.003444469523018e-06)
	- 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: 37
	- eval_max_steps: -1
	- load_best_model_at_end: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:----------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0000 \| 1 \| 0.3295 \| - \|
	\| 0.0017 \| 50 \| 0.3132 \| - \|
	\| 0.0034 \| 100 \| 0.274 \| - \|
	\| 0.0051 \| 150 \| 0.2774 \| - \|
	\| 0.0068 \| 200 \| 0.2578 \| - \|
	\| 0.0084 \| 250 \| 0.2536 \| - \|
	\| 0.0101 \| 300 \| 0.3353 \| - \|
	\| 0.0118 \| 350 \| 0.253 \| - \|
	\| 0.0135 \| 400 \| 0.2865 \| - \|
	\| 0.0152 \| 450 \| 0.2894 \| - \|
	\| 0.0169 \| 500 \| 0.2554 \| 0.2632 \|
	\| 0.0186 \| 550 \| 0.2487 \| - \|
	\| 0.0203 \| 600 \| 0.2713 \| - \|
	\| 0.0220 \| 650 \| 0.2841 \| - \|
	\| 0.0237 \| 700 \| 0.2251 \| - \|
	\| 0.0253 \| 750 \| 0.2534 \| - \|
	\| 0.0270 \| 800 \| 0.2489 \| - \|
	\| 0.0287 \| 850 \| 0.2297 \| - \|
	\| 0.0304 \| 900 \| 0.2288 \| - \|
	\| 0.0321 \| 950 \| 0.211 \| - \|
	\| 0.0338 \| 1000 \| 0.188 \| 0.2073 \|
	\| 0.0355 \| 1050 \| 0.1488 \| - \|
	\| 0.0372 \| 1100 \| 0.2103 \| - \|
	\| 0.0389 \| 1150 \| 0.1607 \| - \|
	\| 0.0406 \| 1200 \| 0.0793 \| - \|
	\| 0.0422 \| 1250 \| 0.0968 \| - \|
	\| 0.0439 \| 1300 \| 0.0987 \| - \|
	\| 0.0456 \| 1350 \| 0.0786 \| - \|
	\| 0.0473 \| 1400 \| 0.0267 \| - \|
	\| 0.0490 \| 1450 \| 0.0432 \| - \|
	\| 0.0507 \| 1500 \| 0.0262 \| 0.064 \|
	\| 0.0524 \| 1550 \| 0.1269 \| - \|
	\| 0.0541 \| 1600 \| 0.039 \| - \|
	\| 0.0558 \| 1650 \| 0.0266 \| - \|
	\| 0.0575 \| 1700 \| 0.0455 \| - \|
	\| 0.0591 \| 1750 \| 0.0175 \| - \|
	\| 0.0608 \| 1800 \| 0.0157 \| - \|
	\| 0.0625 \| 1850 \| 0.0063 \| - \|
	\| 0.0642 \| 1900 \| 0.0146 \| - \|
	\| 0.0659 \| 1950 \| 0.0046 \| - \|
	\| 0.0676 \| 2000 \| 0.0046 \| 0.0464 \|
	\| 0.0693 \| 2050 \| 0.0035 \| - \|
	\| 0.0710 \| 2100 \| 0.0073 \| - \|
	\| 0.0727 \| 2150 \| 0.0012 \| - \|
	\| 0.0744 \| 2200 \| 0.0025 \| - \|
	\| 0.0760 \| 2250 \| 0.0023 \| - \|
	\| 0.0777 \| 2300 \| 0.0017 \| - \|
	\| 0.0794 \| 2350 \| 0.0012 \| - \|
	\| 0.0811 \| 2400 \| 0.0017 \| - \|
	\| 0.0828 \| 2450 \| 0.0016 \| - \|
	\| 0.0845 \| 2500 \| 0.0014 \| 0.0535 \|
	\| 0.0862 \| 2550 \| 0.0011 \| - \|
	\| 0.0879 \| 2600 \| 0.0021 \| - \|
	\| 0.0896 \| 2650 \| 0.0009 \| - \|
	\| 0.0913 \| 2700 \| 0.0008 \| - \|
	\| 0.0929 \| 2750 \| 0.0006 \| - \|
	\| 0.0946 \| 2800 \| 0.0007 \| - \|
	\| 0.0963 \| 2850 \| 0.0012 \| - \|
	\| 0.0980 \| 2900 \| 0.001 \| - \|
	\| 0.0997 \| 2950 \| 0.0005 \| - \|
	\| 0.1014 \| 3000 \| 0.0006 \| 0.0575 \|
	\| 0.1031 \| 3050 \| 0.0006 \| - \|
	\| 0.1048 \| 3100 \| 0.0004 \| - \|
	\| 0.1065 \| 3150 \| 0.0006 \| - \|
	\| 0.1082 \| 3200 \| 0.0005 \| - \|
	\| 0.1098 \| 3250 \| 0.0006 \| - \|
	\| 0.1115 \| 3300 \| 0.0005 \| - \|
	\| 0.1132 \| 3350 \| 0.0008 \| - \|
	\| 0.1149 \| 3400 \| 0.0003 \| - \|
	\| 0.1166 \| 3450 \| 0.0005 \| - \|
	\| 0.1183 \| 3500 \| 0.0004 \| 0.0642 \|
	\| 0.1200 \| 3550 \| 0.0006 \| - \|
	\| 0.1217 \| 3600 \| 0.0003 \| - \|
	\| 0.1234 \| 3650 \| 0.0009 \| - \|
	\| 0.1251 \| 3700 \| 0.0002 \| - \|
	\| 0.1267 \| 3750 \| 0.0003 \| - \|
	\| 0.1284 \| 3800 \| 0.0005 \| - \|
	\| 0.1301 \| 3850 \| 0.0002 \| - \|
	\| 0.1318 \| 3900 \| 0.0002 \| - \|
	\| 0.1335 \| 3950 \| 0.0005 \| - \|
	\| 0.1352 \| 4000 \| 0.0003 \| 0.0697 \|
	\| 0.1369 \| 4050 \| 0.0002 \| - \|
	\| 0.1386 \| 4100 \| 0.0002 \| - \|
	\| 0.1403 \| 4150 \| 0.0004 \| - \|
	\| 0.1420 \| 4200 \| 0.0012 \| - \|
	\| 0.1436 \| 4250 \| 0.0002 \| - \|
	\| 0.1453 \| 4300 \| 0.0002 \| - \|
	\| 0.1470 \| 4350 \| 0.0001 \| - \|
	\| 0.1487 \| 4400 \| 0.0002 \| - \|
	\| 0.1504 \| 4450 \| 0.0002 \| - \|
	\| 0.1521 \| 4500 \| 0.0003 \| 0.0718 \|
	\| 0.1538 \| 4550 \| 0.0003 \| - \|
	\| 0.1555 \| 4600 \| 0.0002 \| - \|
	\| 0.1572 \| 4650 \| 0.0002 \| - \|
	\| 0.1589 \| 4700 \| 0.0003 \| - \|
	\| 0.1605 \| 4750 \| 0.0002 \| - \|
	\| 0.1622 \| 4800 \| 0.0002 \| - \|
	\| 0.1639 \| 4850 \| 0.0002 \| - \|
	\| 0.1656 \| 4900 \| 0.0002 \| - \|
	\| 0.1673 \| 4950 \| 0.0002 \| - \|
	\| 0.1690 \| 5000 \| 0.0002 \| 0.0684 \|
	\| 0.1707 \| 5050 \| 0.0002 \| - \|
	\| 0.1724 \| 5100 \| 0.0002 \| - \|
	\| 0.1741 \| 5150 \| 0.0002 \| - \|
	\| 0.1758 \| 5200 \| 0.0003 \| - \|
	\| 0.1774 \| 5250 \| 0.0002 \| - \|
	\| 0.1791 \| 5300 \| 0.0001 \| - \|
	\| 0.1808 \| 5350 \| 0.0002 \| - \|
	\| 0.1825 \| 5400 \| 0.0001 \| - \|
	\| 0.1842 \| 5450 \| 0.0001 \| - \|
	\| 0.1859 \| 5500 \| 0.0001 \| 0.0731 \|
	\| 0.1876 \| 5550 \| 0.0002 \| - \|
	\| 0.1893 \| 5600 \| 0.0002 \| - \|
	\| 0.1910 \| 5650 \| 0.0001 \| - \|
	\| 0.1927 \| 5700 \| 0.0001 \| - \|
	\| 0.1943 \| 5750 \| 0.0001 \| - \|
	\| 0.1960 \| 5800 \| 0.0002 \| - \|
	\| 0.1977 \| 5850 \| 0.0001 \| - \|
	\| 0.1994 \| 5900 \| 0.0003 \| - \|
	\| 0.2011 \| 5950 \| 0.0002 \| - \|
	\| 0.2028 \| 6000 \| 0.0002 \| 0.0724 \|
	\| 0.2045 \| 6050 \| 0.0001 \| - \|
	\| 0.2062 \| 6100 \| 0.0001 \| - \|
	\| 0.2079 \| 6150 \| 0.0001 \| - \|
	\| 0.2096 \| 6200 \| 0.0001 \| - \|
	\| 0.2112 \| 6250 \| 0.0001 \| - \|
	\| 0.2129 \| 6300 \| 0.0002 \| - \|
	\| 0.2146 \| 6350 \| 0.0001 \| - \|
	\| 0.2163 \| 6400 \| 0.0001 \| - \|
	\| 0.2180 \| 6450 \| 0.0001 \| - \|
	\| 0.2197 \| 6500 \| 0.0001 \| 0.0784 \|
	\| 0.2214 \| 6550 \| 0.0001 \| - \|
	\| 0.2231 \| 6600 \| 0.0001 \| - \|
	\| 0.2248 \| 6650 \| 0.0001 \| - \|
	\| 0.2265 \| 6700 \| 0.0001 \| - \|
	\| 0.2281 \| 6750 \| 0.0001 \| - \|
	\| 0.2298 \| 6800 \| 0.0001 \| - \|
	\| 0.2315 \| 6850 \| 0.0001 \| - \|
	\| 0.2332 \| 6900 \| 0.0001 \| - \|
	\| 0.2349 \| 6950 \| 0.0002 \| - \|
	\| 0.2366 \| 7000 \| 0.0001 \| 0.0672 \|
	\| 0.2383 \| 7050 \| 0.0001 \| - \|
	\| 0.2400 \| 7100 \| 0.0001 \| - \|
	\| 0.2417 \| 7150 \| 0.0001 \| - \|
	\| 0.2434 \| 7200 \| 0.0001 \| - \|
	\| 0.2450 \| 7250 \| 0.0001 \| - \|
	\| 0.2467 \| 7300 \| 0.0001 \| - \|
	\| 0.2484 \| 7350 \| 0.0001 \| - \|
	\| 0.2501 \| 7400 \| 0.0001 \| - \|
	\| 0.2518 \| 7450 \| 0.0001 \| - \|
	\| 0.2535 \| 7500 \| 0.0001 \| 0.0627 \|
	\| 0.2552 \| 7550 \| 0.0001 \| - \|
	\| 0.2569 \| 7600 \| 0.0001 \| - \|
	\| 0.2586 \| 7650 \| 0.0 \| - \|
	\| 0.2603 \| 7700 \| 0.0001 \| - \|
	\| 0.2619 \| 7750 \| 0.0 \| - \|
	\| 0.2636 \| 7800 \| 0.0001 \| - \|
	\| 0.2653 \| 7850 \| 0.0001 \| - \|
	\| 0.2670 \| 7900 \| 0.0001 \| - \|
	\| 0.2687 \| 7950 \| 0.0001 \| - \|
	\| 0.2704 \| 8000 \| 0.0 \| 0.0754 \|
	\| 0.2721 \| 8050 \| 0.0001 \| - \|
	\| 0.2738 \| 8100 \| 0.0001 \| - \|
	\| 0.2755 \| 8150 \| 0.0 \| - \|
	\| 0.2772 \| 8200 \| 0.0 \| - \|
	\| 0.2788 \| 8250 \| 0.0 \| - \|
	\| 0.2805 \| 8300 \| 0.0001 \| - \|
	\| 0.2822 \| 8350 \| 0.0001 \| - \|
	\| 0.2839 \| 8400 \| 0.0001 \| - \|
	\| 0.2856 \| 8450 \| 0.0 \| - \|
	\| 0.2873 \| 8500 \| 0.0 \| 0.0748 \|
	\| 0.2890 \| 8550 \| 0.0 \| - \|
	\| 0.2907 \| 8600 \| 0.0 \| - \|
	\| 0.2924 \| 8650 \| 0.0 \| - \|
	\| 0.2941 \| 8700 \| 0.0 \| - \|
	\| 0.2957 \| 8750 \| 0.0001 \| - \|
	\| 0.2974 \| 8800 \| 0.0001 \| - \|
	\| 0.2991 \| 8850 \| 0.0001 \| - \|
	\| 0.3008 \| 8900 \| 0.0 \| - \|
	\| 0.3025 \| 8950 \| 0.0001 \| - \|
	\| 0.3042 \| 9000 \| 0.0001 \| 0.057 \|
	\| 0.3059 \| 9050 \| 0.0 \| - \|
	\| 0.3076 \| 9100 \| 0.0 \| - \|
	\| 0.3093 \| 9150 \| 0.0002 \| - \|
	\| 0.3110 \| 9200 \| 0.0 \| - \|
	\| 0.3126 \| 9250 \| 0.0 \| - \|
	\| 0.3143 \| 9300 \| 0.0 \| - \|
	\| 0.3160 \| 9350 \| 0.0001 \| - \|
	\| 0.3177 \| 9400 \| 0.0002 \| - \|
	\| 0.3194 \| 9450 \| 0.0 \| - \|
	\| 0.3211 \| 9500 \| 0.0 \| 0.0781 \|
	\| 0.3228 \| 9550 \| 0.0 \| - \|
	\| 0.3245 \| 9600 \| 0.0 \| - \|
	\| 0.3262 \| 9650 \| 0.0 \| - \|
	\| 0.3279 \| 9700 \| 0.0 \| - \|
	\| 0.3295 \| 9750 \| 0.0 \| - \|
	\| 0.3312 \| 9800 \| 0.0 \| - \|
	\| 0.3329 \| 9850 \| 0.0 \| - \|
	\| 0.3346 \| 9900 \| 0.0001 \| - \|
	\| 0.3363 \| 9950 \| 0.0 \| - \|
	\| 0.3380 \| 10000 \| 0.0 \| 0.0698 \|
	\| 0.3397 \| 10050 \| 0.0 \| - \|
	\| 0.3414 \| 10100 \| 0.0 \| - \|
	\| 0.3431 \| 10150 \| 0.0 \| - \|
	\| 0.3448 \| 10200 \| 0.0 \| - \|
	\| 0.3464 \| 10250 \| 0.0022 \| - \|
	\| 0.3481 \| 10300 \| 0.0 \| - \|
	\| 0.3498 \| 10350 \| 0.0001 \| - \|
	\| 0.3515 \| 10400 \| 0.0 \| - \|
	\| 0.3532 \| 10450 \| 0.0 \| - \|
	\| 0.3549 \| 10500 \| 0.0 \| 0.0698 \|
	\| 0.3566 \| 10550 \| 0.0 \| - \|
	\| 0.3583 \| 10600 \| 0.0 \| - \|
	\| 0.3600 \| 10650 \| 0.0 \| - \|
	\| 0.3617 \| 10700 \| 0.0 \| - \|
	\| 0.3633 \| 10750 \| 0.0 \| - \|
	\| 0.3650 \| 10800 \| 0.0 \| - \|
	\| 0.3667 \| 10850 \| 0.0 \| - \|
	\| 0.3684 \| 10900 \| 0.0001 \| - \|
	\| 0.3701 \| 10950 \| 0.0 \| - \|
	\| 0.3718 \| 11000 \| 0.0 \| 0.0746 \|
	\| 0.3735 \| 11050 \| 0.0 \| - \|
	\| 0.3752 \| 11100 \| 0.0 \| - \|
	\| 0.3769 \| 11150 \| 0.0001 \| - \|
	\| 0.3786 \| 11200 \| 0.0 \| - \|
	\| 0.3802 \| 11250 \| 0.0 \| - \|
	\| 0.3819 \| 11300 \| 0.0 \| - \|
	\| 0.3836 \| 11350 \| 0.0 \| - \|
	\| 0.3853 \| 11400 \| 0.0 \| - \|
	\| 0.3870 \| 11450 \| 0.0 \| - \|
	\| 0.3887 \| 11500 \| 0.0 \| 0.0753 \|
	\| 0.3904 \| 11550 \| 0.0 \| - \|
	\| 0.3921 \| 11600 \| 0.0001 \| - \|
	\| 0.3938 \| 11650 \| 0.0 \| - \|
	\| 0.3955 \| 11700 \| 0.0 \| - \|
	\| 0.3971 \| 11750 \| 0.0 \| - \|
	\| 0.3988 \| 11800 \| 0.0 \| - \|
	\| 0.4005 \| 11850 \| 0.0 \| - \|
	\| 0.4022 \| 11900 \| 0.0 \| - \|
	\| 0.4039 \| 11950 \| 0.0 \| - \|
	\| 0.4056 \| 12000 \| 0.0 \| 0.0743 \|
	\| 0.4073 \| 12050 \| 0.0 \| - \|
	\| 0.4090 \| 12100 \| 0.0 \| - \|
	\| 0.4107 \| 12150 \| 0.0 \| - \|
	\| 0.4124 \| 12200 \| 0.0 \| - \|
	\| 0.4140 \| 12250 \| 0.0 \| - \|
	\| 0.4157 \| 12300 \| 0.0 \| - \|
	\| 0.4174 \| 12350 \| 0.0 \| - \|
	\| 0.4191 \| 12400 \| 0.0 \| - \|
	\| 0.4208 \| 12450 \| 0.0 \| - \|
	\| 0.4225 \| 12500 \| 0.0 \| 0.0733 \|
	\| 0.4242 \| 12550 \| 0.0 \| - \|
	\| 0.4259 \| 12600 \| 0.0 \| - \|
	\| 0.4276 \| 12650 \| 0.0 \| - \|
	\| 0.4293 \| 12700 \| 0.0 \| - \|
	\| 0.4309 \| 12750 \| 0.0 \| - \|
	\| 0.4326 \| 12800 \| 0.0 \| - \|
	\| 0.4343 \| 12850 \| 0.0 \| - \|
	\| 0.4360 \| 12900 \| 0.0 \| - \|
	\| 0.4377 \| 12950 \| 0.0 \| - \|
	\| 0.4394 \| 13000 \| 0.0 \| 0.072 \|
	\| 0.4411 \| 13050 \| 0.0 \| - \|
	\| 0.4428 \| 13100 \| 0.0 \| - \|
	\| 0.4445 \| 13150 \| 0.0 \| - \|
	\| 0.4462 \| 13200 \| 0.0 \| - \|
	\| 0.4478 \| 13250 \| 0.0 \| - \|
	\| 0.4495 \| 13300 \| 0.0 \| - \|
	\| 0.4512 \| 13350 \| 0.0 \| - \|
	\| 0.4529 \| 13400 \| 0.0 \| - \|
	\| 0.4546 \| 13450 \| 0.0 \| - \|
	\| 0.4563 \| 13500 \| 0.0 \| 0.0753 \|
	\| 0.4580 \| 13550 \| 0.0 \| - \|
	\| 0.4597 \| 13600 \| 0.0 \| - \|
	\| 0.4614 \| 13650 \| 0.0 \| - \|
	\| 0.4631 \| 13700 \| 0.0 \| - \|
	\| 0.4647 \| 13750 \| 0.0 \| - \|
	\| 0.4664 \| 13800 \| 0.0 \| - \|
	\| 0.4681 \| 13850 \| 0.0 \| - \|
	\| 0.4698 \| 13900 \| 0.0 \| - \|
	\| 0.4715 \| 13950 \| 0.0 \| - \|
	\| 0.4732 \| 14000 \| 0.0 \| 0.0756 \|
	\| 0.4749 \| 14050 \| 0.0 \| - \|
	\| 0.4766 \| 14100 \| 0.0 \| - \|
	\| 0.4783 \| 14150 \| 0.0 \| - \|
	\| 0.4800 \| 14200 \| 0.0 \| - \|
	\| 0.4816 \| 14250 \| 0.0 \| - \|
	\| 0.4833 \| 14300 \| 0.0 \| - \|
	\| 0.4850 \| 14350 \| 0.0 \| - \|
	\| 0.4867 \| 14400 \| 0.0 \| - \|
	\| 0.4884 \| 14450 \| 0.0 \| - \|
	\| 0.4901 \| 14500 \| 0.0 \| 0.0622 \|
	\| 0.4918 \| 14550 \| 0.0 \| - \|
	\| 0.4935 \| 14600 \| 0.0 \| - \|
	\| 0.4952 \| 14650 \| 0.0 \| - \|
	\| 0.4969 \| 14700 \| 0.0 \| - \|
	\| 0.4985 \| 14750 \| 0.0 \| - \|
	\| 0.5002 \| 14800 \| 0.0 \| - \|
	\| 0.5019 \| 14850 \| 0.0 \| - \|
	\| 0.5036 \| 14900 \| 0.0 \| - \|
	\| 0.5053 \| 14950 \| 0.0 \| - \|
	\| 0.5070 \| 15000 \| 0.0 \| 0.0676 \|
	\| 0.5087 \| 15050 \| 0.0 \| - \|
	\| 0.5104 \| 15100 \| 0.0 \| - \|
	\| 0.5121 \| 15150 \| 0.0 \| - \|
	\| 0.5138 \| 15200 \| 0.0 \| - \|
	\| 0.5154 \| 15250 \| 0.0 \| - \|
	\| 0.5171 \| 15300 \| 0.0 \| - \|
	\| 0.5188 \| 15350 \| 0.0 \| - \|
	\| 0.5205 \| 15400 \| 0.0 \| - \|
	\| 0.5222 \| 15450 \| 0.0 \| - \|
	\| 0.5239 \| 15500 \| 0.0 \| 0.0668 \|
	\| 0.5256 \| 15550 \| 0.0 \| - \|
	\| 0.5273 \| 15600 \| 0.0 \| - \|
	\| 0.5290 \| 15650 \| 0.0 \| - \|
	\| 0.5307 \| 15700 \| 0.0 \| - \|
	\| 0.5323 \| 15750 \| 0.0 \| - \|
	\| 0.5340 \| 15800 \| 0.0 \| - \|
	\| 0.5357 \| 15850 \| 0.0 \| - \|
	\| 0.5374 \| 15900 \| 0.0 \| - \|
	\| 0.5391 \| 15950 \| 0.0 \| - \|
	\| 0.5408 \| 16000 \| 0.0 \| 0.0707 \|
	\| 0.5425 \| 16050 \| 0.0 \| - \|
	\| 0.5442 \| 16100 \| 0.0 \| - \|
	\| 0.5459 \| 16150 \| 0.0 \| - \|
	\| 0.5476 \| 16200 \| 0.0 \| - \|
	\| 0.5492 \| 16250 \| 0.0 \| - \|
	\| 0.5509 \| 16300 \| 0.0 \| - \|
	\| 0.5526 \| 16350 \| 0.0 \| - \|
	\| 0.5543 \| 16400 \| 0.0 \| - \|
	\| 0.5560 \| 16450 \| 0.0 \| - \|
	\| 0.5577 \| 16500 \| 0.0 \| 0.0644 \|
	\| 0.5594 \| 16550 \| 0.0 \| - \|
	\| 0.5611 \| 16600 \| 0.0 \| - \|
	\| 0.5628 \| 16650 \| 0.0 \| - \|
	\| 0.5645 \| 16700 \| 0.0 \| - \|
	\| 0.5661 \| 16750 \| 0.0 \| - \|
	\| 0.5678 \| 16800 \| 0.0 \| - \|
	\| 0.5695 \| 16850 \| 0.0 \| - \|
	\| 0.5712 \| 16900 \| 0.0 \| - \|
	\| 0.5729 \| 16950 \| 0.0 \| - \|
	\| 0.5746 \| 17000 \| 0.0 \| 0.0742 \|
	\| 0.5763 \| 17050 \| 0.0 \| - \|
	\| 0.5780 \| 17100 \| 0.0 \| - \|
	\| 0.5797 \| 17150 \| 0.0 \| - \|
	\| 0.5814 \| 17200 \| 0.0 \| - \|
	\| 0.5830 \| 17250 \| 0.0 \| - \|
	\| 0.5847 \| 17300 \| 0.0 \| - \|
	\| 0.5864 \| 17350 \| 0.0 \| - \|
	\| 0.5881 \| 17400 \| 0.0 \| - \|
	\| 0.5898 \| 17450 \| 0.0 \| - \|
	\| 0.5915 \| 17500 \| 0.0 \| 0.0738 \|
	\| 0.5932 \| 17550 \| 0.0 \| - \|
	\| 0.5949 \| 17600 \| 0.0 \| - \|
	\| 0.5966 \| 17650 \| 0.0 \| - \|
	\| 0.5983 \| 17700 \| 0.0 \| - \|
	\| 0.5999 \| 17750 \| 0.0 \| - \|
	\| 0.6016 \| 17800 \| 0.0 \| - \|
	\| 0.6033 \| 17850 \| 0.0 \| - \|
	\| 0.6050 \| 17900 \| 0.0 \| - \|
	\| 0.6067 \| 17950 \| 0.0 \| - \|
	\| 0.6084 \| 18000 \| 0.0 \| 0.0725 \|
	\| 0.6101 \| 18050 \| 0.0 \| - \|
	\| 0.6118 \| 18100 \| 0.0 \| - \|
	\| 0.6135 \| 18150 \| 0.0 \| - \|
	\| 0.6152 \| 18200 \| 0.0 \| - \|
	\| 0.6168 \| 18250 \| 0.0 \| - \|
	\| 0.6185 \| 18300 \| 0.0 \| - \|
	\| 0.6202 \| 18350 \| 0.0 \| - \|
	\| 0.6219 \| 18400 \| 0.0 \| - \|
	\| 0.6236 \| 18450 \| 0.0 \| - \|
	\| 0.6253 \| 18500 \| 0.0 \| 0.0724 \|
	\| 0.6270 \| 18550 \| 0.0 \| - \|
	\| 0.6287 \| 18600 \| 0.0 \| - \|
	\| 0.6304 \| 18650 \| 0.0 \| - \|
	\| 0.6321 \| 18700 \| 0.0 \| - \|
	\| 0.6337 \| 18750 \| 0.0 \| - \|
	\| 0.6354 \| 18800 \| 0.0 \| - \|
	\| 0.6371 \| 18850 \| 0.0 \| - \|
	\| 0.6388 \| 18900 \| 0.0 \| - \|
	\| 0.6405 \| 18950 \| 0.0 \| - \|
	\| 0.6422 \| 19000 \| 0.0 \| 0.0622 \|
	\| 0.6439 \| 19050 \| 0.0 \| - \|
	\| 0.6456 \| 19100 \| 0.0 \| - \|
	\| 0.6473 \| 19150 \| 0.0 \| - \|
	\| 0.6490 \| 19200 \| 0.0 \| - \|
	\| 0.6506 \| 19250 \| 0.0 \| - \|
	\| 0.6523 \| 19300 \| 0.0 \| - \|
	\| 0.6540 \| 19350 \| 0.0 \| - \|
	\| 0.6557 \| 19400 \| 0.0 \| - \|
	\| 0.6574 \| 19450 \| 0.0 \| - \|
	\| 0.6591 \| 19500 \| 0.0 \| 0.0754 \|
	\| 0.6608 \| 19550 \| 0.0 \| - \|
	\| 0.6625 \| 19600 \| 0.0 \| - \|
	\| 0.6642 \| 19650 \| 0.0 \| - \|
	\| 0.6659 \| 19700 \| 0.0 \| - \|
	\| 0.6675 \| 19750 \| 0.0 \| - \|
	\| 0.6692 \| 19800 \| 0.0 \| - \|
	\| 0.6709 \| 19850 \| 0.0 \| - \|
	\| 0.6726 \| 19900 \| 0.0 \| - \|
	\| 0.6743 \| 19950 \| 0.0 \| - \|
	\| 0.6760 \| 20000 \| 0.0 \| 0.0723 \|
	\| 0.6777 \| 20050 \| 0.0 \| - \|
	\| 0.6794 \| 20100 \| 0.0 \| - \|
	\| 0.6811 \| 20150 \| 0.0 \| - \|
	\| 0.6828 \| 20200 \| 0.0 \| - \|
	\| 0.6844 \| 20250 \| 0.0 \| - \|
	\| 0.6861 \| 20300 \| 0.0 \| - \|
	\| 0.6878 \| 20350 \| 0.0 \| - \|
	\| 0.6895 \| 20400 \| 0.0 \| - \|
	\| 0.6912 \| 20450 \| 0.0 \| - \|
	\| 0.6929 \| 20500 \| 0.0 \| 0.0741 \|
	\| 0.6946 \| 20550 \| 0.0 \| - \|
	\| 0.6963 \| 20600 \| 0.0 \| - \|
	\| 0.6980 \| 20650 \| 0.0 \| - \|
	\| 0.6997 \| 20700 \| 0.0 \| - \|
	\| 0.7013 \| 20750 \| 0.0 \| - \|
	\| 0.7030 \| 20800 \| 0.0 \| - \|
	\| 0.7047 \| 20850 \| 0.0 \| - \|
	\| 0.7064 \| 20900 \| 0.0 \| - \|
	\| 0.7081 \| 20950 \| 0.0 \| - \|
	\| 0.7098 \| 21000 \| 0.0 \| 0.0733 \|
	\| 0.7115 \| 21050 \| 0.0 \| - \|
	\| 0.7132 \| 21100 \| 0.0 \| - \|
	\| 0.7149 \| 21150 \| 0.0 \| - \|
	\| 0.7166 \| 21200 \| 0.0 \| - \|
	\| 0.7182 \| 21250 \| 0.0 \| - \|
	\| 0.7199 \| 21300 \| 0.0 \| - \|
	\| 0.7216 \| 21350 \| 0.0 \| - \|
	\| 0.7233 \| 21400 \| 0.0 \| - \|
	\| 0.7250 \| 21450 \| 0.0 \| - \|
	\| 0.7267 \| 21500 \| 0.0 \| 0.0757 \|
	\| 0.7284 \| 21550 \| 0.0 \| - \|
	\| 0.7301 \| 21600 \| 0.0 \| - \|
	\| 0.7318 \| 21650 \| 0.0 \| - \|
	\| 0.7335 \| 21700 \| 0.0 \| - \|
	\| 0.7351 \| 21750 \| 0.0 \| - \|
	\| 0.7368 \| 21800 \| 0.0 \| - \|
	\| 0.7385 \| 21850 \| 0.0 \| - \|
	\| 0.7402 \| 21900 \| 0.0 \| - \|
	\| 0.7419 \| 21950 \| 0.0 \| - \|
	\| 0.7436 \| 22000 \| 0.0 \| 0.0766 \|
	\| 0.7453 \| 22050 \| 0.0 \| - \|
	\| 0.7470 \| 22100 \| 0.0 \| - \|
	\| 0.7487 \| 22150 \| 0.0 \| - \|
	\| 0.7504 \| 22200 \| 0.0 \| - \|
	\| 0.7520 \| 22250 \| 0.0 \| - \|
	\| 0.7537 \| 22300 \| 0.0 \| - \|
	\| 0.7554 \| 22350 \| 0.0 \| - \|
	\| 0.7571 \| 22400 \| 0.0 \| - \|
	\| 0.7588 \| 22450 \| 0.0 \| - \|
	\| 0.7605 \| 22500 \| 0.0 \| 0.0757 \|
	\| 0.7622 \| 22550 \| 0.0 \| - \|
	\| 0.7639 \| 22600 \| 0.0 \| - \|
	\| 0.7656 \| 22650 \| 0.0 \| - \|
	\| 0.7673 \| 22700 \| 0.0 \| - \|
	\| 0.7689 \| 22750 \| 0.0 \| - \|
	\| 0.7706 \| 22800 \| 0.0 \| - \|
	\| 0.7723 \| 22850 \| 0.0 \| - \|
	\| 0.7740 \| 22900 \| 0.0 \| - \|
	\| 0.7757 \| 22950 \| 0.0 \| - \|
	\| 0.7774 \| 23000 \| 0.0 \| 0.0755 \|
	\| 0.7791 \| 23050 \| 0.0 \| - \|
	\| 0.7808 \| 23100 \| 0.0 \| - \|
	\| 0.7825 \| 23150 \| 0.0 \| - \|
	\| 0.7842 \| 23200 \| 0.0 \| - \|
	\| 0.7858 \| 23250 \| 0.0 \| - \|
	\| 0.7875 \| 23300 \| 0.0 \| - \|
	\| 0.7892 \| 23350 \| 0.0 \| - \|
	\| 0.7909 \| 23400 \| 0.0 \| - \|
	\| 0.7926 \| 23450 \| 0.0 \| - \|
	\| 0.7943 \| 23500 \| 0.0 \| 0.076 \|
	\| 0.7960 \| 23550 \| 0.0 \| - \|
	\| 0.7977 \| 23600 \| 0.0 \| - \|
	\| 0.7994 \| 23650 \| 0.0 \| - \|
	\| 0.8011 \| 23700 \| 0.0 \| - \|
	\| 0.8027 \| 23750 \| 0.0 \| - \|
	\| 0.8044 \| 23800 \| 0.0 \| - \|
	\| 0.8061 \| 23850 \| 0.0 \| - \|
	\| 0.8078 \| 23900 \| 0.0 \| - \|
	\| 0.8095 \| 23950 \| 0.0 \| - \|
	\| 0.8112 \| 24000 \| 0.0 \| 0.0756 \|
	\| 0.8129 \| 24050 \| 0.0 \| - \|
	\| 0.8146 \| 24100 \| 0.0 \| - \|
	\| 0.8163 \| 24150 \| 0.0 \| - \|
	\| 0.8180 \| 24200 \| 0.0 \| - \|
	\| 0.8196 \| 24250 \| 0.0 \| - \|
	\| 0.8213 \| 24300 \| 0.0 \| - \|
	\| 0.8230 \| 24350 \| 0.0 \| - \|
	\| 0.8247 \| 24400 \| 0.0 \| - \|
	\| 0.8264 \| 24450 \| 0.0 \| - \|
	\| 0.8281 \| 24500 \| 0.0 \| 0.0759 \|
	\| 0.8298 \| 24550 \| 0.0 \| - \|
	\| 0.8315 \| 24600 \| 0.0 \| - \|
	\| 0.8332 \| 24650 \| 0.0 \| - \|
	\| 0.8349 \| 24700 \| 0.0 \| - \|
	\| 0.8365 \| 24750 \| 0.0 \| - \|
	\| 0.8382 \| 24800 \| 0.0 \| - \|
	\| 0.8399 \| 24850 \| 0.0 \| - \|
	\| 0.8416 \| 24900 \| 0.0 \| - \|
	\| 0.8433 \| 24950 \| 0.0 \| - \|
	\| 0.8450 \| 25000 \| 0.0 \| 0.0762 \|
	\| 0.8467 \| 25050 \| 0.0 \| - \|
	\| 0.8484 \| 25100 \| 0.0 \| - \|
	\| 0.8501 \| 25150 \| 0.0 \| - \|
	\| 0.8518 \| 25200 \| 0.0 \| - \|
	\| 0.8534 \| 25250 \| 0.0 \| - \|
	\| 0.8551 \| 25300 \| 0.0 \| - \|
	\| 0.8568 \| 25350 \| 0.0 \| - \|
	\| 0.8585 \| 25400 \| 0.0 \| - \|
	\| 0.8602 \| 25450 \| 0.0 \| - \|
	\| 0.8619 \| 25500 \| 0.0 \| 0.0733 \|
	\| 0.8636 \| 25550 \| 0.0 \| - \|
	\| 0.8653 \| 25600 \| 0.0 \| - \|
	\| 0.8670 \| 25650 \| 0.0 \| - \|
	\| 0.8687 \| 25700 \| 0.0 \| - \|
	\| 0.8703 \| 25750 \| 0.0 \| - \|
	\| 0.8720 \| 25800 \| 0.0 \| - \|
	\| 0.8737 \| 25850 \| 0.0 \| - \|
	\| 0.8754 \| 25900 \| 0.0 \| - \|
	\| 0.8771 \| 25950 \| 0.0 \| - \|
	\| 0.8788 \| 26000 \| 0.0 \| 0.0742 \|
	\| 0.8805 \| 26050 \| 0.0 \| - \|
	\| 0.8822 \| 26100 \| 0.0 \| - \|
	\| 0.8839 \| 26150 \| 0.0 \| - \|
	\| 0.8856 \| 26200 \| 0.0 \| - \|
	\| 0.8872 \| 26250 \| 0.0 \| - \|
	\| 0.8889 \| 26300 \| 0.0 \| - \|
	\| 0.8906 \| 26350 \| 0.0 \| - \|
	\| 0.8923 \| 26400 \| 0.0 \| - \|
	\| 0.8940 \| 26450 \| 0.0 \| - \|
	\| 0.8957 \| 26500 \| 0.0 \| 0.0756 \|
	\| 0.8974 \| 26550 \| 0.0 \| - \|
	\| 0.8991 \| 26600 \| 0.0 \| - \|
	\| 0.9008 \| 26650 \| 0.0 \| - \|
	\| 0.9025 \| 26700 \| 0.0 \| - \|
	\| 0.9041 \| 26750 \| 0.0 \| - \|
	\| 0.9058 \| 26800 \| 0.0 \| - \|
	\| 0.9075 \| 26850 \| 0.0 \| - \|
	\| 0.9092 \| 26900 \| 0.0 \| - \|
	\| 0.9109 \| 26950 \| 0.0 \| - \|
	\| 0.9126 \| 27000 \| 0.0 \| 0.0751 \|
	\| 0.9143 \| 27050 \| 0.0 \| - \|
	\| 0.9160 \| 27100 \| 0.0 \| - \|
	\| 0.9177 \| 27150 \| 0.0 \| - \|
	\| 0.9194 \| 27200 \| 0.0 \| - \|
	\| 0.9210 \| 27250 \| 0.0 \| - \|
	\| 0.9227 \| 27300 \| 0.0 \| - \|
	\| 0.9244 \| 27350 \| 0.0 \| - \|
	\| 0.9261 \| 27400 \| 0.0 \| - \|
	\| 0.9278 \| 27450 \| 0.0 \| - \|
	\| 0.9295 \| 27500 \| 0.0 \| 0.075 \|
	\| 0.9312 \| 27550 \| 0.0 \| - \|
	\| 0.9329 \| 27600 \| 0.0 \| - \|
	\| 0.9346 \| 27650 \| 0.0 \| - \|
	\| 0.9363 \| 27700 \| 0.0 \| - \|
	\| 0.9379 \| 27750 \| 0.0 \| - \|
	\| 0.9396 \| 27800 \| 0.0 \| - \|
	\| 0.9413 \| 27850 \| 0.0 \| - \|
	\| 0.9430 \| 27900 \| 0.0 \| - \|
	\| 0.9447 \| 27950 \| 0.0 \| - \|
	\| 0.9464 \| 28000 \| 0.0 \| 0.0725 \|
	\| 0.9481 \| 28050 \| 0.0 \| - \|
	\| 0.9498 \| 28100 \| 0.0 \| - \|
	\| 0.9515 \| 28150 \| 0.0 \| - \|
	\| 0.9532 \| 28200 \| 0.0 \| - \|
	\| 0.9548 \| 28250 \| 0.0 \| - \|
	\| 0.9565 \| 28300 \| 0.0 \| - \|
	\| 0.9582 \| 28350 \| 0.0 \| - \|
	\| 0.9599 \| 28400 \| 0.0 \| - \|
	\| 0.9616 \| 28450 \| 0.0 \| - \|
	\| 0.9633 \| 28500 \| 0.0 \| 0.0761 \|
	\| 0.9650 \| 28550 \| 0.0 \| - \|
	\| 0.9667 \| 28600 \| 0.0 \| - \|
	\| 0.9684 \| 28650 \| 0.0 \| - \|
	\| 0.9701 \| 28700 \| 0.0 \| - \|
	\| 0.9717 \| 28750 \| 0.0 \| - \|
	\| 0.9734 \| 28800 \| 0.0 \| - \|
	\| 0.9751 \| 28850 \| 0.0 \| - \|
	\| 0.9768 \| 28900 \| 0.0 \| - \|
	\| 0.9785 \| 28950 \| 0.0 \| - \|
	\| 0.9802 \| 29000 \| 0.0 \| 0.0759 \|
	\| 0.9819 \| 29050 \| 0.0 \| - \|
	\| 0.9836 \| 29100 \| 0.0 \| - \|
	\| 0.9853 \| 29150 \| 0.0 \| - \|
	\| 0.9870 \| 29200 \| 0.0 \| - \|
	\| 0.9886 \| 29250 \| 0.0 \| - \|
	\| 0.9903 \| 29300 \| 0.0 \| - \|
	\| 0.9920 \| 29350 \| 0.0 \| - \|
	\| 0.9937 \| 29400 \| 0.0 \| - \|
	\| 0.9954 \| 29450 \| 0.0 \| - \|
	\| 0.9971 \| 29500 \| 0.0 \| 0.0761 \|
	\| 0.9988 \| 29550 \| 0.0 \| - \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.10.11
	- SetFit: 1.0.1
	- Sentence Transformers: 2.2.2
	- Transformers: 4.25.1
	- PyTorch: 2.1.2
	- Datasets: 2.15.0
	- Tokenizers: 0.13.3

	## 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.
	-->