SFT_GradProject / README.md

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	---
	base_model: sentence-transformers/all-MiniLM-L6-v2
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Good morning
	- text: how does the recommendation system work on this platform
	- text: who are you
	- text: where is the search bar
	- text: how can I find courses related to programming
	inference: true
	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: 0.8333333333333334
	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: 6 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 \|
	\|:--------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| general-questions \| <ul><li>'can you explain the concept of cloud computing'</li><li>'how do I assess my skills after completing a course'</li><li>'what is the significance of feedback in online learning'</li></ul> \|
	\| website-information \| <ul><li>'how to access the dashboard'</li><li>'where can I see my completed courses'</li><li>'where can I find notifications'</li></ul> \|
	\| greet-who_are_you \| <ul><li>"pourquoi j'ai besoin de toi"</li><li>'help please'</li><li>'I can not understand you'</li></ul> \|
	\| recommendations \| <ul><li>'how do I get recommendations based on my interests'</li><li>'can you recommend advanced courses in data science'</li><li>'what courses are trending in web development'</li></ul> \|
	\| greet-hi \| <ul><li>'Hey'</li><li>'Bonsoir'</li><li>'Salut'</li></ul> \|
	\| greet-good_bye \| <ul><li>'sortir'</li><li>'A plus tard'</li><li>'See you later'</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.8333 \|

	## 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("HussienAhmad/SFT_GradProject")
	# Run inference
	preds = model("who are you")
	```

	<!--
	### Downstream Use

	List how someone could finetune this model on their own dataset.
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 1 \| 6.2 \| 11 \|

	\| Label \| Training Sample Count \|
	\|:--------------------\|:----------------------\|
	\| greet-hi \| 5 \|
	\| greet-who_are_you \| 7 \|
	\| greet-good_bye \| 5 \|
	\| general-questions \| 28 \|
	\| recommendations \| 27 \|
	\| website-information \| 28 \|

	### Training Hyperparameters
	- batch_size: (4, 4)
	- num_epochs: (4, 4)
	- 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: True

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:--------:\|:-------------:\|:---------------:\|
	\| 0.0005 \| 1 \| 0.3442 \| - \|
	\| 0.0053 \| 10 \| 0.2974 \| - \|
	\| 0.0105 \| 20 \| 0.1983 \| - \|
	\| 0.0158 \| 30 \| 0.0645 \| - \|
	\| 0.0210 \| 40 \| 0.3592 \| - \|
	\| 0.0263 \| 50 \| 0.0033 \| - \|
	\| 0.0316 \| 60 \| 0.2558 \| - \|
	\| 0.0368 \| 70 \| 0.2319 \| - \|
	\| 0.0421 \| 80 \| 0.3831 \| - \|
	\| 0.0473 \| 90 \| 0.1864 \| - \|
	\| 0.0526 \| 100 \| 0.2244 \| - \|
	\| 0.0579 \| 110 \| 0.2316 \| - \|
	\| 0.0631 \| 120 \| 0.3702 \| - \|
	\| 0.0684 \| 130 \| 0.0582 \| - \|
	\| 0.0736 \| 140 \| 0.1031 \| - \|
	\| 0.0789 \| 150 \| 0.2882 \| - \|
	\| 0.0842 \| 160 \| 0.1125 \| - \|
	\| 0.0894 \| 170 \| 0.1588 \| - \|
	\| 0.0947 \| 180 \| 0.1672 \| - \|
	\| 0.0999 \| 190 \| 0.0974 \| - \|
	\| 0.1052 \| 200 \| 0.1789 \| - \|
	\| 0.1105 \| 210 \| 0.1032 \| - \|
	\| 0.1157 \| 220 \| 0.1344 \| - \|
	\| 0.1210 \| 230 \| 0.0952 \| - \|
	\| 0.1262 \| 240 \| 0.0891 \| - \|
	\| 0.1315 \| 250 \| 0.4312 \| - \|
	\| 0.1368 \| 260 \| 0.0871 \| - \|
	\| 0.1420 \| 270 \| 0.1482 \| - \|
	\| 0.1473 \| 280 \| 0.0645 \| - \|
	\| 0.1526 \| 290 \| 0.1214 \| - \|
	\| 0.1578 \| 300 \| 0.186 \| - \|
	\| 0.1631 \| 310 \| 0.0516 \| - \|
	\| 0.1683 \| 320 \| 0.0761 \| - \|
	\| 0.1736 \| 330 \| 0.0263 \| - \|
	\| 0.1789 \| 340 \| 0.0588 \| - \|
	\| 0.1841 \| 350 \| 0.016 \| - \|
	\| 0.1894 \| 360 \| 0.0264 \| - \|
	\| 0.1946 \| 370 \| 0.0153 \| - \|
	\| 0.1999 \| 380 \| 0.0091 \| - \|
	\| 0.2052 \| 390 \| 0.0347 \| - \|
	\| 0.2104 \| 400 \| 0.0095 \| - \|
	\| 0.2157 \| 410 \| 0.0262 \| - \|
	\| 0.2209 \| 420 \| 0.0182 \| - \|
	\| 0.2262 \| 430 \| 0.1407 \| - \|
	\| 0.2315 \| 440 \| 0.1451 \| - \|
	\| 0.2367 \| 450 \| 0.0045 \| - \|
	\| 0.2420 \| 460 \| 0.0053 \| - \|
	\| 0.2472 \| 470 \| 0.0038 \| - \|
	\| 0.2525 \| 480 \| 0.1549 \| - \|
	\| 0.2578 \| 490 \| 0.0036 \| - \|
	\| 0.2630 \| 500 \| 0.0079 \| - \|
	\| 0.2683 \| 510 \| 0.0065 \| - \|
	\| 0.2735 \| 520 \| 0.005 \| - \|
	\| 0.2788 \| 530 \| 0.0038 \| - \|
	\| 0.2841 \| 540 \| 0.0283 \| - \|
	\| 0.2893 \| 550 \| 0.0114 \| - \|
	\| 0.2946 \| 560 \| 0.0012 \| - \|
	\| 0.2998 \| 570 \| 0.0165 \| - \|
	\| 0.3051 \| 580 \| 0.0009 \| - \|
	\| 0.3104 \| 590 \| 0.038 \| - \|
	\| 0.3156 \| 600 \| 0.0127 \| - \|
	\| 0.3209 \| 610 \| 0.0019 \| - \|
	\| 0.3261 \| 620 \| 0.003 \| - \|
	\| 0.3314 \| 630 \| 0.0013 \| - \|
	\| 0.3367 \| 640 \| 0.0024 \| - \|
	\| 0.3419 \| 650 \| 0.002 \| - \|
	\| 0.3472 \| 660 \| 0.0017 \| - \|
	\| 0.3524 \| 670 \| 0.0074 \| - \|
	\| 0.3577 \| 680 \| 0.0008 \| - \|
	\| 0.3630 \| 690 \| 0.0015 \| - \|
	\| 0.3682 \| 700 \| 0.0018 \| - \|
	\| 0.3735 \| 710 \| 0.0009 \| - \|
	\| 0.3787 \| 720 \| 0.0019 \| - \|
	\| 0.3840 \| 730 \| 0.0032 \| - \|
	\| 0.3893 \| 740 \| 0.001 \| - \|
	\| 0.3945 \| 750 \| 0.0257 \| - \|
	\| 0.3998 \| 760 \| 0.0018 \| - \|
	\| 0.4050 \| 770 \| 0.001 \| - \|
	\| 0.4103 \| 780 \| 0.0006 \| - \|
	\| 0.4156 \| 790 \| 0.0014 \| - \|
	\| 0.4208 \| 800 \| 0.0012 \| - \|
	\| 0.4261 \| 810 \| 0.018 \| - \|
	\| 0.4314 \| 820 \| 0.0013 \| - \|
	\| 0.4366 \| 830 \| 0.0019 \| - \|
	\| 0.4419 \| 840 \| 0.0006 \| - \|
	\| 0.4471 \| 850 \| 0.0012 \| - \|
	\| 0.4524 \| 860 \| 0.0011 \| - \|
	\| 0.4577 \| 870 \| 0.001 \| - \|
	\| 0.4629 \| 880 \| 0.0017 \| - \|
	\| 0.4682 \| 890 \| 0.002 \| - \|
	\| 0.4734 \| 900 \| 0.0009 \| - \|
	\| 0.4787 \| 910 \| 0.0026 \| - \|
	\| 0.4840 \| 920 \| 0.0009 \| - \|
	\| 0.4892 \| 930 \| 0.0019 \| - \|
	\| 0.4945 \| 940 \| 0.0018 \| - \|
	\| 0.4997 \| 950 \| 0.001 \| - \|
	\| 0.5050 \| 960 \| 0.0022 \| - \|
	\| 0.5103 \| 970 \| 0.0006 \| - \|
	\| 0.5155 \| 980 \| 0.001 \| - \|
	\| 0.5208 \| 990 \| 0.0004 \| - \|
	\| 0.5260 \| 1000 \| 0.0002 \| - \|
	\| 0.5313 \| 1010 \| 0.001 \| - \|
	\| 0.5366 \| 1020 \| 0.001 \| - \|
	\| 0.5418 \| 1030 \| 0.0019 \| - \|
	\| 0.5471 \| 1040 \| 0.0004 \| - \|
	\| 0.5523 \| 1050 \| 0.1705 \| - \|
	\| 0.5576 \| 1060 \| 0.0006 \| - \|
	\| 0.5629 \| 1070 \| 0.0006 \| - \|
	\| 0.5681 \| 1080 \| 0.0007 \| - \|
	\| 0.5734 \| 1090 \| 0.1562 \| - \|
	\| 0.5786 \| 1100 \| 0.0008 \| - \|
	\| 0.5839 \| 1110 \| 0.0016 \| - \|
	\| 0.5892 \| 1120 \| 0.001 \| - \|
	\| 0.5944 \| 1130 \| 0.0003 \| - \|
	\| 0.5997 \| 1140 \| 0.0077 \| - \|
	\| 0.6049 \| 1150 \| 0.0006 \| - \|
	\| 0.6102 \| 1160 \| 0.0008 \| - \|
	\| 0.6155 \| 1170 \| 0.0006 \| - \|
	\| 0.6207 \| 1180 \| 0.0007 \| - \|
	\| 0.6260 \| 1190 \| 0.1438 \| - \|
	\| 0.6312 \| 1200 \| 0.0008 \| - \|
	\| 0.6365 \| 1210 \| 0.0012 \| - \|
	\| 0.6418 \| 1220 \| 0.0005 \| - \|
	\| 0.6470 \| 1230 \| 0.0017 \| - \|
	\| 0.6523 \| 1240 \| 0.0007 \| - \|
	\| 0.6575 \| 1250 \| 0.0004 \| - \|
	\| 0.6628 \| 1260 \| 0.0066 \| - \|
	\| 0.6681 \| 1270 \| 0.0004 \| - \|
	\| 0.6733 \| 1280 \| 0.0002 \| - \|
	\| 0.6786 \| 1290 \| 0.1272 \| - \|
	\| 0.6839 \| 1300 \| 0.0019 \| - \|
	\| 0.6891 \| 1310 \| 0.0014 \| - \|
	\| 0.6944 \| 1320 \| 0.0003 \| - \|
	\| 0.6996 \| 1330 \| 0.0007 \| - \|
	\| 0.7049 \| 1340 \| 0.0003 \| - \|
	\| 0.7102 \| 1350 \| 0.0008 \| - \|
	\| 0.7154 \| 1360 \| 0.0005 \| - \|
	\| 0.7207 \| 1370 \| 0.126 \| - \|
	\| 0.7259 \| 1380 \| 0.0003 \| - \|
	\| 0.7312 \| 1390 \| 0.0013 \| - \|
	\| 0.7365 \| 1400 \| 0.0005 \| - \|
	\| 0.7417 \| 1410 \| 0.0003 \| - \|
	\| 0.7470 \| 1420 \| 0.0003 \| - \|
	\| 0.7522 \| 1430 \| 0.0003 \| - \|
	\| 0.7575 \| 1440 \| 0.0005 \| - \|
	\| 0.7628 \| 1450 \| 0.0009 \| - \|
	\| 0.7680 \| 1460 \| 0.0008 \| - \|
	\| 0.7733 \| 1470 \| 0.0002 \| - \|
	\| 0.7785 \| 1480 \| 0.0003 \| - \|
	\| 0.7838 \| 1490 \| 0.0007 \| - \|
	\| 0.7891 \| 1500 \| 0.0064 \| - \|
	\| 0.7943 \| 1510 \| 0.0004 \| - \|
	\| 0.7996 \| 1520 \| 0.0006 \| - \|
	\| 0.8048 \| 1530 \| 0.0003 \| - \|
	\| 0.8101 \| 1540 \| 0.0005 \| - \|
	\| 0.8154 \| 1550 \| 0.0006 \| - \|
	\| 0.8206 \| 1560 \| 0.0005 \| - \|
	\| 0.8259 \| 1570 \| 0.0004 \| - \|
	\| 0.8311 \| 1580 \| 0.0007 \| - \|
	\| 0.8364 \| 1590 \| 0.0006 \| - \|
	\| 0.8417 \| 1600 \| 0.0002 \| - \|
	\| 0.8469 \| 1610 \| 0.0007 \| - \|
	\| 0.8522 \| 1620 \| 0.0002 \| - \|
	\| 0.8574 \| 1630 \| 0.0005 \| - \|
	\| 0.8627 \| 1640 \| 0.0035 \| - \|
	\| 0.8680 \| 1650 \| 0.0004 \| - \|
	\| 0.8732 \| 1660 \| 0.0025 \| - \|
	\| 0.8785 \| 1670 \| 0.0005 \| - \|
	\| 0.8837 \| 1680 \| 0.0021 \| - \|
	\| 0.8890 \| 1690 \| 0.0003 \| - \|
	\| 0.8943 \| 1700 \| 0.0018 \| - \|
	\| 0.8995 \| 1710 \| 0.0004 \| - \|
	\| 0.9048 \| 1720 \| 0.0002 \| - \|
	\| 0.9100 \| 1730 \| 0.0003 \| - \|
	\| 0.9153 \| 1740 \| 0.0006 \| - \|
	\| 0.9206 \| 1750 \| 0.0002 \| - \|
	\| 0.9258 \| 1760 \| 0.0003 \| - \|
	\| 0.9311 \| 1770 \| 0.0004 \| - \|
	\| 0.9363 \| 1780 \| 0.0004 \| - \|
	\| 0.9416 \| 1790 \| 0.0004 \| - \|
	\| 0.9469 \| 1800 \| 0.0006 \| - \|
	\| 0.9521 \| 1810 \| 0.0007 \| - \|
	\| 0.9574 \| 1820 \| 0.001 \| - \|
	\| 0.9627 \| 1830 \| 0.0003 \| - \|
	\| 0.9679 \| 1840 \| 0.0009 \| - \|
	\| 0.9732 \| 1850 \| 0.0001 \| - \|
	\| 0.9784 \| 1860 \| 0.0006 \| - \|
	\| 0.9837 \| 1870 \| 0.0002 \| - \|
	\| 0.9890 \| 1880 \| 0.0003 \| - \|
	\| 0.9942 \| 1890 \| 0.0004 \| - \|
	\| 0.9995 \| 1900 \| 0.0009 \| - \|
	\| 1.0 \| 1901 \| - \| 0.0347 \|
	\| 1.0047 \| 1910 \| 0.0004 \| - \|
	\| 1.0100 \| 1920 \| 0.0004 \| - \|
	\| 1.0153 \| 1930 \| 0.0005 \| - \|
	\| 1.0205 \| 1940 \| 0.0007 \| - \|
	\| 1.0258 \| 1950 \| 0.0085 \| - \|
	\| 1.0310 \| 1960 \| 0.0003 \| - \|
	\| 1.0363 \| 1970 \| 0.0003 \| - \|
	\| 1.0416 \| 1980 \| 0.0002 \| - \|
	\| 1.0468 \| 1990 \| 0.0009 \| - \|
	\| 1.0521 \| 2000 \| 0.0002 \| - \|
	\| 1.0573 \| 2010 \| 0.0059 \| - \|
	\| 1.0626 \| 2020 \| 0.0007 \| - \|
	\| 1.0679 \| 2030 \| 0.0008 \| - \|
	\| 1.0731 \| 2040 \| 0.0002 \| - \|
	\| 1.0784 \| 2050 \| 0.0002 \| - \|
	\| 1.0836 \| 2060 \| 0.0003 \| - \|
	\| 1.0889 \| 2070 \| 0.0003 \| - \|
	\| 1.0942 \| 2080 \| 0.0002 \| - \|
	\| 1.0994 \| 2090 \| 0.0003 \| - \|
	\| 1.1047 \| 2100 \| 0.0002 \| - \|
	\| 1.1099 \| 2110 \| 0.0065 \| - \|
	\| 1.1152 \| 2120 \| 0.0006 \| - \|
	\| 1.1205 \| 2130 \| 0.0004 \| - \|
	\| 1.1257 \| 2140 \| 0.0035 \| - \|
	\| 1.1310 \| 2150 \| 0.0003 \| - \|
	\| 1.1362 \| 2160 \| 0.0002 \| - \|
	\| 1.1415 \| 2170 \| 0.0002 \| - \|
	\| 1.1468 \| 2180 \| 0.0002 \| - \|
	\| 1.1520 \| 2190 \| 0.001 \| - \|
	\| 1.1573 \| 2200 \| 0.0003 \| - \|
	\| 1.1625 \| 2210 \| 0.0002 \| - \|
	\| 1.1678 \| 2220 \| 0.0002 \| - \|
	\| 1.1731 \| 2230 \| 0.0002 \| - \|
	\| 1.1783 \| 2240 \| 0.0003 \| - \|
	\| 1.1836 \| 2250 \| 0.0002 \| - \|
	\| 1.1888 \| 2260 \| 0.0008 \| - \|
	\| 1.1941 \| 2270 \| 0.0002 \| - \|
	\| 1.1994 \| 2280 \| 0.0018 \| - \|
	\| 1.2046 \| 2290 \| 0.0001 \| - \|
	\| 1.2099 \| 2300 \| 0.0002 \| - \|
	\| 1.2151 \| 2310 \| 0.0005 \| - \|
	\| 1.2204 \| 2320 \| 0.0008 \| - \|
	\| 1.2257 \| 2330 \| 0.0002 \| - \|
	\| 1.2309 \| 2340 \| 0.0003 \| - \|
	\| 1.2362 \| 2350 \| 0.0002 \| - \|
	\| 1.2415 \| 2360 \| 0.0003 \| - \|
	\| 1.2467 \| 2370 \| 0.0001 \| - \|
	\| 1.2520 \| 2380 \| 0.0002 \| - \|
	\| 1.2572 \| 2390 \| 0.0002 \| - \|
	\| 1.2625 \| 2400 \| 0.0002 \| - \|
	\| 1.2678 \| 2410 \| 0.0003 \| - \|
	\| 1.2730 \| 2420 \| 0.0002 \| - \|
	\| 1.2783 \| 2430 \| 0.0002 \| - \|
	\| 1.2835 \| 2440 \| 0.0002 \| - \|
	\| 1.2888 \| 2450 \| 0.0003 \| - \|
	\| 1.2941 \| 2460 \| 0.0004 \| - \|
	\| 1.2993 \| 2470 \| 0.0002 \| - \|
	\| 1.3046 \| 2480 \| 0.0002 \| - \|
	\| 1.3098 \| 2490 \| 0.0006 \| - \|
	\| 1.3151 \| 2500 \| 0.0002 \| - \|
	\| 1.3204 \| 2510 \| 0.0002 \| - \|
	\| 1.3256 \| 2520 \| 0.0001 \| - \|
	\| 1.3309 \| 2530 \| 0.0037 \| - \|
	\| 1.3361 \| 2540 \| 0.0004 \| - \|
	\| 1.3414 \| 2550 \| 0.0003 \| - \|
	\| 1.3467 \| 2560 \| 0.0001 \| - \|
	\| 1.3519 \| 2570 \| 0.0001 \| - \|
	\| 1.3572 \| 2580 \| 0.0003 \| - \|
	\| 1.3624 \| 2590 \| 0.0002 \| - \|
	\| 1.3677 \| 2600 \| 0.0003 \| - \|
	\| 1.3730 \| 2610 \| 0.0003 \| - \|
	\| 1.3782 \| 2620 \| 0.0003 \| - \|
	\| 1.3835 \| 2630 \| 0.0003 \| - \|
	\| 1.3887 \| 2640 \| 0.0002 \| - \|
	\| 1.3940 \| 2650 \| 0.0034 \| - \|
	\| 1.3993 \| 2660 \| 0.0002 \| - \|
	\| 1.4045 \| 2670 \| 0.0004 \| - \|
	\| 1.4098 \| 2680 \| 0.0004 \| - \|
	\| 1.4150 \| 2690 \| 0.0003 \| - \|
	\| 1.4203 \| 2700 \| 0.0003 \| - \|
	\| 1.4256 \| 2710 \| 0.0007 \| - \|
	\| 1.4308 \| 2720 \| 0.0002 \| - \|
	\| 1.4361 \| 2730 \| 0.0004 \| - \|
	\| 1.4413 \| 2740 \| 0.0004 \| - \|
	\| 1.4466 \| 2750 \| 0.0005 \| - \|
	\| 1.4519 \| 2760 \| 0.0003 \| - \|
	\| 1.4571 \| 2770 \| 0.0003 \| - \|
	\| 1.4624 \| 2780 \| 0.0005 \| - \|
	\| 1.4676 \| 2790 \| 0.0015 \| - \|
	\| 1.4729 \| 2800 \| 0.0005 \| - \|
	\| 1.4782 \| 2810 \| 0.0003 \| - \|
	\| 1.4834 \| 2820 \| 0.0003 \| - \|
	\| 1.4887 \| 2830 \| 0.0002 \| - \|
	\| 1.4940 \| 2840 \| 0.0003 \| - \|
	\| 1.4992 \| 2850 \| 0.0004 \| - \|
	\| 1.5045 \| 2860 \| 0.0025 \| - \|
	\| 1.5097 \| 2870 \| 0.0001 \| - \|
	\| 1.5150 \| 2880 \| 0.0002 \| - \|
	\| 1.5203 \| 2890 \| 0.0004 \| - \|
	\| 1.5255 \| 2900 \| 0.0001 \| - \|
	\| 1.5308 \| 2910 \| 0.0003 \| - \|
	\| 1.5360 \| 2920 \| 0.0006 \| - \|
	\| 1.5413 \| 2930 \| 0.0001 \| - \|
	\| 1.5466 \| 2940 \| 0.0001 \| - \|
	\| 1.5518 \| 2950 \| 0.0004 \| - \|
	\| 1.5571 \| 2960 \| 0.0002 \| - \|
	\| 1.5623 \| 2970 \| 0.0006 \| - \|
	\| 1.5676 \| 2980 \| 0.0003 \| - \|
	\| 1.5729 \| 2990 \| 0.001 \| - \|
	\| 1.5781 \| 3000 \| 0.0003 \| - \|
	\| 1.5834 \| 3010 \| 0.0002 \| - \|
	\| 1.5886 \| 3020 \| 0.0003 \| - \|
	\| 1.5939 \| 3030 \| 0.0002 \| - \|
	\| 1.5992 \| 3040 \| 0.0001 \| - \|
	\| 1.6044 \| 3050 \| 0.0002 \| - \|
	\| 1.6097 \| 3060 \| 0.0002 \| - \|
	\| 1.6149 \| 3070 \| 0.0002 \| - \|
	\| 1.6202 \| 3080 \| 0.0001 \| - \|
	\| 1.6255 \| 3090 \| 0.0002 \| - \|
	\| 1.6307 \| 3100 \| 0.0002 \| - \|
	\| 1.6360 \| 3110 \| 0.0001 \| - \|
	\| 1.6412 \| 3120 \| 0.0001 \| - \|
	\| 1.6465 \| 3130 \| 0.0002 \| - \|
	\| 1.6518 \| 3140 \| 0.0003 \| - \|
	\| 1.6570 \| 3150 \| 0.0002 \| - \|
	\| 1.6623 \| 3160 \| 0.0002 \| - \|
	\| 1.6675 \| 3170 \| 0.0001 \| - \|
	\| 1.6728 \| 3180 \| 0.0002 \| - \|
	\| 1.6781 \| 3190 \| 0.0002 \| - \|
	\| 1.6833 \| 3200 \| 0.0008 \| - \|
	\| 1.6886 \| 3210 \| 0.0002 \| - \|
	\| 1.6938 \| 3220 \| 0.0003 \| - \|
	\| 1.6991 \| 3230 \| 0.0001 \| - \|
	\| 1.7044 \| 3240 \| 0.0001 \| - \|
	\| 1.7096 \| 3250 \| 0.0001 \| - \|
	\| 1.7149 \| 3260 \| 0.0002 \| - \|
	\| 1.7201 \| 3270 \| 0.0003 \| - \|
	\| 1.7254 \| 3280 \| 0.0001 \| - \|
	\| 1.7307 \| 3290 \| 0.0003 \| - \|
	\| 1.7359 \| 3300 \| 0.0001 \| - \|
	\| 1.7412 \| 3310 \| 0.0003 \| - \|
	\| 1.7464 \| 3320 \| 0.0002 \| - \|
	\| 1.7517 \| 3330 \| 0.0002 \| - \|
	\| 1.7570 \| 3340 \| 0.0001 \| - \|
	\| 1.7622 \| 3350 \| 0.0002 \| - \|
	\| 1.7675 \| 3360 \| 0.0001 \| - \|
	\| 1.7728 \| 3370 \| 0.0005 \| - \|
	\| 1.7780 \| 3380 \| 0.0001 \| - \|
	\| 1.7833 \| 3390 \| 0.0003 \| - \|
	\| 1.7885 \| 3400 \| 0.0002 \| - \|
	\| 1.7938 \| 3410 \| 0.0003 \| - \|
	\| 1.7991 \| 3420 \| 0.0002 \| - \|
	\| 1.8043 \| 3430 \| 0.0002 \| - \|
	\| 1.8096 \| 3440 \| 0.0009 \| - \|
	\| 1.8148 \| 3450 \| 0.0001 \| - \|
	\| 1.8201 \| 3460 \| 0.0005 \| - \|
	\| 1.8254 \| 3470 \| 0.0002 \| - \|
	\| 1.8306 \| 3480 \| 0.0004 \| - \|
	\| 1.8359 \| 3490 \| 0.0002 \| - \|
	\| 1.8411 \| 3500 \| 0.0001 \| - \|
	\| 1.8464 \| 3510 \| 0.0001 \| - \|
	\| 1.8517 \| 3520 \| 0.0003 \| - \|
	\| 1.8569 \| 3530 \| 0.0001 \| - \|
	\| 1.8622 \| 3540 \| 0.0002 \| - \|
	\| 1.8674 \| 3550 \| 0.0002 \| - \|
	\| 1.8727 \| 3560 \| 0.0011 \| - \|
	\| 1.8780 \| 3570 \| 0.0003 \| - \|
	\| 1.8832 \| 3580 \| 0.0003 \| - \|
	\| 1.8885 \| 3590 \| 0.0002 \| - \|
	\| 1.8937 \| 3600 \| 0.0001 \| - \|
	\| 1.8990 \| 3610 \| 0.0001 \| - \|
	\| 1.9043 \| 3620 \| 0.0002 \| - \|
	\| 1.9095 \| 3630 \| 0.0001 \| - \|
	\| 1.9148 \| 3640 \| 0.0002 \| - \|
	\| 1.9200 \| 3650 \| 0.0002 \| - \|
	\| 1.9253 \| 3660 \| 0.0002 \| - \|
	\| 1.9306 \| 3670 \| 0.0002 \| - \|
	\| 1.9358 \| 3680 \| 0.0001 \| - \|
	\| 1.9411 \| 3690 \| 0.0002 \| - \|
	\| 1.9463 \| 3700 \| 0.0003 \| - \|
	\| 1.9516 \| 3710 \| 0.0006 \| - \|
	\| 1.9569 \| 3720 \| 0.0004 \| - \|
	\| 1.9621 \| 3730 \| 0.0001 \| - \|
	\| 1.9674 \| 3740 \| 0.0002 \| - \|
	\| 1.9726 \| 3750 \| 0.0004 \| - \|
	\| 1.9779 \| 3760 \| 0.0002 \| - \|
	\| 1.9832 \| 3770 \| 0.0004 \| - \|
	\| 1.9884 \| 3780 \| 0.0003 \| - \|
	\| 1.9937 \| 3790 \| 0.0002 \| - \|
	\| 1.9989 \| 3800 \| 0.0002 \| - \|
	\| 2.0 \| 3802 \| - \| 0.0333 \|
	\| 2.0042 \| 3810 \| 0.0001 \| - \|
	\| 2.0095 \| 3820 \| 0.0002 \| - \|
	\| 2.0147 \| 3830 \| 0.0004 \| - \|
	\| 2.0200 \| 3840 \| 0.0005 \| - \|
	\| 2.0252 \| 3850 \| 0.0002 \| - \|
	\| 2.0305 \| 3860 \| 0.0001 \| - \|
	\| 2.0358 \| 3870 \| 0.0005 \| - \|
	\| 2.0410 \| 3880 \| 0.0002 \| - \|
	\| 2.0463 \| 3890 \| 0.0002 \| - \|
	\| 2.0516 \| 3900 \| 0.0002 \| - \|
	\| 2.0568 \| 3910 \| 0.0003 \| - \|
	\| 2.0621 \| 3920 \| 0.0002 \| - \|
	\| 2.0673 \| 3930 \| 0.0005 \| - \|
	\| 2.0726 \| 3940 \| 0.0002 \| - \|
	\| 2.0779 \| 3950 \| 0.0001 \| - \|
	\| 2.0831 \| 3960 \| 0.0001 \| - \|
	\| 2.0884 \| 3970 \| 0.0003 \| - \|
	\| 2.0936 \| 3980 \| 0.0001 \| - \|
	\| 2.0989 \| 3990 \| 0.0002 \| - \|
	\| 2.1042 \| 4000 \| 0.0001 \| - \|
	\| 2.1094 \| 4010 \| 0.0001 \| - \|
	\| 2.1147 \| 4020 \| 0.0001 \| - \|
	\| 2.1199 \| 4030 \| 0.0004 \| - \|
	\| 2.1252 \| 4040 \| 0.0002 \| - \|
	\| 2.1305 \| 4050 \| 0.0003 \| - \|
	\| 2.1357 \| 4060 \| 0.0002 \| - \|
	\| 2.1410 \| 4070 \| 0.0001 \| - \|
	\| 2.1462 \| 4080 \| 0.0001 \| - \|
	\| 2.1515 \| 4090 \| 0.0001 \| - \|
	\| 2.1568 \| 4100 \| 0.0001 \| - \|
	\| 2.1620 \| 4110 \| 0.0001 \| - \|
	\| 2.1673 \| 4120 \| 0.0001 \| - \|
	\| 2.1725 \| 4130 \| 0.0001 \| - \|
	\| 2.1778 \| 4140 \| 0.0001 \| - \|
	\| 2.1831 \| 4150 \| 0.0009 \| - \|
	\| 2.1883 \| 4160 \| 0.0001 \| - \|
	\| 2.1936 \| 4170 \| 0.0003 \| - \|
	\| 2.1988 \| 4180 \| 0.0001 \| - \|
	\| 2.2041 \| 4190 \| 0.0002 \| - \|
	\| 2.2094 \| 4200 \| 0.0003 \| - \|
	\| 2.2146 \| 4210 \| 0.0008 \| - \|
	\| 2.2199 \| 4220 \| 0.0002 \| - \|
	\| 2.2251 \| 4230 \| 0.0004 \| - \|
	\| 2.2304 \| 4240 \| 0.0002 \| - \|
	\| 2.2357 \| 4250 \| 0.0001 \| - \|
	\| 2.2409 \| 4260 \| 0.0004 \| - \|
	\| 2.2462 \| 4270 \| 0.0001 \| - \|
	\| 2.2514 \| 4280 \| 0.0001 \| - \|
	\| 2.2567 \| 4290 \| 0.0001 \| - \|
	\| 2.2620 \| 4300 \| 0.0001 \| - \|
	\| 2.2672 \| 4310 \| 0.0002 \| - \|
	\| 2.2725 \| 4320 \| 0.0002 \| - \|
	\| 2.2777 \| 4330 \| 0.0002 \| - \|
	\| 2.2830 \| 4340 \| 0.0002 \| - \|
	\| 2.2883 \| 4350 \| 0.0001 \| - \|
	\| 2.2935 \| 4360 \| 0.0001 \| - \|
	\| 2.2988 \| 4370 \| 0.0001 \| - \|
	\| 2.3041 \| 4380 \| 0.0004 \| - \|
	\| 2.3093 \| 4390 \| 0.0002 \| - \|
	\| 2.3146 \| 4400 \| 0.0001 \| - \|
	\| 2.3198 \| 4410 \| 0.0004 \| - \|
	\| 2.3251 \| 4420 \| 0.0001 \| - \|
	\| 2.3304 \| 4430 \| 0.0001 \| - \|
	\| 2.3356 \| 4440 \| 0.0001 \| - \|
	\| 2.3409 \| 4450 \| 0.0001 \| - \|
	\| 2.3461 \| 4460 \| 0.0001 \| - \|
	\| 2.3514 \| 4470 \| 0.0002 \| - \|
	\| 2.3567 \| 4480 \| 0.0004 \| - \|
	\| 2.3619 \| 4490 \| 0.0003 \| - \|
	\| 2.3672 \| 4500 \| 0.0002 \| - \|
	\| 2.3724 \| 4510 \| 0.0001 \| - \|
	\| 2.3777 \| 4520 \| 0.0001 \| - \|
	\| 2.3830 \| 4530 \| 0.0001 \| - \|
	\| 2.3882 \| 4540 \| 0.0001 \| - \|
	\| 2.3935 \| 4550 \| 0.0001 \| - \|
	\| 2.3987 \| 4560 \| 0.0002 \| - \|
	\| 2.4040 \| 4570 \| 0.0001 \| - \|
	\| 2.4093 \| 4580 \| 0.0001 \| - \|
	\| 2.4145 \| 4590 \| 0.0001 \| - \|
	\| 2.4198 \| 4600 \| 0.0001 \| - \|
	\| 2.4250 \| 4610 \| 0.0001 \| - \|
	\| 2.4303 \| 4620 \| 0.0008 \| - \|
	\| 2.4356 \| 4630 \| 0.0001 \| - \|
	\| 2.4408 \| 4640 \| 0.0002 \| - \|
	\| 2.4461 \| 4650 \| 0.0001 \| - \|
	\| 2.4513 \| 4660 \| 0.0001 \| - \|
	\| 2.4566 \| 4670 \| 0.0001 \| - \|
	\| 2.4619 \| 4680 \| 0.0001 \| - \|
	\| 2.4671 \| 4690 \| 0.0001 \| - \|
	\| 2.4724 \| 4700 \| 0.0001 \| - \|
	\| 2.4776 \| 4710 \| 0.0001 \| - \|
	\| 2.4829 \| 4720 \| 0.0001 \| - \|
	\| 2.4882 \| 4730 \| 0.0002 \| - \|
	\| 2.4934 \| 4740 \| 0.0001 \| - \|
	\| 2.4987 \| 4750 \| 0.0001 \| - \|
	\| 2.5039 \| 4760 \| 0.0008 \| - \|
	\| 2.5092 \| 4770 \| 0.0002 \| - \|
	\| 2.5145 \| 4780 \| 0.0001 \| - \|
	\| 2.5197 \| 4790 \| 0.0001 \| - \|
	\| 2.5250 \| 4800 \| 0.0007 \| - \|
	\| 2.5302 \| 4810 \| 0.0003 \| - \|
	\| 2.5355 \| 4820 \| 0.0001 \| - \|
	\| 2.5408 \| 4830 \| 0.0001 \| - \|
	\| 2.5460 \| 4840 \| 0.0001 \| - \|
	\| 2.5513 \| 4850 \| 0.0003 \| - \|
	\| 2.5565 \| 4860 \| 0.0001 \| - \|
	\| 2.5618 \| 4870 \| 0.0001 \| - \|
	\| 2.5671 \| 4880 \| 0.0002 \| - \|
	\| 2.5723 \| 4890 \| 0.0001 \| - \|
	\| 2.5776 \| 4900 \| 0.0001 \| - \|
	\| 2.5829 \| 4910 \| 0.0003 \| - \|
	\| 2.5881 \| 4920 \| 0.0001 \| - \|
	\| 2.5934 \| 4930 \| 0.0002 \| - \|
	\| 2.5986 \| 4940 \| 0.0003 \| - \|
	\| 2.6039 \| 4950 \| 0.0001 \| - \|
	\| 2.6092 \| 4960 \| 0.0002 \| - \|
	\| 2.6144 \| 4970 \| 0.0001 \| - \|
	\| 2.6197 \| 4980 \| 0.0002 \| - \|
	\| 2.6249 \| 4990 \| 0.0002 \| - \|
	\| 2.6302 \| 5000 \| 0.0002 \| - \|
	\| 2.6355 \| 5010 \| 0.0004 \| - \|
	\| 2.6407 \| 5020 \| 0.0001 \| - \|
	\| 2.6460 \| 5030 \| 0.0001 \| - \|
	\| 2.6512 \| 5040 \| 0.0004 \| - \|
	\| 2.6565 \| 5050 \| 0.0001 \| - \|
	\| 2.6618 \| 5060 \| 0.0002 \| - \|
	\| 2.6670 \| 5070 \| 0.0014 \| - \|
	\| 2.6723 \| 5080 \| 0.0003 \| - \|
	\| 2.6775 \| 5090 \| 0.0001 \| - \|
	\| 2.6828 \| 5100 \| 0.0003 \| - \|
	\| 2.6881 \| 5110 \| 0.0001 \| - \|
	\| 2.6933 \| 5120 \| 0.0001 \| - \|
	\| 2.6986 \| 5130 \| 0.0009 \| - \|
	\| 2.7038 \| 5140 \| 0.0002 \| - \|
	\| 2.7091 \| 5150 \| 0.0003 \| - \|
	\| 2.7144 \| 5160 \| 0.0001 \| - \|
	\| 2.7196 \| 5170 \| 0.0001 \| - \|
	\| 2.7249 \| 5180 \| 0.0002 \| - \|
	\| 2.7301 \| 5190 \| 0.0001 \| - \|
	\| 2.7354 \| 5200 \| 0.0001 \| - \|
	\| 2.7407 \| 5210 \| 0.0001 \| - \|
	\| 2.7459 \| 5220 \| 0.0002 \| - \|
	\| 2.7512 \| 5230 \| 0.0004 \| - \|
	\| 2.7564 \| 5240 \| 0.0001 \| - \|
	\| 2.7617 \| 5250 \| 0.0001 \| - \|
	\| 2.7670 \| 5260 \| 0.0004 \| - \|
	\| 2.7722 \| 5270 \| 0.0003 \| - \|
	\| 2.7775 \| 5280 \| 0.0002 \| - \|
	\| 2.7827 \| 5290 \| 0.0002 \| - \|
	\| 2.7880 \| 5300 \| 0.0001 \| - \|
	\| 2.7933 \| 5310 \| 0.0003 \| - \|
	\| 2.7985 \| 5320 \| 0.0001 \| - \|
	\| 2.8038 \| 5330 \| 0.0005 \| - \|
	\| 2.8090 \| 5340 \| 0.0001 \| - \|
	\| 2.8143 \| 5350 \| 0.0001 \| - \|
	\| 2.8196 \| 5360 \| 0.0001 \| - \|
	\| 2.8248 \| 5370 \| 0.0001 \| - \|
	\| 2.8301 \| 5380 \| 0.0003 \| - \|
	\| 2.8353 \| 5390 \| 0.0001 \| - \|
	\| 2.8406 \| 5400 \| 0.0008 \| - \|
	\| 2.8459 \| 5410 \| 0.0001 \| - \|
	\| 2.8511 \| 5420 \| 0.0001 \| - \|
	\| 2.8564 \| 5430 \| 0.0001 \| - \|
	\| 2.8617 \| 5440 \| 0.0002 \| - \|
	\| 2.8669 \| 5450 \| 0.0001 \| - \|
	\| 2.8722 \| 5460 \| 0.0004 \| - \|
	\| 2.8774 \| 5470 \| 0.0001 \| - \|
	\| 2.8827 \| 5480 \| 0.0001 \| - \|
	\| 2.8880 \| 5490 \| 0.0002 \| - \|
	\| 2.8932 \| 5500 \| 0.0001 \| - \|
	\| 2.8985 \| 5510 \| 0.0001 \| - \|
	\| 2.9037 \| 5520 \| 0.0001 \| - \|
	\| 2.9090 \| 5530 \| 0.0002 \| - \|
	\| 2.9143 \| 5540 \| 0.0002 \| - \|
	\| 2.9195 \| 5550 \| 0.0001 \| - \|
	\| 2.9248 \| 5560 \| 0.0001 \| - \|
	\| 2.9300 \| 5570 \| 0.0005 \| - \|
	\| 2.9353 \| 5580 \| 0.0002 \| - \|
	\| 2.9406 \| 5590 \| 0.0001 \| - \|
	\| 2.9458 \| 5600 \| 0.0001 \| - \|
	\| 2.9511 \| 5610 \| 0.0003 \| - \|
	\| 2.9563 \| 5620 \| 0.0001 \| - \|
	\| 2.9616 \| 5630 \| 0.0001 \| - \|
	\| 2.9669 \| 5640 \| 0.0001 \| - \|
	\| 2.9721 \| 5650 \| 0.0006 \| - \|
	\| 2.9774 \| 5660 \| 0.0001 \| - \|
	\| 2.9826 \| 5670 \| 0.0001 \| - \|
	\| 2.9879 \| 5680 \| 0.0001 \| - \|
	\| 2.9932 \| 5690 \| 0.0001 \| - \|
	\| 2.9984 \| 5700 \| 0.0001 \| - \|
	\| 3.0 \| 5703 \| - \| 0.0349 \|
	\| 3.0037 \| 5710 \| 0.0001 \| - \|
	\| 3.0089 \| 5720 \| 0.0001 \| - \|
	\| 3.0142 \| 5730 \| 0.0002 \| - \|
	\| 3.0195 \| 5740 \| 0.0001 \| - \|
	\| 3.0247 \| 5750 \| 0.0002 \| - \|
	\| 3.0300 \| 5760 \| 0.0001 \| - \|
	\| 3.0352 \| 5770 \| 0.0008 \| - \|
	\| 3.0405 \| 5780 \| 0.0004 \| - \|
	\| 3.0458 \| 5790 \| 0.0003 \| - \|
	\| 3.0510 \| 5800 \| 0.0001 \| - \|
	\| 3.0563 \| 5810 \| 0.0001 \| - \|
	\| 3.0615 \| 5820 \| 0.0006 \| - \|
	\| 3.0668 \| 5830 \| 0.0002 \| - \|
	\| 3.0721 \| 5840 \| 0.0001 \| - \|
	\| 3.0773 \| 5850 \| 0.0002 \| - \|
	\| 3.0826 \| 5860 \| 0.0002 \| - \|
	\| 3.0878 \| 5870 \| 0.0002 \| - \|
	\| 3.0931 \| 5880 \| 0.0002 \| - \|
	\| 3.0984 \| 5890 \| 0.0001 \| - \|
	\| 3.1036 \| 5900 \| 0.0001 \| - \|
	\| 3.1089 \| 5910 \| 0.0001 \| - \|
	\| 3.1142 \| 5920 \| 0.0001 \| - \|
	\| 3.1194 \| 5930 \| 0.0001 \| - \|
	\| 3.1247 \| 5940 \| 0.0001 \| - \|
	\| 3.1299 \| 5950 \| 0.0002 \| - \|
	\| 3.1352 \| 5960 \| 0.0003 \| - \|
	\| 3.1405 \| 5970 \| 0.0003 \| - \|
	\| 3.1457 \| 5980 \| 0.0009 \| - \|
	\| 3.1510 \| 5990 \| 0.0001 \| - \|
	\| 3.1562 \| 6000 \| 0.0001 \| - \|
	\| 3.1615 \| 6010 \| 0.0002 \| - \|
	\| 3.1668 \| 6020 \| 0.0001 \| - \|
	\| 3.1720 \| 6030 \| 0.0001 \| - \|
	\| 3.1773 \| 6040 \| 0.0001 \| - \|
	\| 3.1825 \| 6050 \| 0.0002 \| - \|
	\| 3.1878 \| 6060 \| 0.0001 \| - \|
	\| 3.1931 \| 6070 \| 0.0001 \| - \|
	\| 3.1983 \| 6080 \| 0.0002 \| - \|
	\| 3.2036 \| 6090 \| 0.0001 \| - \|
	\| 3.2088 \| 6100 \| 0.0002 \| - \|
	\| 3.2141 \| 6110 \| 0.0003 \| - \|
	\| 3.2194 \| 6120 \| 0.0001 \| - \|
	\| 3.2246 \| 6130 \| 0.0001 \| - \|
	\| 3.2299 \| 6140 \| 0.0001 \| - \|
	\| 3.2351 \| 6150 \| 0.0001 \| - \|
	\| 3.2404 \| 6160 \| 0.0001 \| - \|
	\| 3.2457 \| 6170 \| 0.0001 \| - \|
	\| 3.2509 \| 6180 \| 0.0001 \| - \|
	\| 3.2562 \| 6190 \| 0.0001 \| - \|
	\| 3.2614 \| 6200 \| 0.0001 \| - \|
	\| 3.2667 \| 6210 \| 0.0002 \| - \|
	\| 3.2720 \| 6220 \| 0.0001 \| - \|
	\| 3.2772 \| 6230 \| 0.0001 \| - \|
	\| 3.2825 \| 6240 \| 0.0001 \| - \|
	\| 3.2877 \| 6250 \| 0.0002 \| - \|
	\| 3.2930 \| 6260 \| 0.0001 \| - \|
	\| 3.2983 \| 6270 \| 0.0001 \| - \|
	\| 3.3035 \| 6280 \| 0.0002 \| - \|
	\| 3.3088 \| 6290 \| 0.0001 \| - \|
	\| 3.3140 \| 6300 \| 0.0001 \| - \|
	\| 3.3193 \| 6310 \| 0.0001 \| - \|
	\| 3.3246 \| 6320 \| 0.0001 \| - \|
	\| 3.3298 \| 6330 \| 0.0 \| - \|
	\| 3.3351 \| 6340 \| 0.0003 \| - \|
	\| 3.3403 \| 6350 \| 0.0002 \| - \|
	\| 3.3456 \| 6360 \| 0.0001 \| - \|
	\| 3.3509 \| 6370 \| 0.0001 \| - \|
	\| 3.3561 \| 6380 \| 0.0003 \| - \|
	\| 3.3614 \| 6390 \| 0.0 \| - \|
	\| 3.3666 \| 6400 \| 0.0001 \| - \|
	\| 3.3719 \| 6410 \| 0.0001 \| - \|
	\| 3.3772 \| 6420 \| 0.0001 \| - \|
	\| 3.3824 \| 6430 \| 0.0001 \| - \|
	\| 3.3877 \| 6440 \| 0.0001 \| - \|
	\| 3.3930 \| 6450 \| 0.0003 \| - \|
	\| 3.3982 \| 6460 \| 0.0002 \| - \|
	\| 3.4035 \| 6470 \| 0.0001 \| - \|
	\| 3.4087 \| 6480 \| 0.0002 \| - \|
	\| 3.4140 \| 6490 \| 0.0003 \| - \|
	\| 3.4193 \| 6500 \| 0.0 \| - \|
	\| 3.4245 \| 6510 \| 0.0001 \| - \|
	\| 3.4298 \| 6520 \| 0.0002 \| - \|
	\| 3.4350 \| 6530 \| 0.0001 \| - \|
	\| 3.4403 \| 6540 \| 0.0001 \| - \|
	\| 3.4456 \| 6550 \| 0.0001 \| - \|
	\| 3.4508 \| 6560 \| 0.0001 \| - \|
	\| 3.4561 \| 6570 \| 0.0001 \| - \|
	\| 3.4613 \| 6580 \| 0.0001 \| - \|
	\| 3.4666 \| 6590 \| 0.0001 \| - \|
	\| 3.4719 \| 6600 \| 0.0001 \| - \|
	\| 3.4771 \| 6610 \| 0.0001 \| - \|
	\| 3.4824 \| 6620 \| 0.0003 \| - \|
	\| 3.4876 \| 6630 \| 0.0001 \| - \|
	\| 3.4929 \| 6640 \| 0.0001 \| - \|
	\| 3.4982 \| 6650 \| 0.0001 \| - \|
	\| 3.5034 \| 6660 \| 0.0002 \| - \|
	\| 3.5087 \| 6670 \| 0.0001 \| - \|
	\| 3.5139 \| 6680 \| 0.0007 \| - \|
	\| 3.5192 \| 6690 \| 0.0004 \| - \|
	\| 3.5245 \| 6700 \| 0.0001 \| - \|
	\| 3.5297 \| 6710 \| 0.0001 \| - \|
	\| 3.5350 \| 6720 \| 0.0001 \| - \|
	\| 3.5402 \| 6730 \| 0.0001 \| - \|
	\| 3.5455 \| 6740 \| 0.0001 \| - \|
	\| 3.5508 \| 6750 \| 0.0001 \| - \|
	\| 3.5560 \| 6760 \| 0.0001 \| - \|
	\| 3.5613 \| 6770 \| 0.0001 \| - \|
	\| 3.5665 \| 6780 \| 0.0001 \| - \|
	\| 3.5718 \| 6790 \| 0.0001 \| - \|
	\| 3.5771 \| 6800 \| 0.0 \| - \|
	\| 3.5823 \| 6810 \| 0.0001 \| - \|
	\| 3.5876 \| 6820 \| 0.0001 \| - \|
	\| 3.5928 \| 6830 \| 0.0004 \| - \|
	\| 3.5981 \| 6840 \| 0.0001 \| - \|
	\| 3.6034 \| 6850 \| 0.0001 \| - \|
	\| 3.6086 \| 6860 \| 0.0001 \| - \|
	\| 3.6139 \| 6870 \| 0.0 \| - \|
	\| 3.6191 \| 6880 \| 0.0001 \| - \|
	\| 3.6244 \| 6890 \| 0.0001 \| - \|
	\| 3.6297 \| 6900 \| 0.0001 \| - \|
	\| 3.6349 \| 6910 \| 0.0001 \| - \|
	\| 3.6402 \| 6920 \| 0.0002 \| - \|
	\| 3.6454 \| 6930 \| 0.0001 \| - \|
	\| 3.6507 \| 6940 \| 0.0001 \| - \|
	\| 3.6560 \| 6950 \| 0.0 \| - \|
	\| 3.6612 \| 6960 \| 0.0 \| - \|
	\| 3.6665 \| 6970 \| 0.0001 \| - \|
	\| 3.6718 \| 6980 \| 0.0001 \| - \|
	\| 3.6770 \| 6990 \| 0.0002 \| - \|
	\| 3.6823 \| 7000 \| 0.0001 \| - \|
	\| 3.6875 \| 7010 \| 0.0001 \| - \|
	\| 3.6928 \| 7020 \| 0.0001 \| - \|
	\| 3.6981 \| 7030 \| 0.0001 \| - \|
	\| 3.7033 \| 7040 \| 0.0001 \| - \|
	\| 3.7086 \| 7050 \| 0.0002 \| - \|
	\| 3.7138 \| 7060 \| 0.0001 \| - \|
	\| 3.7191 \| 7070 \| 0.0001 \| - \|
	\| 3.7244 \| 7080 \| 0.0001 \| - \|
	\| 3.7296 \| 7090 \| 0.0001 \| - \|
	\| 3.7349 \| 7100 \| 0.0001 \| - \|
	\| 3.7401 \| 7110 \| 0.0001 \| - \|
	\| 3.7454 \| 7120 \| 0.0001 \| - \|
	\| 3.7507 \| 7130 \| 0.0003 \| - \|
	\| 3.7559 \| 7140 \| 0.0001 \| - \|
	\| 3.7612 \| 7150 \| 0.0001 \| - \|
	\| 3.7664 \| 7160 \| 0.0002 \| - \|
	\| 3.7717 \| 7170 \| 0.0002 \| - \|
	\| 3.7770 \| 7180 \| 0.0001 \| - \|
	\| 3.7822 \| 7190 \| 0.0001 \| - \|
	\| 3.7875 \| 7200 \| 0.0001 \| - \|
	\| 3.7927 \| 7210 \| 0.0003 \| - \|
	\| 3.7980 \| 7220 \| 0.0001 \| - \|
	\| 3.8033 \| 7230 \| 0.0001 \| - \|
	\| 3.8085 \| 7240 \| 0.0001 \| - \|
	\| 3.8138 \| 7250 \| 0.0001 \| - \|
	\| 3.8190 \| 7260 \| 0.0001 \| - \|
	\| 3.8243 \| 7270 \| 0.0002 \| - \|
	\| 3.8296 \| 7280 \| 0.0002 \| - \|
	\| 3.8348 \| 7290 \| 0.0001 \| - \|
	\| 3.8401 \| 7300 \| 0.0001 \| - \|
	\| 3.8453 \| 7310 \| 0.0001 \| - \|
	\| 3.8506 \| 7320 \| 0.0001 \| - \|
	\| 3.8559 \| 7330 \| 0.0001 \| - \|
	\| 3.8611 \| 7340 \| 0.0006 \| - \|
	\| 3.8664 \| 7350 \| 0.0001 \| - \|
	\| 3.8716 \| 7360 \| 0.0001 \| - \|
	\| 3.8769 \| 7370 \| 0.0 \| - \|
	\| 3.8822 \| 7380 \| 0.0003 \| - \|
	\| 3.8874 \| 7390 \| 0.0001 \| - \|
	\| 3.8927 \| 7400 \| 0.0001 \| - \|
	\| 3.8979 \| 7410 \| 0.0001 \| - \|
	\| 3.9032 \| 7420 \| 0.0001 \| - \|
	\| 3.9085 \| 7430 \| 0.0002 \| - \|
	\| 3.9137 \| 7440 \| 0.0001 \| - \|
	\| 3.9190 \| 7450 \| 0.0002 \| - \|
	\| 3.9243 \| 7460 \| 0.0001 \| - \|
	\| 3.9295 \| 7470 \| 0.0001 \| - \|
	\| 3.9348 \| 7480 \| 0.0002 \| - \|
	\| 3.9400 \| 7490 \| 0.0001 \| - \|
	\| 3.9453 \| 7500 \| 0.0002 \| - \|
	\| 3.9506 \| 7510 \| 0.0001 \| - \|
	\| 3.9558 \| 7520 \| 0.0001 \| - \|
	\| 3.9611 \| 7530 \| 0.0001 \| - \|
	\| 3.9663 \| 7540 \| 0.0001 \| - \|
	\| 3.9716 \| 7550 \| 0.0001 \| - \|
	\| 3.9769 \| 7560 \| 0.0002 \| - \|
	\| 3.9821 \| 7570 \| 0.0001 \| - \|
	\| 3.9874 \| 7580 \| 0.0001 \| - \|
	\| 3.9926 \| 7590 \| 0.0001 \| - \|
	\| 3.9979 \| 7600 \| 0.0001 \| - \|
	\| 4.0 \| 7604 \| - \| 0.0319 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 3.0.1
	- Transformers: 4.37.0
	- PyTorch: 2.5.1+cu121
	- Datasets: 3.1.0
	- 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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