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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	widget:
	- text: It doesn't depend on hi-teck evangelism.
	- text: But in the all region we see gender unequal; in 2000 boys have education often
	then girls on 15 millions.
	- text: There is opinion, that universities should have equal amount of male and female
	students in every subject in society.
	- text: A building's style may say a lot about its history.
	- text: Manufactured goods by rail is the same amount as by road, Machinery transported
	by road has minimal percent in second chart.
	pipeline_tag: text-classification
	inference: true
	base_model: sentence-transformers/all-MiniLM-L6-v2
	model-index:
	- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.592741935483871
	name: Accuracy
	---

	# SetFit with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 256 tokens
	- Number of Classes: 5 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 \|
	\|:-----------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| Copying expression \| <ul><li>'What is needed to improve the situation with widespread using of gadjets is definite action should be encouraged and promoted by means of avoiding them.'</li><li>'Inside every of us are our passions.'</li><li>'The number of 15-59 year old people will increase for 11% but the number of 0-14 will fall and become 37%.'</li></ul> \|
	\| Synonyms \| <ul><li>'But some persons consider that the institutes should accept the equal amount of girls and boys in every faculty.'</li><li>'Nowadays problem of ecology and environment is rather acute and many people are alarmed by it.'</li><li>'The amount of people over 65 was rising between 1940 and the end of 1970s.'</li></ul> \|
	\| Tense semantics \| <ul><li>'After that the figure uncreases dramatically from 180 billions in 2009 to approximately 279 billions in 2011.'</li><li>'On the contrary, in 2014 the UK book market demonstrate minimum income, only 2,6 and 1,8 billion dollars for print book and eBook, correspondely.'</li><li>'It is not clear, what it is depends on, but after the higest point in 42% in Japan the percentage get down to 30%.'</li></ul> \|
	\| Word form transmission \| <ul><li>'A lot of people from music and cinema industry lose money due to somebody sends pirate copies to the internet.'</li><li>'The deal was worth $2 billions .'</li><li>'According to the projections numbers of people in the age of 15-60 years will show a considerable increase in 2050 by 11 per cent, such as people aged 60 and more years by 2,1 percent.'</li></ul> \|
	\| Transliteration \| <ul><li>'According to the statistic a lot of people with some horible diseases can get cvalificate help only in Japan.'</li><li>"It doesn't depend on hi-teck evangelism."</li><li>'GMO technologies are believed to be dangerous.'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.5927 \|

	## 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("Zlovoblachko/L1-classifier")
	# Run inference
	preds = model("It doesn't depend on hi-teck evangelism.")
	```

	<!--
	### 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 \| 2 \| 20.788 \| 54 \|

	\| Label \| Training Sample Count \|
	\|:-----------------------\|:----------------------\|
	\| Synonyms \| 91 \|
	\| Copying expression \| 55 \|
	\| Tense semantics \| 57 \|
	\| Word form transmission \| 32 \|
	\| Transliteration \| 15 \|

	### Training Hyperparameters
	- batch_size: (32, 32)
	- num_epochs: (15, 15)
	- 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 \|
	\|:-------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0043 \| 1 \| 0.3438 \| - \|
	\| 0.0342 \| 50 \| 0.2906 \| - \|
	\| 0.0685 \| 100 \| 0.2761 \| - \|
	\| 0.1027 \| 150 \| 0.2696 \| - \|
	\| 0.1370 \| 200 \| 0.2381 \| - \|
	\| 0.1712 \| 250 \| 0.2542 \| - \|
	\| 0.2055 \| 300 \| 0.1781 \| - \|
	\| 0.2397 \| 350 \| 0.2067 \| - \|
	\| 0.2740 \| 400 \| 0.222 \| - \|
	\| 0.3082 \| 450 \| 0.2372 \| - \|
	\| 0.3425 \| 500 \| 0.193 \| - \|
	\| 0.3767 \| 550 \| 0.2399 \| - \|
	\| 0.4110 \| 600 \| 0.1712 \| - \|
	\| 0.4452 \| 650 \| 0.1697 \| - \|
	\| 0.4795 \| 700 \| 0.1507 \| - \|
	\| 0.5137 \| 750 \| 0.0947 \| - \|
	\| 0.5479 \| 800 \| 0.0722 \| - \|
	\| 0.5822 \| 850 \| 0.0975 \| - \|
	\| 0.6164 \| 900 \| 0.035 \| - \|
	\| 0.6507 \| 950 \| 0.0114 \| - \|
	\| 0.6849 \| 1000 \| 0.0332 \| - \|
	\| 0.7192 \| 1050 \| 0.0274 \| - \|
	\| 0.7534 \| 1100 \| 0.0126 \| - \|
	\| 0.7877 \| 1150 \| 0.0267 \| - \|
	\| 0.8219 \| 1200 \| 0.0194 \| - \|
	\| 0.8562 \| 1250 \| 0.0206 \| - \|
	\| 0.8904 \| 1300 \| 0.0228 \| - \|
	\| 0.9247 \| 1350 \| 0.0076 \| - \|
	\| 0.9589 \| 1400 \| 0.0342 \| - \|
	\| 0.9932 \| 1450 \| 0.0252 \| - \|
	\| 1.0274 \| 1500 \| 0.0164 \| - \|
	\| 1.0616 \| 1550 \| 0.0049 \| - \|
	\| 1.0959 \| 1600 \| 0.0043 \| - \|
	\| 1.1301 \| 1650 \| 0.0114 \| - \|
	\| 1.1644 \| 1700 \| 0.03 \| - \|
	\| 1.1986 \| 1750 \| 0.0026 \| - \|
	\| 1.2329 \| 1800 \| 0.0012 \| - \|
	\| 1.2671 \| 1850 \| 0.0073 \| - \|
	\| 1.3014 \| 1900 \| 0.0146 \| - \|
	\| 1.3356 \| 1950 \| 0.001 \| - \|
	\| 1.3699 \| 2000 \| 0.0088 \| - \|
	\| 1.4041 \| 2050 \| 0.0031 \| - \|
	\| 1.4384 \| 2100 \| 0.0125 \| - \|
	\| 1.4726 \| 2150 \| 0.0357 \| - \|
	\| 1.5068 \| 2200 \| 0.0186 \| - \|
	\| 1.5411 \| 2250 \| 0.0178 \| - \|
	\| 1.5753 \| 2300 \| 0.0071 \| - \|
	\| 1.6096 \| 2350 \| 0.0186 \| - \|
	\| 1.6438 \| 2400 \| 0.0077 \| - \|
	\| 1.6781 \| 2450 \| 0.0183 \| - \|
	\| 1.7123 \| 2500 \| 0.0007 \| - \|
	\| 1.7466 \| 2550 \| 0.0007 \| - \|
	\| 1.7808 \| 2600 \| 0.0052 \| - \|
	\| 1.8151 \| 2650 \| 0.0077 \| - \|
	\| 1.8493 \| 2700 \| 0.0421 \| - \|
	\| 1.8836 \| 2750 \| 0.0272 \| - \|
	\| 1.9178 \| 2800 \| 0.0144 \| - \|
	\| 1.9521 \| 2850 \| 0.0038 \| - \|
	\| 1.9863 \| 2900 \| 0.0043 \| - \|
	\| 2.0205 \| 2950 \| 0.0187 \| - \|
	\| 2.0548 \| 3000 \| 0.0075 \| - \|
	\| 2.0890 \| 3050 \| 0.0151 \| - \|
	\| 2.1233 \| 3100 \| 0.0114 \| - \|
	\| 2.1575 \| 3150 \| 0.0022 \| - \|
	\| 2.1918 \| 3200 \| 0.0007 \| - \|
	\| 2.2260 \| 3250 \| 0.0196 \| - \|
	\| 2.2603 \| 3300 \| 0.0266 \| - \|
	\| 2.2945 \| 3350 \| 0.0139 \| - \|
	\| 2.3288 \| 3400 \| 0.0169 \| - \|
	\| 2.3630 \| 3450 \| 0.0124 \| - \|
	\| 2.3973 \| 3500 \| 0.0018 \| - \|
	\| 2.4315 \| 3550 \| 0.0242 \| - \|
	\| 2.4658 \| 3600 \| 0.0402 \| - \|
	\| 2.5 \| 3650 \| 0.0015 \| - \|
	\| 2.5342 \| 3700 \| 0.0042 \| - \|
	\| 2.5685 \| 3750 \| 0.0437 \| - \|
	\| 2.6027 \| 3800 \| 0.006 \| - \|
	\| 2.6370 \| 3850 \| 0.0005 \| - \|
	\| 2.6712 \| 3900 \| 0.0118 \| - \|
	\| 2.7055 \| 3950 \| 0.0166 \| - \|
	\| 2.7397 \| 4000 \| 0.025 \| - \|
	\| 2.7740 \| 4050 \| 0.0167 \| - \|
	\| 2.8082 \| 4100 \| 0.0285 \| - \|
	\| 2.8425 \| 4150 \| 0.0048 \| - \|
	\| 2.8767 \| 4200 \| 0.0149 \| - \|
	\| 2.9110 \| 4250 \| 0.0078 \| - \|
	\| 2.9452 \| 4300 \| 0.0097 \| - \|
	\| 2.9795 \| 4350 \| 0.0068 \| - \|
	\| 3.0137 \| 4400 \| 0.0235 \| - \|
	\| 3.0479 \| 4450 \| 0.0004 \| - \|
	\| 3.0822 \| 4500 \| 0.0355 \| - \|
	\| 3.1164 \| 4550 \| 0.0237 \| - \|
	\| 3.1507 \| 4600 \| 0.0004 \| - \|
	\| 3.1849 \| 4650 \| 0.0003 \| - \|
	\| 3.2192 \| 4700 \| 0.0038 \| - \|
	\| 3.2534 \| 4750 \| 0.0002 \| - \|
	\| 3.2877 \| 4800 \| 0.0105 \| - \|
	\| 3.3219 \| 4850 \| 0.0055 \| - \|
	\| 3.3562 \| 4900 \| 0.0282 \| - \|
	\| 3.3904 \| 4950 \| 0.0105 \| - \|
	\| 3.4247 \| 5000 \| 0.0362 \| - \|
	\| 3.4589 \| 5050 \| 0.0004 \| - \|
	\| 3.4932 \| 5100 \| 0.0229 \| - \|
	\| 3.5274 \| 5150 \| 0.0092 \| - \|
	\| 3.5616 \| 5200 \| 0.033 \| - \|
	\| 3.5959 \| 5250 \| 0.0003 \| - \|
	\| 3.6301 \| 5300 \| 0.0444 \| - \|
	\| 3.6644 \| 5350 \| 0.0181 \| - \|
	\| 3.6986 \| 5400 \| 0.0254 \| - \|
	\| 3.7329 \| 5450 \| 0.0057 \| - \|
	\| 3.7671 \| 5500 \| 0.0511 \| - \|
	\| 3.8014 \| 5550 \| 0.0024 \| - \|
	\| 3.8356 \| 5600 \| 0.0195 \| - \|
	\| 3.8699 \| 5650 \| 0.0202 \| - \|
	\| 3.9041 \| 5700 \| 0.0003 \| - \|
	\| 3.9384 \| 5750 \| 0.0322 \| - \|
	\| 3.9726 \| 5800 \| 0.0123 \| - \|
	\| 4.0068 \| 5850 \| 0.0002 \| - \|
	\| 4.0411 \| 5900 \| 0.0002 \| - \|
	\| 4.0753 \| 5950 \| 0.008 \| - \|
	\| 4.1096 \| 6000 \| 0.0053 \| - \|
	\| 4.1438 \| 6050 \| 0.0003 \| - \|
	\| 4.1781 \| 6100 \| 0.0213 \| - \|
	\| 4.2123 \| 6150 \| 0.0046 \| - \|
	\| 4.2466 \| 6200 \| 0.0331 \| - \|
	\| 4.2808 \| 6250 \| 0.0078 \| - \|
	\| 4.3151 \| 6300 \| 0.0042 \| - \|
	\| 4.3493 \| 6350 \| 0.0234 \| - \|
	\| 4.3836 \| 6400 \| 0.0043 \| - \|
	\| 4.4178 \| 6450 \| 0.0253 \| - \|
	\| 4.4521 \| 6500 \| 0.0303 \| - \|
	\| 4.4863 \| 6550 \| 0.004 \| - \|
	\| 4.5205 \| 6600 \| 0.0166 \| - \|
	\| 4.5548 \| 6650 \| 0.0269 \| - \|
	\| 4.5890 \| 6700 \| 0.0079 \| - \|
	\| 4.6233 \| 6750 \| 0.0001 \| - \|
	\| 4.6575 \| 6800 \| 0.0002 \| - \|
	\| 4.6918 \| 6850 \| 0.0002 \| - \|
	\| 4.7260 \| 6900 \| 0.0199 \| - \|
	\| 4.7603 \| 6950 \| 0.0282 \| - \|
	\| 4.7945 \| 7000 \| 0.0016 \| - \|
	\| 4.8288 \| 7050 \| 0.0068 \| - \|
	\| 4.8630 \| 7100 \| 0.0054 \| - \|
	\| 4.8973 \| 7150 \| 0.036 \| - \|
	\| 4.9315 \| 7200 \| 0.0054 \| - \|
	\| 4.9658 \| 7250 \| 0.0174 \| - \|
	\| 5.0 \| 7300 \| 0.0001 \| - \|
	\| 5.0342 \| 7350 \| 0.0123 \| - \|
	\| 5.0685 \| 7400 \| 0.0218 \| - \|
	\| 5.1027 \| 7450 \| 0.0162 \| - \|
	\| 5.1370 \| 7500 \| 0.0181 \| - \|
	\| 5.1712 \| 7550 \| 0.0001 \| - \|
	\| 5.2055 \| 7600 \| 0.0201 \| - \|
	\| 5.2397 \| 7650 \| 0.0232 \| - \|
	\| 5.2740 \| 7700 \| 0.0003 \| - \|
	\| 5.3082 \| 7750 \| 0.0002 \| - \|
	\| 5.3425 \| 7800 \| 0.0094 \| - \|
	\| 5.3767 \| 7850 \| 0.0151 \| - \|
	\| 5.4110 \| 7900 \| 0.0099 \| - \|
	\| 5.4452 \| 7950 \| 0.01 \| - \|
	\| 5.4795 \| 8000 \| 0.0378 \| - \|
	\| 5.5137 \| 8050 \| 0.0199 \| - \|
	\| 5.5479 \| 8100 \| 0.0201 \| - \|
	\| 5.5822 \| 8150 \| 0.0242 \| - \|
	\| 5.6164 \| 8200 \| 0.0015 \| - \|
	\| 5.6507 \| 8250 \| 0.0002 \| - \|
	\| 5.6849 \| 8300 \| 0.0047 \| - \|
	\| 5.7192 \| 8350 \| 0.0002 \| - \|
	\| 5.7534 \| 8400 \| 0.0001 \| - \|
	\| 5.7877 \| 8450 \| 0.0215 \| - \|
	\| 5.8219 \| 8500 \| 0.0159 \| - \|
	\| 5.8562 \| 8550 \| 0.0001 \| - \|
	\| 5.8904 \| 8600 \| 0.0194 \| - \|
	\| 5.9247 \| 8650 \| 0.0058 \| - \|
	\| 5.9589 \| 8700 \| 0.0001 \| - \|
	\| 5.9932 \| 8750 \| 0.0164 \| - \|
	\| 6.0274 \| 8800 \| 0.0272 \| - \|
	\| 6.0616 \| 8850 \| 0.0001 \| - \|
	\| 6.0959 \| 8900 \| 0.0031 \| - \|
	\| 6.1301 \| 8950 \| 0.0154 \| - \|
	\| 6.1644 \| 9000 \| 0.0403 \| - \|
	\| 6.1986 \| 9050 \| 0.0035 \| - \|
	\| 6.2329 \| 9100 \| 0.0001 \| - \|
	\| 6.2671 \| 9150 \| 0.0061 \| - \|
	\| 6.3014 \| 9200 \| 0.0118 \| - \|
	\| 6.3356 \| 9250 \| 0.0031 \| - \|
	\| 6.3699 \| 9300 \| 0.0001 \| - \|
	\| 6.4041 \| 9350 \| 0.0098 \| - \|
	\| 6.4384 \| 9400 \| 0.0001 \| - \|
	\| 6.4726 \| 9450 \| 0.0343 \| - \|
	\| 6.5068 \| 9500 \| 0.017 \| - \|
	\| 6.5411 \| 9550 \| 0.0025 \| - \|
	\| 6.5753 \| 9600 \| 0.0001 \| - \|
	\| 6.6096 \| 9650 \| 0.0181 \| - \|
	\| 6.6438 \| 9700 \| 0.0191 \| - \|
	\| 6.6781 \| 9750 \| 0.0186 \| - \|
	\| 6.7123 \| 9800 \| 0.0001 \| - \|
	\| 6.7466 \| 9850 \| 0.0002 \| - \|
	\| 6.7808 \| 9900 \| 0.0001 \| - \|
	\| 6.8151 \| 9950 \| 0.0086 \| - \|
	\| 6.8493 \| 10000 \| 0.0377 \| - \|
	\| 6.8836 \| 10050 \| 0.0167 \| - \|
	\| 6.9178 \| 10100 \| 0.0034 \| - \|
	\| 6.9521 \| 10150 \| 0.0054 \| - \|
	\| 6.9863 \| 10200 \| 0.0048 \| - \|
	\| 7.0205 \| 10250 \| 0.0219 \| - \|
	\| 7.0548 \| 10300 \| 0.0001 \| - \|
	\| 7.0890 \| 10350 \| 0.0001 \| - \|
	\| 7.1233 \| 10400 \| 0.0262 \| - \|
	\| 7.1575 \| 10450 \| 0.0069 \| - \|
	\| 7.1918 \| 10500 \| 0.0001 \| - \|
	\| 7.2260 \| 10550 \| 0.0158 \| - \|
	\| 7.2603 \| 10600 \| 0.0192 \| - \|
	\| 7.2945 \| 10650 \| 0.0098 \| - \|
	\| 7.3288 \| 10700 \| 0.0001 \| - \|
	\| 7.3630 \| 10750 \| 0.0002 \| - \|
	\| 7.3973 \| 10800 \| 0.0021 \| - \|
	\| 7.4315 \| 10850 \| 0.0252 \| - \|
	\| 7.4658 \| 10900 \| 0.0383 \| - \|
	\| 7.5 \| 10950 \| 0.0001 \| - \|
	\| 7.5342 \| 11000 \| 0.0001 \| - \|
	\| 7.5685 \| 11050 \| 0.0491 \| - \|
	\| 7.6027 \| 11100 \| 0.0076 \| - \|
	\| 7.6370 \| 11150 \| 0.0089 \| - \|
	\| 7.6712 \| 11200 \| 0.0162 \| - \|
	\| 7.7055 \| 11250 \| 0.0163 \| - \|
	\| 7.7397 \| 11300 \| 0.0188 \| - \|
	\| 7.7740 \| 11350 \| 0.0141 \| - \|
	\| 7.8082 \| 11400 \| 0.0277 \| - \|
	\| 7.8425 \| 11450 \| 0.0001 \| - \|
	\| 7.8767 \| 11500 \| 0.0001 \| - \|
	\| 7.9110 \| 11550 \| 0.0055 \| - \|
	\| 7.9452 \| 11600 \| 0.0029 \| - \|
	\| 7.9795 \| 11650 \| 0.0001 \| - \|
	\| 8.0137 \| 11700 \| 0.0186 \| - \|
	\| 8.0479 \| 11750 \| 0.0037 \| - \|
	\| 8.0822 \| 11800 \| 0.0205 \| - \|
	\| 8.1164 \| 11850 \| 0.0217 \| - \|
	\| 8.1507 \| 11900 \| 0.0036 \| - \|
	\| 8.1849 \| 11950 \| 0.0039 \| - \|
	\| 8.2192 \| 12000 \| 0.0001 \| - \|
	\| 8.2534 \| 12050 \| 0.0055 \| - \|
	\| 8.2877 \| 12100 \| 0.0027 \| - \|
	\| 8.3219 \| 12150 \| 0.0029 \| - \|
	\| 8.3562 \| 12200 \| 0.0279 \| - \|
	\| 8.3904 \| 12250 \| 0.0139 \| - \|
	\| 8.4247 \| 12300 \| 0.04 \| - \|
	\| 8.4589 \| 12350 \| 0.003 \| - \|
	\| 8.4932 \| 12400 \| 0.0161 \| - \|
	\| 8.5274 \| 12450 \| 0.0001 \| - \|
	\| 8.5616 \| 12500 \| 0.035 \| - \|
	\| 8.5959 \| 12550 \| 0.0021 \| - \|
	\| 8.6301 \| 12600 \| 0.0355 \| - \|
	\| 8.6644 \| 12650 \| 0.0139 \| - \|
	\| 8.6986 \| 12700 \| 0.0183 \| - \|
	\| 8.7329 \| 12750 \| 0.0041 \| - \|
	\| 8.7671 \| 12800 \| 0.0354 \| - \|
	\| 8.8014 \| 12850 \| 0.0 \| - \|
	\| 8.8356 \| 12900 \| 0.0197 \| - \|
	\| 8.8699 \| 12950 \| 0.0189 \| - \|
	\| 8.9041 \| 13000 \| 0.0063 \| - \|
	\| 8.9384 \| 13050 \| 0.0309 \| - \|
	\| 8.9726 \| 13100 \| 0.0029 \| - \|
	\| 9.0068 \| 13150 \| 0.0027 \| - \|
	\| 9.0411 \| 13200 \| 0.0018 \| - \|
	\| 9.0753 \| 13250 \| 0.0104 \| - \|
	\| 9.1096 \| 13300 \| 0.0057 \| - \|
	\| 9.1438 \| 13350 \| 0.0051 \| - \|
	\| 9.1781 \| 13400 \| 0.0172 \| - \|
	\| 9.2123 \| 13450 \| 0.0001 \| - \|
	\| 9.2466 \| 13500 \| 0.0347 \| - \|
	\| 9.2808 \| 13550 \| 0.0024 \| - \|
	\| 9.3151 \| 13600 \| 0.0147 \| - \|
	\| 9.3493 \| 13650 \| 0.0218 \| - \|
	\| 9.3836 \| 13700 \| 0.0028 \| - \|
	\| 9.4178 \| 13750 \| 0.0205 \| - \|
	\| 9.4521 \| 13800 \| 0.0215 \| - \|
	\| 9.4863 \| 13850 \| 0.0001 \| - \|
	\| 9.5205 \| 13900 \| 0.0157 \| - \|
	\| 9.5548 \| 13950 \| 0.0227 \| - \|
	\| 9.5890 \| 14000 \| 0.0001 \| - \|
	\| 9.6233 \| 14050 \| 0.0048 \| - \|
	\| 9.6575 \| 14100 \| 0.0106 \| - \|
	\| 9.6918 \| 14150 \| 0.0077 \| - \|
	\| 9.7260 \| 14200 \| 0.0225 \| - \|
	\| 9.7603 \| 14250 \| 0.0173 \| - \|
	\| 9.7945 \| 14300 \| 0.0028 \| - \|
	\| 9.8288 \| 14350 \| 0.0022 \| - \|
	\| 9.8630 \| 14400 \| 0.003 \| - \|
	\| 9.8973 \| 14450 \| 0.0355 \| - \|
	\| 9.9315 \| 14500 \| 0.0001 \| - \|
	\| 9.9658 \| 14550 \| 0.0187 \| - \|
	\| 10.0 \| 14600 \| 0.0001 \| - \|
	\| 0.0007 \| 1 \| 0.0055 \| - \|
	\| 0.0342 \| 50 \| 0.0127 \| - \|
	\| 0.0685 \| 100 \| 0.0206 \| - \|
	\| 0.1027 \| 150 \| 0.0195 \| - \|
	\| 0.1370 \| 200 \| 0.0238 \| - \|
	\| 0.1712 \| 250 \| 0.0029 \| - \|
	\| 0.2055 \| 300 \| 0.0204 \| - \|
	\| 0.2397 \| 350 \| 0.0174 \| - \|
	\| 0.2740 \| 400 \| 0.0001 \| - \|
	\| 0.3082 \| 450 \| 0.0023 \| - \|
	\| 0.3425 \| 500 \| 0.0001 \| - \|
	\| 0.3767 \| 550 \| 0.0254 \| - \|
	\| 0.4110 \| 600 \| 0.0029 \| - \|
	\| 0.4452 \| 650 \| 0.0082 \| - \|
	\| 0.4795 \| 700 \| 0.0411 \| - \|
	\| 0.5137 \| 750 \| 0.0159 \| - \|
	\| 0.5479 \| 800 \| 0.0207 \| - \|
	\| 0.5822 \| 850 \| 0.0173 \| - \|
	\| 0.6164 \| 900 \| 0.0001 \| - \|
	\| 0.6507 \| 950 \| 0.0018 \| - \|
	\| 0.6849 \| 1000 \| 0.0059 \| - \|
	\| 0.7192 \| 1050 \| 0.0014 \| - \|
	\| 0.7534 \| 1100 \| 0.0022 \| - \|
	\| 0.7877 \| 1150 \| 0.0187 \| - \|
	\| 0.8219 \| 1200 \| 0.0158 \| - \|
	\| 0.8562 \| 1250 \| 0.0025 \| - \|
	\| 0.8904 \| 1300 \| 0.0113 \| - \|
	\| 0.9247 \| 1350 \| 0.0007 \| - \|
	\| 0.9589 \| 1400 \| 0.004 \| - \|
	\| 0.9932 \| 1450 \| 0.0216 \| - \|
	\| 1.0274 \| 1500 \| 0.0213 \| - \|
	\| 1.0616 \| 1550 \| 0.0044 \| - \|
	\| 1.0959 \| 1600 \| 0.0025 \| - \|
	\| 1.1301 \| 1650 \| 0.0154 \| - \|
	\| 1.1644 \| 1700 \| 0.038 \| - \|
	\| 1.1986 \| 1750 \| 0.0001 \| - \|
	\| 1.2329 \| 1800 \| 0.0004 \| - \|
	\| 1.2671 \| 1850 \| 0.0065 \| - \|
	\| 1.3014 \| 1900 \| 0.0087 \| - \|
	\| 1.3356 \| 1950 \| 0.0001 \| - \|
	\| 1.3699 \| 2000 \| 0.0039 \| - \|
	\| 1.4041 \| 2050 \| 0.0005 \| - \|
	\| 1.4384 \| 2100 \| 0.0087 \| - \|
	\| 1.4726 \| 2150 \| 0.0369 \| - \|
	\| 1.5068 \| 2200 \| 0.0157 \| - \|
	\| 1.5411 \| 2250 \| 0.0094 \| - \|
	\| 1.5753 \| 2300 \| 0.0042 \| - \|
	\| 1.6096 \| 2350 \| 0.018 \| - \|
	\| 1.6438 \| 2400 \| 0.014 \| - \|
	\| 1.6781 \| 2450 \| 0.0161 \| - \|
	\| 1.7123 \| 2500 \| 0.0011 \| - \|
	\| 1.7466 \| 2550 \| 0.0001 \| - \|
	\| 1.7808 \| 2600 \| 0.004 \| - \|
	\| 1.8151 \| 2650 \| 0.0048 \| - \|
	\| 1.8493 \| 2700 \| 0.0403 \| - \|
	\| 1.8836 \| 2750 \| 0.0254 \| - \|
	\| 1.9178 \| 2800 \| 0.0124 \| - \|
	\| 1.9521 \| 2850 \| 0.0028 \| - \|
	\| 1.9863 \| 2900 \| 0.0026 \| - \|
	\| 2.0205 \| 2950 \| 0.0171 \| - \|
	\| 2.0548 \| 3000 \| 0.0049 \| - \|
	\| 2.0890 \| 3050 \| 0.0092 \| - \|
	\| 2.1233 \| 3100 \| 0.0134 \| - \|
	\| 2.1575 \| 3150 \| 0.0021 \| - \|
	\| 2.1918 \| 3200 \| 0.0001 \| - \|
	\| 2.2260 \| 3250 \| 0.0153 \| - \|
	\| 2.2603 \| 3300 \| 0.0253 \| - \|
	\| 2.2945 \| 3350 \| 0.0095 \| - \|
	\| 2.3288 \| 3400 \| 0.0144 \| - \|
	\| 2.3630 \| 3450 \| 0.0064 \| - \|
	\| 2.3973 \| 3500 \| 0.0013 \| - \|
	\| 2.4315 \| 3550 \| 0.0216 \| - \|
	\| 2.4658 \| 3600 \| 0.0387 \| - \|
	\| 2.5 \| 3650 \| 0.0018 \| - \|
	\| 2.5342 \| 3700 \| 0.0034 \| - \|
	\| 2.5685 \| 3750 \| 0.0428 \| - \|
	\| 2.6027 \| 3800 \| 0.0055 \| - \|
	\| 2.6370 \| 3850 \| 0.0001 \| - \|
	\| 2.6712 \| 3900 \| 0.0154 \| - \|
	\| 2.7055 \| 3950 \| 0.0176 \| - \|
	\| 2.7397 \| 4000 \| 0.0213 \| - \|
	\| 2.7740 \| 4050 \| 0.016 \| - \|
	\| 2.8082 \| 4100 \| 0.0293 \| - \|
	\| 2.8425 \| 4150 \| 0.0034 \| - \|
	\| 2.8767 \| 4200 \| 0.0119 \| - \|
	\| 2.9110 \| 4250 \| 0.0061 \| - \|
	\| 2.9452 \| 4300 \| 0.0068 \| - \|
	\| 2.9795 \| 4350 \| 0.006 \| - \|
	\| 3.0137 \| 4400 \| 0.0211 \| - \|
	\| 3.0479 \| 4450 \| 0.0001 \| - \|
	\| 3.0822 \| 4500 \| 0.0303 \| - \|
	\| 3.1164 \| 4550 \| 0.0225 \| - \|
	\| 3.1507 \| 4600 \| 0.0001 \| - \|
	\| 3.1849 \| 4650 \| 0.0002 \| - \|
	\| 3.2192 \| 4700 \| 0.0031 \| - \|
	\| 3.2534 \| 4750 \| 0.0001 \| - \|
	\| 3.2877 \| 4800 \| 0.0103 \| - \|
	\| 3.3219 \| 4850 \| 0.0055 \| - \|
	\| 3.3562 \| 4900 \| 0.0297 \| - \|
	\| 3.3904 \| 4950 \| 0.0121 \| - \|
	\| 3.4247 \| 5000 \| 0.0348 \| - \|
	\| 3.4589 \| 5050 \| 0.0003 \| - \|
	\| 3.4932 \| 5100 \| 0.0212 \| - \|
	\| 3.5274 \| 5150 \| 0.0077 \| - \|
	\| 3.5616 \| 5200 \| 0.0339 \| - \|
	\| 3.5959 \| 5250 \| 0.0001 \| - \|
	\| 3.6301 \| 5300 \| 0.0444 \| - \|
	\| 3.6644 \| 5350 \| 0.0167 \| - \|
	\| 3.6986 \| 5400 \| 0.0245 \| - \|
	\| 3.7329 \| 5450 \| 0.005 \| - \|
	\| 3.7671 \| 5500 \| 0.047 \| - \|
	\| 3.8014 \| 5550 \| 0.0021 \| - \|
	\| 3.8356 \| 5600 \| 0.019 \| - \|
	\| 3.8699 \| 5650 \| 0.0187 \| - \|
	\| 3.9041 \| 5700 \| 0.0001 \| - \|
	\| 3.9384 \| 5750 \| 0.0328 \| - \|
	\| 3.9726 \| 5800 \| 0.0097 \| - \|
	\| 4.0068 \| 5850 \| 0.0001 \| - \|
	\| 4.0411 \| 5900 \| 0.0001 \| - \|
	\| 4.0753 \| 5950 \| 0.0078 \| - \|
	\| 4.1096 \| 6000 \| 0.0057 \| - \|
	\| 4.1438 \| 6050 \| 0.0002 \| - \|
	\| 4.1781 \| 6100 \| 0.0218 \| - \|
	\| 4.2123 \| 6150 \| 0.0038 \| - \|
	\| 4.2466 \| 6200 \| 0.0337 \| - \|
	\| 4.2808 \| 6250 \| 0.0065 \| - \|
	\| 4.3151 \| 6300 \| 0.0033 \| - \|
	\| 4.3493 \| 6350 \| 0.0228 \| - \|
	\| 4.3836 \| 6400 \| 0.0033 \| - \|
	\| 4.4178 \| 6450 \| 0.0244 \| - \|
	\| 4.4521 \| 6500 \| 0.027 \| - \|
	\| 4.4863 \| 6550 \| 0.0027 \| - \|
	\| 4.5205 \| 6600 \| 0.0153 \| - \|
	\| 4.5548 \| 6650 \| 0.0241 \| - \|
	\| 4.5890 \| 6700 \| 0.0071 \| - \|
	\| 4.6233 \| 6750 \| 0.0001 \| - \|
	\| 4.6575 \| 6800 \| 0.0 \| - \|
	\| 4.6918 \| 6850 \| 0.0001 \| - \|
	\| 4.7260 \| 6900 \| 0.0203 \| - \|
	\| 4.7603 \| 6950 \| 0.0273 \| - \|
	\| 4.7945 \| 7000 \| 0.0017 \| - \|
	\| 4.8288 \| 7050 \| 0.0062 \| - \|
	\| 4.8630 \| 7100 \| 0.0043 \| - \|
	\| 4.8973 \| 7150 \| 0.0346 \| - \|
	\| 4.9315 \| 7200 \| 0.005 \| - \|
	\| 4.9658 \| 7250 \| 0.0182 \| - \|
	\| 5.0 \| 7300 \| 0.0001 \| - \|
	\| 5.0342 \| 7350 \| 0.0108 \| - \|
	\| 5.0685 \| 7400 \| 0.0218 \| - \|
	\| 5.1027 \| 7450 \| 0.0163 \| - \|
	\| 5.1370 \| 7500 \| 0.0195 \| - \|
	\| 5.1712 \| 7550 \| 0.0001 \| - \|
	\| 5.2055 \| 7600 \| 0.0195 \| - \|
	\| 5.2397 \| 7650 \| 0.0222 \| - \|
	\| 5.2740 \| 7700 \| 0.0002 \| - \|
	\| 5.3082 \| 7750 \| 0.0001 \| - \|
	\| 5.3425 \| 7800 \| 0.0078 \| - \|
	\| 5.3767 \| 7850 \| 0.0158 \| - \|
	\| 5.4110 \| 7900 \| 0.0081 \| - \|
	\| 5.4452 \| 7950 \| 0.0087 \| - \|
	\| 5.4795 \| 8000 \| 0.0372 \| - \|
	\| 5.5137 \| 8050 \| 0.019 \| - \|
	\| 5.5479 \| 8100 \| 0.0188 \| - \|
	\| 5.5822 \| 8150 \| 0.0238 \| - \|
	\| 5.6164 \| 8200 \| 0.0018 \| - \|
	\| 5.6507 \| 8250 \| 0.0001 \| - \|
	\| 5.6849 \| 8300 \| 0.0046 \| - \|
	\| 5.7192 \| 8350 \| 0.0001 \| - \|
	\| 5.7534 \| 8400 \| 0.0001 \| - \|
	\| 5.7877 \| 8450 \| 0.0216 \| - \|
	\| 5.8219 \| 8500 \| 0.0164 \| - \|
	\| 5.8562 \| 8550 \| 0.0 \| - \|
	\| 5.8904 \| 8600 \| 0.018 \| - \|
	\| 5.9247 \| 8650 \| 0.0059 \| - \|
	\| 5.9589 \| 8700 \| 0.0001 \| - \|
	\| 5.9932 \| 8750 \| 0.0168 \| - \|
	\| 6.0274 \| 8800 \| 0.0259 \| - \|
	\| 6.0616 \| 8850 \| 0.0001 \| - \|
	\| 6.0959 \| 8900 \| 0.0029 \| - \|
	\| 6.1301 \| 8950 \| 0.0159 \| - \|
	\| 6.1644 \| 9000 \| 0.041 \| - \|
	\| 6.1986 \| 9050 \| 0.0035 \| - \|
	\| 6.2329 \| 9100 \| 0.0001 \| - \|
	\| 6.2671 \| 9150 \| 0.005 \| - \|
	\| 6.3014 \| 9200 \| 0.0101 \| - \|
	\| 6.3356 \| 9250 \| 0.0027 \| - \|
	\| 6.3699 \| 9300 \| 0.0 \| - \|
	\| 6.4041 \| 9350 \| 0.0094 \| - \|
	\| 6.4384 \| 9400 \| 0.0001 \| - \|
	\| 6.4726 \| 9450 \| 0.0335 \| - \|
	\| 6.5068 \| 9500 \| 0.0168 \| - \|
	\| 6.5411 \| 9550 \| 0.0025 \| - \|
	\| 6.5753 \| 9600 \| 0.0001 \| - \|
	\| 6.6096 \| 9650 \| 0.0185 \| - \|
	\| 6.6438 \| 9700 \| 0.0188 \| - \|
	\| 6.6781 \| 9750 \| 0.0187 \| - \|
	\| 6.7123 \| 9800 \| 0.0001 \| - \|
	\| 6.7466 \| 9850 \| 0.0002 \| - \|
	\| 6.7808 \| 9900 \| 0.0001 \| - \|
	\| 6.8151 \| 9950 \| 0.0087 \| - \|
	\| 6.8493 \| 10000 \| 0.0371 \| - \|
	\| 6.8836 \| 10050 \| 0.0172 \| - \|
	\| 6.9178 \| 10100 \| 0.0028 \| - \|
	\| 6.9521 \| 10150 \| 0.0055 \| - \|
	\| 6.9863 \| 10200 \| 0.0043 \| - \|
	\| 7.0205 \| 10250 \| 0.0219 \| - \|
	\| 7.0548 \| 10300 \| 0.0 \| - \|
	\| 7.0890 \| 10350 \| 0.0001 \| - \|
	\| 7.1233 \| 10400 \| 0.026 \| - \|
	\| 7.1575 \| 10450 \| 0.0067 \| - \|
	\| 7.1918 \| 10500 \| 0.0001 \| - \|
	\| 7.2260 \| 10550 \| 0.0162 \| - \|
	\| 7.2603 \| 10600 \| 0.019 \| - \|
	\| 7.2945 \| 10650 \| 0.0093 \| - \|
	\| 7.3288 \| 10700 \| 0.0001 \| - \|
	\| 7.3630 \| 10750 \| 0.0002 \| - \|
	\| 7.3973 \| 10800 \| 0.002 \| - \|
	\| 7.4315 \| 10850 \| 0.0247 \| - \|
	\| 7.4658 \| 10900 \| 0.0394 \| - \|
	\| 7.5 \| 10950 \| 0.0001 \| - \|
	\| 7.5342 \| 11000 \| 0.0001 \| - \|
	\| 7.5685 \| 11050 \| 0.0503 \| - \|
	\| 7.6027 \| 11100 \| 0.0066 \| - \|
	\| 7.6370 \| 11150 \| 0.0087 \| - \|
	\| 7.6712 \| 11200 \| 0.0165 \| - \|
	\| 7.7055 \| 11250 \| 0.0164 \| - \|
	\| 7.7397 \| 11300 \| 0.019 \| - \|
	\| 7.7740 \| 11350 \| 0.0143 \| - \|
	\| 7.8082 \| 11400 \| 0.0282 \| - \|
	\| 7.8425 \| 11450 \| 0.0001 \| - \|
	\| 7.8767 \| 11500 \| 0.0 \| - \|
	\| 7.9110 \| 11550 \| 0.0049 \| - \|
	\| 7.9452 \| 11600 \| 0.0028 \| - \|
	\| 7.9795 \| 11650 \| 0.0001 \| - \|
	\| 8.0137 \| 11700 \| 0.0184 \| - \|
	\| 8.0479 \| 11750 \| 0.0038 \| - \|
	\| 8.0822 \| 11800 \| 0.0211 \| - \|
	\| 8.1164 \| 11850 \| 0.0217 \| - \|
	\| 8.1507 \| 11900 \| 0.0035 \| - \|
	\| 8.1849 \| 11950 \| 0.0039 \| - \|
	\| 8.2192 \| 12000 \| 0.0 \| - \|
	\| 8.2534 \| 12050 \| 0.0055 \| - \|
	\| 8.2877 \| 12100 \| 0.0027 \| - \|
	\| 8.3219 \| 12150 \| 0.0031 \| - \|
	\| 8.3562 \| 12200 \| 0.0271 \| - \|
	\| 8.3904 \| 12250 \| 0.0138 \| - \|
	\| 8.4247 \| 12300 \| 0.0413 \| - \|
	\| 8.4589 \| 12350 \| 0.0029 \| - \|
	\| 8.4932 \| 12400 \| 0.0161 \| - \|
	\| 8.5274 \| 12450 \| 0.0 \| - \|
	\| 8.5616 \| 12500 \| 0.0352 \| - \|
	\| 8.5959 \| 12550 \| 0.0018 \| - \|
	\| 8.6301 \| 12600 \| 0.0363 \| - \|
	\| 8.6644 \| 12650 \| 0.0136 \| - \|
	\| 8.6986 \| 12700 \| 0.0175 \| - \|
	\| 8.7329 \| 12750 \| 0.0045 \| - \|
	\| 8.7671 \| 12800 \| 0.036 \| - \|
	\| 8.8014 \| 12850 \| 0.0001 \| - \|
	\| 8.8356 \| 12900 \| 0.0188 \| - \|
	\| 8.8699 \| 12950 \| 0.0192 \| - \|
	\| 8.9041 \| 13000 \| 0.0059 \| - \|
	\| 8.9384 \| 13050 \| 0.0298 \| - \|
	\| 8.9726 \| 13100 \| 0.0026 \| - \|
	\| 9.0068 \| 13150 \| 0.0027 \| - \|
	\| 9.0411 \| 13200 \| 0.0017 \| - \|
	\| 9.0753 \| 13250 \| 0.0103 \| - \|
	\| 9.1096 \| 13300 \| 0.0061 \| - \|
	\| 9.1438 \| 13350 \| 0.0043 \| - \|
	\| 9.1781 \| 13400 \| 0.0189 \| - \|
	\| 9.2123 \| 13450 \| 0.0001 \| - \|
	\| 9.2466 \| 13500 \| 0.0363 \| - \|
	\| 9.2808 \| 13550 \| 0.0019 \| - \|
	\| 9.3151 \| 13600 \| 0.0141 \| - \|
	\| 9.3493 \| 13650 \| 0.0213 \| - \|
	\| 9.3836 \| 13700 \| 0.0029 \| - \|
	\| 9.4178 \| 13750 \| 0.0217 \| - \|
	\| 9.4521 \| 13800 \| 0.0218 \| - \|
	\| 9.4863 \| 13850 \| 0.0001 \| - \|
	\| 9.5205 \| 13900 \| 0.014 \| - \|
	\| 9.5548 \| 13950 \| 0.0213 \| - \|
	\| 9.5890 \| 14000 \| 0.0 \| - \|
	\| 9.6233 \| 14050 \| 0.004 \| - \|
	\| 9.6575 \| 14100 \| 0.0112 \| - \|
	\| 9.6918 \| 14150 \| 0.0077 \| - \|
	\| 9.7260 \| 14200 \| 0.0237 \| - \|
	\| 9.7603 \| 14250 \| 0.0202 \| - \|
	\| 9.7945 \| 14300 \| 0.003 \| - \|
	\| 9.8288 \| 14350 \| 0.002 \| - \|
	\| 9.8630 \| 14400 \| 0.0028 \| - \|
	\| 9.8973 \| 14450 \| 0.0398 \| - \|
	\| 9.9315 \| 14500 \| 0.0001 \| - \|
	\| 9.9658 \| 14550 \| 0.0185 \| - \|
	\| 10.0 \| 14600 \| 0.0001 \| - \|
	\| 10.0342 \| 14650 \| 0.0102 \| - \|
	\| 10.0685 \| 14700 \| 0.0164 \| - \|
	\| 10.1027 \| 14750 \| 0.0161 \| - \|
	\| 10.1370 \| 14800 \| 0.0221 \| - \|
	\| 10.1712 \| 14850 \| 0.0016 \| - \|
	\| 10.2055 \| 14900 \| 0.0151 \| - \|
	\| 10.2397 \| 14950 \| 0.0215 \| - \|
	\| 10.2740 \| 15000 \| 0.0021 \| - \|
	\| 10.3082 \| 15050 \| 0.0075 \| - \|
	\| 10.3425 \| 15100 \| 0.0001 \| - \|
	\| 10.3767 \| 15150 \| 0.0211 \| - \|
	\| 10.4110 \| 15200 \| 0.0022 \| - \|
	\| 10.4452 \| 15250 \| 0.0001 \| - \|
	\| 10.4795 \| 15300 \| 0.0348 \| - \|
	\| 10.5137 \| 15350 \| 0.0211 \| - \|
	\| 10.5479 \| 15400 \| 0.0193 \| - \|
	\| 10.5822 \| 15450 \| 0.0203 \| - \|
	\| 10.6164 \| 15500 \| 0.0001 \| - \|
	\| 10.6507 \| 15550 \| 0.0 \| - \|
	\| 10.6849 \| 15600 \| 0.0028 \| - \|
	\| 10.7192 \| 15650 \| 0.0025 \| - \|
	\| 10.7534 \| 15700 \| 0.003 \| - \|
	\| 10.7877 \| 15750 \| 0.0199 \| - \|
	\| 10.8219 \| 15800 \| 0.0238 \| - \|
	\| 10.8562 \| 15850 \| 0.0024 \| - \|
	\| 10.8904 \| 15900 \| 0.0149 \| - \|
	\| 10.9247 \| 15950 \| 0.0019 \| - \|
	\| 10.9589 \| 16000 \| 0.0001 \| - \|
	\| 10.9932 \| 16050 \| 0.0206 \| - \|
	\| 11.0274 \| 16100 \| 0.0187 \| - \|
	\| 11.0616 \| 16150 \| 0.0025 \| - \|
	\| 11.0959 \| 16200 \| 0.0001 \| - \|
	\| 11.1301 \| 16250 \| 0.0185 \| - \|
	\| 11.1644 \| 16300 \| 0.0476 \| - \|
	\| 11.1986 \| 16350 \| 0.0027 \| - \|
	\| 11.2329 \| 16400 \| 0.0064 \| - \|
	\| 11.2671 \| 16450 \| 0.0026 \| - \|
	\| 11.3014 \| 16500 \| 0.0055 \| - \|
	\| 11.3356 \| 16550 \| 0.0024 \| - \|
	\| 11.3699 \| 16600 \| 0.0059 \| - \|
	\| 11.4041 \| 16650 \| 0.0 \| - \|
	\| 11.4384 \| 16700 \| 0.0 \| - \|
	\| 11.4726 \| 16750 \| 0.0333 \| - \|
	\| 11.5068 \| 16800 \| 0.0231 \| - \|
	\| 11.5411 \| 16850 \| 0.0084 \| - \|
	\| 11.5753 \| 16900 \| 0.0001 \| - \|
	\| 11.6096 \| 16950 \| 0.0173 \| - \|
	\| 11.6438 \| 17000 \| 0.0207 \| - \|
	\| 11.6781 \| 17050 \| 0.0162 \| - \|
	\| 11.7123 \| 17100 \| 0.0071 \| - \|
	\| 11.7466 \| 17150 \| 0.0049 \| - \|
	\| 11.7808 \| 17200 \| 0.0025 \| - \|
	\| 11.8151 \| 17250 \| 0.011 \| - \|
	\| 11.8493 \| 17300 \| 0.035 \| - \|
	\| 11.8836 \| 17350 \| 0.0168 \| - \|
	\| 11.9178 \| 17400 \| 0.0085 \| - \|
	\| 11.9521 \| 17450 \| 0.0028 \| - \|
	\| 11.9863 \| 17500 \| 0.0 \| - \|
	\| 12.0205 \| 17550 \| 0.0239 \| - \|
	\| 12.0548 \| 17600 \| 0.0026 \| - \|
	\| 12.0890 \| 17650 \| 0.008 \| - \|
	\| 12.1233 \| 17700 \| 0.0165 \| - \|
	\| 12.1575 \| 17750 \| 0.0027 \| - \|
	\| 12.1918 \| 17800 \| 0.0069 \| - \|
	\| 12.2260 \| 17850 \| 0.0215 \| - \|
	\| 12.2603 \| 17900 \| 0.0236 \| - \|
	\| 12.2945 \| 17950 \| 0.0001 \| - \|
	\| 12.3288 \| 18000 \| 0.0001 \| - \|
	\| 12.3630 \| 18050 \| 0.0025 \| - \|
	\| 12.3973 \| 18100 \| 0.0026 \| - \|
	\| 12.4315 \| 18150 \| 0.0164 \| - \|
	\| 12.4658 \| 18200 \| 0.035 \| - \|
	\| 12.5 \| 18250 \| 0.0032 \| - \|
	\| 12.5342 \| 18300 \| 0.0 \| - \|
	\| 12.5685 \| 18350 \| 0.0343 \| - \|
	\| 12.6027 \| 18400 \| 0.0024 \| - \|
	\| 12.6370 \| 18450 \| 0.0025 \| - \|
	\| 12.6712 \| 18500 \| 0.0202 \| - \|
	\| 12.7055 \| 18550 \| 0.0192 \| - \|
	\| 12.7397 \| 18600 \| 0.017 \| - \|
	\| 12.7740 \| 18650 \| 0.02 \| - \|
	\| 12.8082 \| 18700 \| 0.0321 \| - \|
	\| 12.8425 \| 18750 \| 0.0001 \| - \|
	\| 12.8767 \| 18800 \| 0.0023 \| - \|
	\| 12.9110 \| 18850 \| 0.0028 \| - \|
	\| 12.9452 \| 18900 \| 0.0 \| - \|
	\| 12.9795 \| 18950 \| 0.0 \| - \|
	\| 13.0137 \| 19000 \| 0.0267 \| - \|
	\| 13.0479 \| 19050 \| 0.0056 \| - \|
	\| 13.0822 \| 19100 \| 0.0219 \| - \|
	\| 13.1164 \| 19150 \| 0.0184 \| - \|
	\| 13.1507 \| 19200 \| 0.0028 \| - \|
	\| 13.1849 \| 19250 \| 0.0 \| - \|
	\| 13.2192 \| 19300 \| 0.005 \| - \|
	\| 13.2534 \| 19350 \| 0.0056 \| - \|
	\| 13.2877 \| 19400 \| 0.0033 \| - \|
	\| 13.3219 \| 19450 \| 0.0 \| - \|
	\| 13.3562 \| 19500 \| 0.034 \| - \|
	\| 13.3904 \| 19550 \| 0.0173 \| - \|
	\| 13.4247 \| 19600 \| 0.033 \| - \|
	\| 13.4589 \| 19650 \| 0.0025 \| - \|
	\| 13.4932 \| 19700 \| 0.0279 \| - \|
	\| 13.5274 \| 19750 \| 0.0052 \| - \|
	\| 13.5616 \| 19800 \| 0.0351 \| - \|
	\| 13.5959 \| 19850 \| 0.0 \| - \|
	\| 13.6301 \| 19900 \| 0.035 \| - \|
	\| 13.6644 \| 19950 \| 0.0069 \| - \|
	\| 13.6986 \| 20000 \| 0.0227 \| - \|
	\| 13.7329 \| 20050 \| 0.0 \| - \|
	\| 13.7671 \| 20100 \| 0.0347 \| - \|
	\| 13.8014 \| 20150 \| 0.0 \| - \|
	\| 13.8356 \| 20200 \| 0.0217 \| - \|
	\| 13.8699 \| 20250 \| 0.02 \| - \|
	\| 13.9041 \| 20300 \| 0.0 \| - \|
	\| 13.9384 \| 20350 \| 0.0393 \| - \|
	\| 13.9726 \| 20400 \| 0.0053 \| - \|
	\| 14.0068 \| 20450 \| 0.0026 \| - \|
	\| 14.0411 \| 20500 \| 0.0025 \| - \|
	\| 14.0753 \| 20550 \| 0.0049 \| - \|
	\| 14.1096 \| 20600 \| 0.0 \| - \|
	\| 14.1438 \| 20650 \| 0.0 \| - \|
	\| 14.1781 \| 20700 \| 0.0184 \| - \|
	\| 14.2123 \| 20750 \| 0.0029 \| - \|
	\| 14.2466 \| 20800 \| 0.0313 \| - \|
	\| 14.2808 \| 20850 \| 0.0 \| - \|
	\| 14.3151 \| 20900 \| 0.0051 \| - \|
	\| 14.3493 \| 20950 \| 0.0157 \| - \|
	\| 14.3836 \| 21000 \| 0.0059 \| - \|
	\| 14.4178 \| 21050 \| 0.0182 \| - \|
	\| 14.4521 \| 21100 \| 0.0242 \| - \|
	\| 14.4863 \| 21150 \| 0.0024 \| - \|
	\| 14.5205 \| 21200 \| 0.026 \| - \|
	\| 14.5548 \| 21250 \| 0.0211 \| - \|
	\| 14.5890 \| 21300 \| 0.0053 \| - \|
	\| 14.6233 \| 21350 \| 0.0 \| - \|
	\| 14.6575 \| 21400 \| 0.0 \| - \|
	\| 14.6918 \| 21450 \| 0.0034 \| - \|
	\| 14.7260 \| 21500 \| 0.0239 \| - \|
	\| 14.7603 \| 21550 \| 0.0209 \| - \|
	\| 14.7945 \| 21600 \| 0.0028 \| - \|
	\| 14.8288 \| 21650 \| 0.0 \| - \|
	\| 14.8630 \| 21700 \| 0.0022 \| - \|
	\| 14.8973 \| 21750 \| 0.0364 \| - \|
	\| 14.9315 \| 21800 \| 0.0052 \| - \|
	\| 14.9658 \| 21850 \| 0.0239 \| - \|
	\| 15.0 \| 21900 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 2.6.1
	- Transformers: 4.38.2
	- PyTorch: 2.2.1+cu121
	- Datasets: 2.18.0
	- Tokenizers: 0.15.2

	## 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}
	}
	```

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