725_model_v4 / README.md

Push model using huggingface_hub.

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	---
	library_name: setfit
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	metrics:
	- accuracy
	- f1
	- precision
	- recall
	widget:
	- text: man, product/whatever is my new best friend. i like product but the integration
	of product into office and product is a lot of fun. i just spent the day feeding
	it my training presentation i'm preparing in my day job and it was very helpful.
	almost better than humans.
	- text: that's great news! product is the perfect platform to share these advanced
	product prompts and help more users get the most out of it!
	- text: after only one week's trial of the new product with brand enabled, i have
	replaced my default browser product that i was using for more than 7 years with
	new product. i no longer need to spend a lot of time finding answers from a bunch
	of search results and web pages. it's amazing
	- text: very impressive. brand is finally fighting back. i am just a little worried
	about the scalability of such a high context window size, since even in their
	demos it took quite a while to process everything. regardless, i am very interested
	in seeing what types of capabilities a >1m token size window can unleash.
	- text: product the way it shows the sources is so fucking cool, this new ai is amazing
	pipeline_tag: text-classification
	inference: true
	base_model: BAAI/bge-small-en-v1.5
	model-index:
	- name: SetFit with BAAI/bge-small-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: Unknown
	type: unknown
	split: test
	metrics:
	- type: accuracy
	value: 0.964
	name: Accuracy
	- type: f1
	value:
	- 0.9130434782608695
	- 0.888888888888889
	- 0.9779951100244498
	name: F1
	- type: precision
	value:
	- 0.9545454545454546
	- 1.0
	- 0.9615384615384616
	name: Precision
	- type: recall
	value:
	- 0.875
	- 0.8
	- 0.9950248756218906
	name: Recall
	---

	# SetFit with BAAI/bge-small-en-v1.5

	This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co./BAAI/bge-small-en-v1.5) 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: [BAAI/bge-small-en-v1.5](https://huggingface.co./BAAI/bge-small-en-v1.5)
	- Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
	- Maximum Sequence Length: 512 tokens
	- Number of Classes: 3 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 \|
	\|:--------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| neither \| <ul><li>'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'</li><li>"i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."</li><li>'thank you comrade i mean colleague. i will have brand summarize.'</li></ul> \|
	\| peak \| <ul><li>'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'</li><li>'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'</li><li>'just got access to personalization in brand!! totally unexpected. very happy'</li></ul> \|
	\| pit \| <ul><li>'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'</li><li>'brand product is def better at extracting numbers from images, product failed (pro version) twice...'</li><li>"the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/"</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \| F1 \| Precision \| Recall \|
	\|:--------\|:---------\|:------------------------------------------------------------\|:----------------------------------------------\|:---------------------------------\|
	\| all \| 0.964 \| [0.9130434782608695, 0.888888888888889, 0.9779951100244498] \| [0.9545454545454546, 1.0, 0.9615384615384616] \| [0.875, 0.8, 0.9950248756218906] \|

	## 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("jamiehudson/725_model_v4")
	# Run inference
	preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
	```

	<!--
	### 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 \| 31.6606 \| 98 \|

	\| Label \| Training Sample Count \|
	\|:--------\|:----------------------\|
	\| pit \| 277 \|
	\| peak \| 265 \|
	\| neither \| 1105 \|

	### Training Hyperparameters
	- batch_size: (32, 32)
	- num_epochs: (1, 1)
	- max_steps: -1
	- sampling_strategy: oversampling
	- body_learning_rate: (2e-05, 1e-05)
	- head_learning_rate: 0.01
	- loss: CosineSimilarityLoss
	- distance_metric: cosine_distance
	- margin: 0.25
	- end_to_end: False
	- use_amp: False
	- warmup_proportion: 0.1
	- seed: 42
	- eval_max_steps: -1
	- load_best_model_at_end: False

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:------:\|:-----:\|:-------------:\|:---------------:\|
	\| 0.0000 \| 1 \| 0.2683 \| - \|
	\| 0.0012 \| 50 \| 0.2643 \| - \|
	\| 0.0023 \| 100 \| 0.2432 \| - \|
	\| 0.0035 \| 150 \| 0.2623 \| - \|
	\| 0.0047 \| 200 \| 0.2527 \| - \|
	\| 0.0058 \| 250 \| 0.2252 \| - \|
	\| 0.0070 \| 300 \| 0.2362 \| - \|
	\| 0.0082 \| 350 \| 0.2334 \| - \|
	\| 0.0093 \| 400 \| 0.2189 \| - \|
	\| 0.0105 \| 450 \| 0.2144 \| - \|
	\| 0.0117 \| 500 \| 0.1971 \| - \|
	\| 0.0129 \| 550 \| 0.1565 \| - \|
	\| 0.0140 \| 600 \| 0.0816 \| - \|
	\| 0.0152 \| 650 \| 0.1417 \| - \|
	\| 0.0164 \| 700 \| 0.1051 \| - \|
	\| 0.0175 \| 750 \| 0.0686 \| - \|
	\| 0.0187 \| 800 \| 0.0394 \| - \|
	\| 0.0199 \| 850 \| 0.0947 \| - \|
	\| 0.0210 \| 900 \| 0.0468 \| - \|
	\| 0.0222 \| 950 \| 0.0143 \| - \|
	\| 0.0234 \| 1000 \| 0.0281 \| - \|
	\| 0.0245 \| 1050 \| 0.0329 \| - \|
	\| 0.0257 \| 1100 \| 0.0206 \| - \|
	\| 0.0269 \| 1150 \| 0.0113 \| - \|
	\| 0.0280 \| 1200 \| 0.0054 \| - \|
	\| 0.0292 \| 1250 \| 0.0056 \| - \|
	\| 0.0304 \| 1300 \| 0.0209 \| - \|
	\| 0.0315 \| 1350 \| 0.0064 \| - \|
	\| 0.0327 \| 1400 \| 0.0085 \| - \|
	\| 0.0339 \| 1450 \| 0.0025 \| - \|
	\| 0.0350 \| 1500 \| 0.0031 \| - \|
	\| 0.0362 \| 1550 \| 0.0024 \| - \|
	\| 0.0374 \| 1600 \| 0.0014 \| - \|
	\| 0.0386 \| 1650 \| 0.0019 \| - \|
	\| 0.0397 \| 1700 \| 0.0023 \| - \|
	\| 0.0409 \| 1750 \| 0.0014 \| - \|
	\| 0.0421 \| 1800 \| 0.002 \| - \|
	\| 0.0432 \| 1850 \| 0.001 \| - \|
	\| 0.0444 \| 1900 \| 0.001 \| - \|
	\| 0.0456 \| 1950 \| 0.0019 \| - \|
	\| 0.0467 \| 2000 \| 0.0017 \| - \|
	\| 0.0479 \| 2050 \| 0.001 \| - \|
	\| 0.0491 \| 2100 \| 0.0008 \| - \|
	\| 0.0502 \| 2150 \| 0.0011 \| - \|
	\| 0.0514 \| 2200 \| 0.0006 \| - \|
	\| 0.0526 \| 2250 \| 0.0012 \| - \|
	\| 0.0537 \| 2300 \| 0.0008 \| - \|
	\| 0.0549 \| 2350 \| 0.0014 \| - \|
	\| 0.0561 \| 2400 \| 0.0009 \| - \|
	\| 0.0572 \| 2450 \| 0.0009 \| - \|
	\| 0.0584 \| 2500 \| 0.001 \| - \|
	\| 0.0596 \| 2550 \| 0.0007 \| - \|
	\| 0.0607 \| 2600 \| 0.0007 \| - \|
	\| 0.0619 \| 2650 \| 0.0006 \| - \|
	\| 0.0631 \| 2700 \| 0.0004 \| - \|
	\| 0.0643 \| 2750 \| 0.0007 \| - \|
	\| 0.0654 \| 2800 \| 0.0005 \| - \|
	\| 0.0666 \| 2850 \| 0.0007 \| - \|
	\| 0.0678 \| 2900 \| 0.0007 \| - \|
	\| 0.0689 \| 2950 \| 0.0006 \| - \|
	\| 0.0701 \| 3000 \| 0.0005 \| - \|
	\| 0.0713 \| 3050 \| 0.0007 \| - \|
	\| 0.0724 \| 3100 \| 0.0008 \| - \|
	\| 0.0736 \| 3150 \| 0.0005 \| - \|
	\| 0.0748 \| 3200 \| 0.0005 \| - \|
	\| 0.0759 \| 3250 \| 0.0005 \| - \|
	\| 0.0771 \| 3300 \| 0.0006 \| - \|
	\| 0.0783 \| 3350 \| 0.0006 \| - \|
	\| 0.0794 \| 3400 \| 0.0006 \| - \|
	\| 0.0806 \| 3450 \| 0.0004 \| - \|
	\| 0.0818 \| 3500 \| 0.0005 \| - \|
	\| 0.0829 \| 3550 \| 0.0005 \| - \|
	\| 0.0841 \| 3600 \| 0.0005 \| - \|
	\| 0.0853 \| 3650 \| 0.0005 \| - \|
	\| 0.0864 \| 3700 \| 0.0006 \| - \|
	\| 0.0876 \| 3750 \| 0.0039 \| - \|
	\| 0.0888 \| 3800 \| 0.0004 \| - \|
	\| 0.0900 \| 3850 \| 0.0003 \| - \|
	\| 0.0911 \| 3900 \| 0.0004 \| - \|
	\| 0.0923 \| 3950 \| 0.0007 \| - \|
	\| 0.0935 \| 4000 \| 0.0003 \| - \|
	\| 0.0946 \| 4050 \| 0.0004 \| - \|
	\| 0.0958 \| 4100 \| 0.0003 \| - \|
	\| 0.0970 \| 4150 \| 0.0003 \| - \|
	\| 0.0981 \| 4200 \| 0.0004 \| - \|
	\| 0.0993 \| 4250 \| 0.0003 \| - \|
	\| 0.1005 \| 4300 \| 0.0004 \| - \|
	\| 0.1016 \| 4350 \| 0.0003 \| - \|
	\| 0.1028 \| 4400 \| 0.0004 \| - \|
	\| 0.1040 \| 4450 \| 0.0003 \| - \|
	\| 0.1051 \| 4500 \| 0.0004 \| - \|
	\| 0.1063 \| 4550 \| 0.0003 \| - \|
	\| 0.1075 \| 4600 \| 0.0003 \| - \|
	\| 0.1086 \| 4650 \| 0.0003 \| - \|
	\| 0.1098 \| 4700 \| 0.0003 \| - \|
	\| 0.1110 \| 4750 \| 0.0016 \| - \|
	\| 0.1121 \| 4800 \| 0.0003 \| - \|
	\| 0.1133 \| 4850 \| 0.0002 \| - \|
	\| 0.1145 \| 4900 \| 0.0003 \| - \|
	\| 0.1157 \| 4950 \| 0.0002 \| - \|
	\| 0.1168 \| 5000 \| 0.0003 \| - \|
	\| 0.1180 \| 5050 \| 0.0003 \| - \|
	\| 0.1192 \| 5100 \| 0.0003 \| - \|
	\| 0.1203 \| 5150 \| 0.0002 \| - \|
	\| 0.1215 \| 5200 \| 0.0003 \| - \|
	\| 0.1227 \| 5250 \| 0.0002 \| - \|
	\| 0.1238 \| 5300 \| 0.0178 \| - \|
	\| 0.1250 \| 5350 \| 0.0014 \| - \|
	\| 0.1262 \| 5400 \| 0.002 \| - \|
	\| 0.1273 \| 5450 \| 0.0002 \| - \|
	\| 0.1285 \| 5500 \| 0.0008 \| - \|
	\| 0.1297 \| 5550 \| 0.0003 \| - \|
	\| 0.1308 \| 5600 \| 0.0002 \| - \|
	\| 0.1320 \| 5650 \| 0.0002 \| - \|
	\| 0.1332 \| 5700 \| 0.0002 \| - \|
	\| 0.1343 \| 5750 \| 0.0003 \| - \|
	\| 0.1355 \| 5800 \| 0.0002 \| - \|
	\| 0.1367 \| 5850 \| 0.0003 \| - \|
	\| 0.1378 \| 5900 \| 0.0003 \| - \|
	\| 0.1390 \| 5950 \| 0.0002 \| - \|
	\| 0.1402 \| 6000 \| 0.0002 \| - \|
	\| 0.1414 \| 6050 \| 0.0002 \| - \|
	\| 0.1425 \| 6100 \| 0.0002 \| - \|
	\| 0.1437 \| 6150 \| 0.0002 \| - \|
	\| 0.1449 \| 6200 \| 0.0002 \| - \|
	\| 0.1460 \| 6250 \| 0.0019 \| - \|
	\| 0.1472 \| 6300 \| 0.0005 \| - \|
	\| 0.1484 \| 6350 \| 0.0002 \| - \|
	\| 0.1495 \| 6400 \| 0.0005 \| - \|
	\| 0.1507 \| 6450 \| 0.0003 \| - \|
	\| 0.1519 \| 6500 \| 0.0208 \| - \|
	\| 0.1530 \| 6550 \| 0.0003 \| - \|
	\| 0.1542 \| 6600 \| 0.0002 \| - \|
	\| 0.1554 \| 6650 \| 0.0002 \| - \|
	\| 0.1565 \| 6700 \| 0.0002 \| - \|
	\| 0.1577 \| 6750 \| 0.0002 \| - \|
	\| 0.1589 \| 6800 \| 0.0002 \| - \|
	\| 0.1600 \| 6850 \| 0.0002 \| - \|
	\| 0.1612 \| 6900 \| 0.0104 \| - \|
	\| 0.1624 \| 6950 \| 0.0001 \| - \|
	\| 0.1635 \| 7000 \| 0.0002 \| - \|
	\| 0.1647 \| 7050 \| 0.0002 \| - \|
	\| 0.1659 \| 7100 \| 0.0002 \| - \|
	\| 0.1671 \| 7150 \| 0.0001 \| - \|
	\| 0.1682 \| 7200 \| 0.0002 \| - \|
	\| 0.1694 \| 7250 \| 0.0002 \| - \|
	\| 0.1706 \| 7300 \| 0.0003 \| - \|
	\| 0.1717 \| 7350 \| 0.0002 \| - \|
	\| 0.1729 \| 7400 \| 0.0001 \| - \|
	\| 0.1741 \| 7450 \| 0.0001 \| - \|
	\| 0.1752 \| 7500 \| 0.0002 \| - \|
	\| 0.1764 \| 7550 \| 0.0004 \| - \|
	\| 0.1776 \| 7600 \| 0.0002 \| - \|
	\| 0.1787 \| 7650 \| 0.0005 \| - \|
	\| 0.1799 \| 7700 \| 0.0001 \| - \|
	\| 0.1811 \| 7750 \| 0.0002 \| - \|
	\| 0.1822 \| 7800 \| 0.0002 \| - \|
	\| 0.1834 \| 7850 \| 0.0001 \| - \|
	\| 0.1846 \| 7900 \| 0.0002 \| - \|
	\| 0.1857 \| 7950 \| 0.0002 \| - \|
	\| 0.1869 \| 8000 \| 0.0002 \| - \|
	\| 0.1881 \| 8050 \| 0.0001 \| - \|
	\| 0.1892 \| 8100 \| 0.0002 \| - \|
	\| 0.1904 \| 8150 \| 0.0001 \| - \|
	\| 0.1916 \| 8200 \| 0.0001 \| - \|
	\| 0.1928 \| 8250 \| 0.0001 \| - \|
	\| 0.1939 \| 8300 \| 0.0001 \| - \|
	\| 0.1951 \| 8350 \| 0.0001 \| - \|
	\| 0.1963 \| 8400 \| 0.0002 \| - \|
	\| 0.1974 \| 8450 \| 0.0002 \| - \|
	\| 0.1986 \| 8500 \| 0.0002 \| - \|
	\| 0.1998 \| 8550 \| 0.0002 \| - \|
	\| 0.2009 \| 8600 \| 0.0001 \| - \|
	\| 0.2021 \| 8650 \| 0.0001 \| - \|
	\| 0.2033 \| 8700 \| 0.0001 \| - \|
	\| 0.2044 \| 8750 \| 0.0001 \| - \|
	\| 0.2056 \| 8800 \| 0.0001 \| - \|
	\| 0.2068 \| 8850 \| 0.0001 \| - \|
	\| 0.2079 \| 8900 \| 0.0001 \| - \|
	\| 0.2091 \| 8950 \| 0.0001 \| - \|
	\| 0.2103 \| 9000 \| 0.0001 \| - \|
	\| 0.2114 \| 9050 \| 0.0001 \| - \|
	\| 0.2126 \| 9100 \| 0.0001 \| - \|
	\| 0.2138 \| 9150 \| 0.0001 \| - \|
	\| 0.2149 \| 9200 \| 0.0001 \| - \|
	\| 0.2161 \| 9250 \| 0.0002 \| - \|
	\| 0.2173 \| 9300 \| 0.0001 \| - \|
	\| 0.2185 \| 9350 \| 0.0002 \| - \|
	\| 0.2196 \| 9400 \| 0.0001 \| - \|
	\| 0.2208 \| 9450 \| 0.0001 \| - \|
	\| 0.2220 \| 9500 \| 0.0001 \| - \|
	\| 0.2231 \| 9550 \| 0.0001 \| - \|
	\| 0.2243 \| 9600 \| 0.0001 \| - \|
	\| 0.2255 \| 9650 \| 0.0002 \| - \|
	\| 0.2266 \| 9700 \| 0.0002 \| - \|
	\| 0.2278 \| 9750 \| 0.0001 \| - \|
	\| 0.2290 \| 9800 \| 0.0001 \| - \|
	\| 0.2301 \| 9850 \| 0.0002 \| - \|
	\| 0.2313 \| 9900 \| 0.0001 \| - \|
	\| 0.2325 \| 9950 \| 0.0001 \| - \|
	\| 0.2336 \| 10000 \| 0.0001 \| - \|
	\| 0.2348 \| 10050 \| 0.0001 \| - \|
	\| 0.2360 \| 10100 \| 0.0001 \| - \|
	\| 0.2371 \| 10150 \| 0.0001 \| - \|
	\| 0.2383 \| 10200 \| 0.0001 \| - \|
	\| 0.2395 \| 10250 \| 0.0001 \| - \|
	\| 0.2406 \| 10300 \| 0.0001 \| - \|
	\| 0.2418 \| 10350 \| 0.0001 \| - \|
	\| 0.2430 \| 10400 \| 0.0001 \| - \|
	\| 0.2442 \| 10450 \| 0.0001 \| - \|
	\| 0.2453 \| 10500 \| 0.0001 \| - \|
	\| 0.2465 \| 10550 \| 0.0001 \| - \|
	\| 0.2477 \| 10600 \| 0.0001 \| - \|
	\| 0.2488 \| 10650 \| 0.0001 \| - \|
	\| 0.2500 \| 10700 \| 0.0001 \| - \|
	\| 0.2512 \| 10750 \| 0.0001 \| - \|
	\| 0.2523 \| 10800 \| 0.0001 \| - \|
	\| 0.2535 \| 10850 \| 0.0001 \| - \|
	\| 0.2547 \| 10900 \| 0.0001 \| - \|
	\| 0.2558 \| 10950 \| 0.0001 \| - \|
	\| 0.2570 \| 11000 \| 0.0002 \| - \|
	\| 0.2582 \| 11050 \| 0.0001 \| - \|
	\| 0.2593 \| 11100 \| 0.0003 \| - \|
	\| 0.2605 \| 11150 \| 0.0001 \| - \|
	\| 0.2617 \| 11200 \| 0.0001 \| - \|
	\| 0.2628 \| 11250 \| 0.0001 \| - \|
	\| 0.2640 \| 11300 \| 0.0001 \| - \|
	\| 0.2652 \| 11350 \| 0.0001 \| - \|
	\| 0.2663 \| 11400 \| 0.0001 \| - \|
	\| 0.2675 \| 11450 \| 0.0001 \| - \|
	\| 0.2687 \| 11500 \| 0.0001 \| - \|
	\| 0.2699 \| 11550 \| 0.0001 \| - \|
	\| 0.2710 \| 11600 \| 0.0001 \| - \|
	\| 0.2722 \| 11650 \| 0.0001 \| - \|
	\| 0.2734 \| 11700 \| 0.0001 \| - \|
	\| 0.2745 \| 11750 \| 0.0001 \| - \|
	\| 0.2757 \| 11800 \| 0.0001 \| - \|
	\| 0.2769 \| 11850 \| 0.0001 \| - \|
	\| 0.2780 \| 11900 \| 0.0001 \| - \|
	\| 0.2792 \| 11950 \| 0.0001 \| - \|
	\| 0.2804 \| 12000 \| 0.0001 \| - \|
	\| 0.2815 \| 12050 \| 0.0001 \| - \|
	\| 0.2827 \| 12100 \| 0.0137 \| - \|
	\| 0.2839 \| 12150 \| 0.0001 \| - \|
	\| 0.2850 \| 12200 \| 0.0001 \| - \|
	\| 0.2862 \| 12250 \| 0.0001 \| - \|
	\| 0.2874 \| 12300 \| 0.0001 \| - \|
	\| 0.2885 \| 12350 \| 0.0001 \| - \|
	\| 0.2897 \| 12400 \| 0.0001 \| - \|
	\| 0.2909 \| 12450 \| 0.0001 \| - \|
	\| 0.2920 \| 12500 \| 0.0001 \| - \|
	\| 0.2932 \| 12550 \| 0.0001 \| - \|
	\| 0.2944 \| 12600 \| 0.0001 \| - \|
	\| 0.2956 \| 12650 \| 0.0001 \| - \|
	\| 0.2967 \| 12700 \| 0.0 \| - \|
	\| 0.2979 \| 12750 \| 0.0001 \| - \|
	\| 0.2991 \| 12800 \| 0.0001 \| - \|
	\| 0.3002 \| 12850 \| 0.0001 \| - \|
	\| 0.3014 \| 12900 \| 0.0001 \| - \|
	\| 0.3026 \| 12950 \| 0.0001 \| - \|
	\| 0.3037 \| 13000 \| 0.0001 \| - \|
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	\| 0.3061 \| 13100 \| 0.0001 \| - \|
	\| 0.3072 \| 13150 \| 0.0001 \| - \|
	\| 0.3084 \| 13200 \| 0.0001 \| - \|
	\| 0.3096 \| 13250 \| 0.0001 \| - \|
	\| 0.3107 \| 13300 \| 0.0001 \| - \|
	\| 0.3119 \| 13350 \| 0.0001 \| - \|
	\| 0.3131 \| 13400 \| 0.0001 \| - \|
	\| 0.3142 \| 13450 \| 0.0001 \| - \|
	\| 0.3154 \| 13500 \| 0.0001 \| - \|
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	\| 0.3201 \| 13700 \| 0.0001 \| - \|
	\| 0.3213 \| 13750 \| 0.0001 \| - \|
	\| 0.3224 \| 13800 \| 0.0001 \| - \|
	\| 0.3236 \| 13850 \| 0.0 \| - \|
	\| 0.3248 \| 13900 \| 0.0001 \| - \|
	\| 0.3259 \| 13950 \| 0.0001 \| - \|
	\| 0.3271 \| 14000 \| 0.0001 \| - \|
	\| 0.3283 \| 14050 \| 0.0002 \| - \|
	\| 0.3294 \| 14100 \| 0.0001 \| - \|
	\| 0.3306 \| 14150 \| 0.0001 \| - \|
	\| 0.3318 \| 14200 \| 0.0001 \| - \|
	\| 0.3329 \| 14250 \| 0.0001 \| - \|
	\| 0.3341 \| 14300 \| 0.0001 \| - \|
	\| 0.3353 \| 14350 \| 0.0001 \| - \|
	\| 0.3364 \| 14400 \| 0.0001 \| - \|
	\| 0.3376 \| 14450 \| 0.0001 \| - \|
	\| 0.3388 \| 14500 \| 0.0001 \| - \|
	\| 0.3399 \| 14550 \| 0.0001 \| - \|
	\| 0.3411 \| 14600 \| 0.0001 \| - \|
	\| 0.3423 \| 14650 \| 0.0001 \| - \|
	\| 0.3434 \| 14700 \| 0.0001 \| - \|
	\| 0.3446 \| 14750 \| 0.0001 \| - \|
	\| 0.3458 \| 14800 \| 0.0001 \| - \|
	\| 0.3470 \| 14850 \| 0.0001 \| - \|
	\| 0.3481 \| 14900 \| 0.0001 \| - \|
	\| 0.3493 \| 14950 \| 0.0 \| - \|
	\| 0.3505 \| 15000 \| 0.0001 \| - \|
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	\| 0.3528 \| 15100 \| 0.0 \| - \|
	\| 0.3540 \| 15150 \| 0.0001 \| - \|
	\| 0.3551 \| 15200 \| 0.0001 \| - \|
	\| 0.3563 \| 15250 \| 0.0001 \| - \|
	\| 0.3575 \| 15300 \| 0.0001 \| - \|
	\| 0.3586 \| 15350 \| 0.0001 \| - \|
	\| 0.3598 \| 15400 \| 0.0001 \| - \|
	\| 0.3610 \| 15450 \| 0.0001 \| - \|
	\| 0.3621 \| 15500 \| 0.0001 \| - \|
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	\| 0.3645 \| 15600 \| 0.0002 \| - \|
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	\| 0.3692 \| 15800 \| 0.0001 \| - \|
	\| 0.3703 \| 15850 \| 0.0001 \| - \|
	\| 0.3715 \| 15900 \| 0.0001 \| - \|
	\| 0.3727 \| 15950 \| 0.0 \| - \|
	\| 0.3738 \| 16000 \| 0.0 \| - \|
	\| 0.3750 \| 16050 \| 0.0 \| - \|
	\| 0.3762 \| 16100 \| 0.0 \| - \|
	\| 0.3773 \| 16150 \| 0.0001 \| - \|
	\| 0.3785 \| 16200 \| 0.0001 \| - \|
	\| 0.3797 \| 16250 \| 0.0001 \| - \|
	\| 0.3808 \| 16300 \| 0.0001 \| - \|
	\| 0.3820 \| 16350 \| 0.0001 \| - \|
	\| 0.3832 \| 16400 \| 0.0001 \| - \|
	\| 0.3843 \| 16450 \| 0.0 \| - \|
	\| 0.3855 \| 16500 \| 0.0001 \| - \|
	\| 0.3867 \| 16550 \| 0.0 \| - \|
	\| 0.3878 \| 16600 \| 0.0001 \| - \|
	\| 0.3890 \| 16650 \| 0.0001 \| - \|
	\| 0.3902 \| 16700 \| 0.0001 \| - \|
	\| 0.3913 \| 16750 \| 0.0001 \| - \|
	\| 0.3925 \| 16800 \| 0.0002 \| - \|
	\| 0.3937 \| 16850 \| 0.0002 \| - \|
	\| 0.3949 \| 16900 \| 0.0 \| - \|
	\| 0.3960 \| 16950 \| 0.0 \| - \|
	\| 0.3972 \| 17000 \| 0.0 \| - \|
	\| 0.3984 \| 17050 \| 0.0001 \| - \|
	\| 0.3995 \| 17100 \| 0.0001 \| - \|
	\| 0.4007 \| 17150 \| 0.0001 \| - \|
	\| 0.4019 \| 17200 \| 0.0001 \| - \|
	\| 0.4030 \| 17250 \| 0.0 \| - \|
	\| 0.4042 \| 17300 \| 0.0 \| - \|
	\| 0.4054 \| 17350 \| 0.0279 \| - \|
	\| 0.4065 \| 17400 \| 0.0 \| - \|
	\| 0.4077 \| 17450 \| 0.0 \| - \|
	\| 0.4089 \| 17500 \| 0.0 \| - \|
	\| 0.4100 \| 17550 \| 0.0 \| - \|
	\| 0.4112 \| 17600 \| 0.0001 \| - \|
	\| 0.4124 \| 17650 \| 0.0 \| - \|
	\| 0.4135 \| 17700 \| 0.028 \| - \|
	\| 0.4147 \| 17750 \| 0.0 \| - \|
	\| 0.4159 \| 17800 \| 0.0 \| - \|
	\| 0.4170 \| 17850 \| 0.0 \| - \|
	\| 0.4182 \| 17900 \| 0.0 \| - \|
	\| 0.4194 \| 17950 \| 0.0001 \| - \|
	\| 0.4206 \| 18000 \| 0.0 \| - \|
	\| 0.4217 \| 18050 \| 0.0 \| - \|
	\| 0.4229 \| 18100 \| 0.0001 \| - \|
	\| 0.4241 \| 18150 \| 0.0 \| - \|
	\| 0.4252 \| 18200 \| 0.0 \| - \|
	\| 0.4264 \| 18250 \| 0.0 \| - \|
	\| 0.4276 \| 18300 \| 0.0 \| - \|
	\| 0.4287 \| 18350 \| 0.0 \| - \|
	\| 0.4299 \| 18400 \| 0.0 \| - \|
	\| 0.4311 \| 18450 \| 0.0001 \| - \|
	\| 0.4322 \| 18500 \| 0.0001 \| - \|
	\| 0.4334 \| 18550 \| 0.0001 \| - \|
	\| 0.4346 \| 18600 \| 0.0001 \| - \|
	\| 0.4357 \| 18650 \| 0.0 \| - \|
	\| 0.4369 \| 18700 \| 0.0 \| - \|
	\| 0.4381 \| 18750 \| 0.0001 \| - \|
	\| 0.4392 \| 18800 \| 0.0001 \| - \|
	\| 0.4404 \| 18850 \| 0.0 \| - \|
	\| 0.4416 \| 18900 \| 0.0001 \| - \|
	\| 0.4427 \| 18950 \| 0.0001 \| - \|
	\| 0.4439 \| 19000 \| 0.0 \| - \|
	\| 0.4451 \| 19050 \| 0.0 \| - \|
	\| 0.4463 \| 19100 \| 0.0001 \| - \|
	\| 0.4474 \| 19150 \| 0.0 \| - \|
	\| 0.4486 \| 19200 \| 0.0001 \| - \|
	\| 0.4498 \| 19250 \| 0.0 \| - \|
	\| 0.4509 \| 19300 \| 0.0001 \| - \|
	\| 0.4521 \| 19350 \| 0.0001 \| - \|
	\| 0.4533 \| 19400 \| 0.0001 \| - \|
	\| 0.4544 \| 19450 \| 0.0 \| - \|
	\| 0.4556 \| 19500 \| 0.0001 \| - \|
	\| 0.4568 \| 19550 \| 0.0001 \| - \|
	\| 0.4579 \| 19600 \| 0.0001 \| - \|
	\| 0.4591 \| 19650 \| 0.0001 \| - \|
	\| 0.4603 \| 19700 \| 0.0001 \| - \|
	\| 0.4614 \| 19750 \| 0.0001 \| - \|
	\| 0.4626 \| 19800 \| 0.0 \| - \|
	\| 0.4638 \| 19850 \| 0.0 \| - \|
	\| 0.4649 \| 19900 \| 0.0001 \| - \|
	\| 0.4661 \| 19950 \| 0.0 \| - \|
	\| 0.4673 \| 20000 \| 0.0 \| - \|
	\| 0.4684 \| 20050 \| 0.0 \| - \|
	\| 0.4696 \| 20100 \| 0.0 \| - \|
	\| 0.4708 \| 20150 \| 0.0 \| - \|
	\| 0.4720 \| 20200 \| 0.0 \| - \|
	\| 0.4731 \| 20250 \| 0.0 \| - \|
	\| 0.4743 \| 20300 \| 0.0001 \| - \|
	\| 0.4755 \| 20350 \| 0.0001 \| - \|
	\| 0.4766 \| 20400 \| 0.0001 \| - \|
	\| 0.4778 \| 20450 \| 0.0 \| - \|
	\| 0.4790 \| 20500 \| 0.0 \| - \|
	\| 0.4801 \| 20550 \| 0.0001 \| - \|
	\| 0.4813 \| 20600 \| 0.0 \| - \|
	\| 0.4825 \| 20650 \| 0.0005 \| - \|
	\| 0.4836 \| 20700 \| 0.0001 \| - \|
	\| 0.4848 \| 20750 \| 0.0001 \| - \|
	\| 0.4860 \| 20800 \| 0.0 \| - \|
	\| 0.4871 \| 20850 \| 0.0001 \| - \|
	\| 0.4883 \| 20900 \| 0.0001 \| - \|
	\| 0.4895 \| 20950 \| 0.0 \| - \|
	\| 0.4906 \| 21000 \| 0.0 \| - \|
	\| 0.4918 \| 21050 \| 0.0 \| - \|
	\| 0.4930 \| 21100 \| 0.0 \| - \|
	\| 0.4941 \| 21150 \| 0.0001 \| - \|
	\| 0.4953 \| 21200 \| 0.0 \| - \|
	\| 0.4965 \| 21250 \| 0.0001 \| - \|
	\| 0.4977 \| 21300 \| 0.0 \| - \|
	\| 0.4988 \| 21350 \| 0.0001 \| - \|
	\| 0.5000 \| 21400 \| 0.0001 \| - \|
	\| 0.5012 \| 21450 \| 0.0 \| - \|
	\| 0.5023 \| 21500 \| 0.0 \| - \|
	\| 0.5035 \| 21550 \| 0.0 \| - \|
	\| 0.5047 \| 21600 \| 0.0001 \| - \|
	\| 0.5058 \| 21650 \| 0.0 \| - \|
	\| 0.5070 \| 21700 \| 0.0 \| - \|
	\| 0.5082 \| 21750 \| 0.0 \| - \|
	\| 0.5093 \| 21800 \| 0.0 \| - \|
	\| 0.5105 \| 21850 \| 0.0 \| - \|
	\| 0.5117 \| 21900 \| 0.0001 \| - \|
	\| 0.5128 \| 21950 \| 0.0 \| - \|
	\| 0.5140 \| 22000 \| 0.0 \| - \|
	\| 0.5152 \| 22050 \| 0.0 \| - \|
	\| 0.5163 \| 22100 \| 0.0 \| - \|
	\| 0.5175 \| 22150 \| 0.0 \| - \|
	\| 0.5187 \| 22200 \| 0.0001 \| - \|
	\| 0.5198 \| 22250 \| 0.0 \| - \|
	\| 0.5210 \| 22300 \| 0.0001 \| - \|
	\| 0.5222 \| 22350 \| 0.0 \| - \|
	\| 0.5234 \| 22400 \| 0.0001 \| - \|
	\| 0.5245 \| 22450 \| 0.0001 \| - \|
	\| 0.5257 \| 22500 \| 0.0 \| - \|
	\| 0.5269 \| 22550 \| 0.0 \| - \|
	\| 0.5280 \| 22600 \| 0.0 \| - \|
	\| 0.5292 \| 22650 \| 0.0 \| - \|
	\| 0.5304 \| 22700 \| 0.0 \| - \|
	\| 0.5315 \| 22750 \| 0.0 \| - \|
	\| 0.5327 \| 22800 \| 0.0 \| - \|
	\| 0.5339 \| 22850 \| 0.0 \| - \|
	\| 0.5350 \| 22900 \| 0.0001 \| - \|
	\| 0.5362 \| 22950 \| 0.0 \| - \|
	\| 0.5374 \| 23000 \| 0.0 \| - \|
	\| 0.5385 \| 23050 \| 0.0001 \| - \|
	\| 0.5397 \| 23100 \| 0.0 \| - \|
	\| 0.5409 \| 23150 \| 0.0 \| - \|
	\| 0.5420 \| 23200 \| 0.0001 \| - \|
	\| 0.5432 \| 23250 \| 0.0 \| - \|
	\| 0.5444 \| 23300 \| 0.0001 \| - \|
	\| 0.5455 \| 23350 \| 0.0001 \| - \|
	\| 0.5467 \| 23400 \| 0.0 \| - \|
	\| 0.5479 \| 23450 \| 0.0 \| - \|
	\| 0.5491 \| 23500 \| 0.0001 \| - \|
	\| 0.5502 \| 23550 \| 0.0 \| - \|
	\| 0.5514 \| 23600 \| 0.0001 \| - \|
	\| 0.5526 \| 23650 \| 0.0 \| - \|
	\| 0.5537 \| 23700 \| 0.0 \| - \|
	\| 0.5549 \| 23750 \| 0.0001 \| - \|
	\| 0.5561 \| 23800 \| 0.0 \| - \|
	\| 0.5572 \| 23850 \| 0.0 \| - \|
	\| 0.5584 \| 23900 \| 0.0 \| - \|
	\| 0.5596 \| 23950 \| 0.0 \| - \|
	\| 0.5607 \| 24000 \| 0.0 \| - \|
	\| 0.5619 \| 24050 \| 0.0 \| - \|
	\| 0.5631 \| 24100 \| 0.0001 \| - \|
	\| 0.5642 \| 24150 \| 0.0001 \| - \|
	\| 0.5654 \| 24200 \| 0.0 \| - \|
	\| 0.5666 \| 24250 \| 0.0 \| - \|
	\| 0.5677 \| 24300 \| 0.0001 \| - \|
	\| 0.5689 \| 24350 \| 0.0 \| - \|
	\| 0.5701 \| 24400 \| 0.0001 \| - \|
	\| 0.5712 \| 24450 \| 0.0 \| - \|
	\| 0.5724 \| 24500 \| 0.0 \| - \|
	\| 0.5736 \| 24550 \| 0.0 \| - \|
	\| 0.5748 \| 24600 \| 0.0029 \| - \|
	\| 0.5759 \| 24650 \| 0.0 \| - \|
	\| 0.5771 \| 24700 \| 0.0 \| - \|
	\| 0.5783 \| 24750 \| 0.0 \| - \|
	\| 0.5794 \| 24800 \| 0.0 \| - \|
	\| 0.5806 \| 24850 \| 0.0 \| - \|
	\| 0.5818 \| 24900 \| 0.0 \| - \|
	\| 0.5829 \| 24950 \| 0.0001 \| - \|
	\| 0.5841 \| 25000 \| 0.0 \| - \|
	\| 0.5853 \| 25050 \| 0.0 \| - \|
	\| 0.5864 \| 25100 \| 0.0001 \| - \|
	\| 0.5876 \| 25150 \| 0.0 \| - \|
	\| 0.5888 \| 25200 \| 0.0 \| - \|
	\| 0.5899 \| 25250 \| 0.0 \| - \|
	\| 0.5911 \| 25300 \| 0.0001 \| - \|
	\| 0.5923 \| 25350 \| 0.0 \| - \|
	\| 0.5934 \| 25400 \| 0.0001 \| - \|
	\| 0.5946 \| 25450 \| 0.0 \| - \|
	\| 0.5958 \| 25500 \| 0.0 \| - \|
	\| 0.5969 \| 25550 \| 0.0 \| - \|
	\| 0.5981 \| 25600 \| 0.0 \| - \|
	\| 0.5993 \| 25650 \| 0.0 \| - \|
	\| 0.6005 \| 25700 \| 0.0 \| - \|
	\| 0.6016 \| 25750 \| 0.0 \| - \|
	\| 0.6028 \| 25800 \| 0.0 \| - \|
	\| 0.6040 \| 25850 \| 0.0 \| - \|
	\| 0.6051 \| 25900 \| 0.0 \| - \|
	\| 0.6063 \| 25950 \| 0.0 \| - \|
	\| 0.6075 \| 26000 \| 0.0 \| - \|
	\| 0.6086 \| 26050 \| 0.0 \| - \|
	\| 0.6098 \| 26100 \| 0.0 \| - \|
	\| 0.6110 \| 26150 \| 0.0 \| - \|
	\| 0.6121 \| 26200 \| 0.0 \| - \|
	\| 0.6133 \| 26250 \| 0.0 \| - \|
	\| 0.6145 \| 26300 \| 0.0 \| - \|
	\| 0.6156 \| 26350 \| 0.0001 \| - \|
	\| 0.6168 \| 26400 \| 0.0 \| - \|
	\| 0.6180 \| 26450 \| 0.0 \| - \|
	\| 0.6191 \| 26500 \| 0.0 \| - \|
	\| 0.6203 \| 26550 \| 0.0 \| - \|
	\| 0.6215 \| 26600 \| 0.0001 \| - \|
	\| 0.6226 \| 26650 \| 0.0 \| - \|
	\| 0.6238 \| 26700 \| 0.0 \| - \|
	\| 0.6250 \| 26750 \| 0.0 \| - \|
	\| 0.6262 \| 26800 \| 0.0 \| - \|
	\| 0.6273 \| 26850 \| 0.0 \| - \|
	\| 0.6285 \| 26900 \| 0.0 \| - \|
	\| 0.6297 \| 26950 \| 0.0 \| - \|
	\| 0.6308 \| 27000 \| 0.0 \| - \|
	\| 0.6320 \| 27050 \| 0.0001 \| - \|
	\| 0.6332 \| 27100 \| 0.0 \| - \|
	\| 0.6343 \| 27150 \| 0.0 \| - \|
	\| 0.6355 \| 27200 \| 0.0 \| - \|
	\| 0.6367 \| 27250 \| 0.0001 \| - \|
	\| 0.6378 \| 27300 \| 0.0 \| - \|
	\| 0.6390 \| 27350 \| 0.0 \| - \|
	\| 0.6402 \| 27400 \| 0.0 \| - \|
	\| 0.6413 \| 27450 \| 0.0 \| - \|
	\| 0.6425 \| 27500 \| 0.0 \| - \|
	\| 0.6437 \| 27550 \| 0.0 \| - \|
	\| 0.6448 \| 27600 \| 0.0001 \| - \|
	\| 0.6460 \| 27650 \| 0.0001 \| - \|
	\| 0.6472 \| 27700 \| 0.0 \| - \|
	\| 0.6483 \| 27750 \| 0.0 \| - \|
	\| 0.6495 \| 27800 \| 0.0 \| - \|
	\| 0.6507 \| 27850 \| 0.0 \| - \|
	\| 0.6519 \| 27900 \| 0.0 \| - \|
	\| 0.6530 \| 27950 \| 0.0 \| - \|
	\| 0.6542 \| 28000 \| 0.0 \| - \|
	\| 0.6554 \| 28050 \| 0.0 \| - \|
	\| 0.6565 \| 28100 \| 0.0 \| - \|
	\| 0.6577 \| 28150 \| 0.0 \| - \|
	\| 0.6589 \| 28200 \| 0.0 \| - \|
	\| 0.6600 \| 28250 \| 0.0 \| - \|
	\| 0.6612 \| 28300 \| 0.0 \| - \|
	\| 0.6624 \| 28350 \| 0.0 \| - \|
	\| 0.6635 \| 28400 \| 0.0 \| - \|
	\| 0.6647 \| 28450 \| 0.0 \| - \|
	\| 0.6659 \| 28500 \| 0.0 \| - \|
	\| 0.6670 \| 28550 \| 0.0 \| - \|
	\| 0.6682 \| 28600 \| 0.0001 \| - \|
	\| 0.6694 \| 28650 \| 0.0 \| - \|
	\| 0.6705 \| 28700 \| 0.0 \| - \|
	\| 0.6717 \| 28750 \| 0.0 \| - \|
	\| 0.6729 \| 28800 \| 0.0 \| - \|
	\| 0.6740 \| 28850 \| 0.0 \| - \|
	\| 0.6752 \| 28900 \| 0.0 \| - \|
	\| 0.6764 \| 28950 \| 0.0 \| - \|
	\| 0.6776 \| 29000 \| 0.0 \| - \|
	\| 0.6787 \| 29050 \| 0.0 \| - \|
	\| 0.6799 \| 29100 \| 0.0 \| - \|
	\| 0.6811 \| 29150 \| 0.0001 \| - \|
	\| 0.6822 \| 29200 \| 0.0 \| - \|
	\| 0.6834 \| 29250 \| 0.0 \| - \|
	\| 0.6846 \| 29300 \| 0.0 \| - \|
	\| 0.6857 \| 29350 \| 0.0 \| - \|
	\| 0.6869 \| 29400 \| 0.0 \| - \|
	\| 0.6881 \| 29450 \| 0.0 \| - \|
	\| 0.6892 \| 29500 \| 0.0 \| - \|
	\| 0.6904 \| 29550 \| 0.0 \| - \|
	\| 0.6916 \| 29600 \| 0.0 \| - \|
	\| 0.6927 \| 29650 \| 0.0 \| - \|
	\| 0.6939 \| 29700 \| 0.0 \| - \|
	\| 0.6951 \| 29750 \| 0.0 \| - \|
	\| 0.6962 \| 29800 \| 0.0 \| - \|
	\| 0.6974 \| 29850 \| 0.0 \| - \|
	\| 0.6986 \| 29900 \| 0.0 \| - \|
	\| 0.6998 \| 29950 \| 0.0 \| - \|
	\| 0.7009 \| 30000 \| 0.0 \| - \|
	\| 0.7021 \| 30050 \| 0.0 \| - \|
	\| 0.7033 \| 30100 \| 0.0 \| - \|
	\| 0.7044 \| 30150 \| 0.0 \| - \|
	\| 0.7056 \| 30200 \| 0.0 \| - \|
	\| 0.7068 \| 30250 \| 0.0 \| - \|
	\| 0.7079 \| 30300 \| 0.0 \| - \|
	\| 0.7091 \| 30350 \| 0.0 \| - \|
	\| 0.7103 \| 30400 \| 0.0 \| - \|
	\| 0.7114 \| 30450 \| 0.0 \| - \|
	\| 0.7126 \| 30500 \| 0.0 \| - \|
	\| 0.7138 \| 30550 \| 0.0 \| - \|
	\| 0.7149 \| 30600 \| 0.0 \| - \|
	\| 0.7161 \| 30650 \| 0.0 \| - \|
	\| 0.7173 \| 30700 \| 0.0 \| - \|
	\| 0.7184 \| 30750 \| 0.0 \| - \|
	\| 0.7196 \| 30800 \| 0.0 \| - \|
	\| 0.7208 \| 30850 \| 0.0001 \| - \|
	\| 0.7219 \| 30900 \| 0.0 \| - \|
	\| 0.7231 \| 30950 \| 0.0 \| - \|
	\| 0.7243 \| 31000 \| 0.0 \| - \|
	\| 0.7255 \| 31050 \| 0.0 \| - \|
	\| 0.7266 \| 31100 \| 0.0 \| - \|
	\| 0.7278 \| 31150 \| 0.0 \| - \|
	\| 0.7290 \| 31200 \| 0.0 \| - \|
	\| 0.7301 \| 31250 \| 0.0 \| - \|
	\| 0.7313 \| 31300 \| 0.0 \| - \|
	\| 0.7325 \| 31350 \| 0.0 \| - \|
	\| 0.7336 \| 31400 \| 0.0 \| - \|
	\| 0.7348 \| 31450 \| 0.0 \| - \|
	\| 0.7360 \| 31500 \| 0.0 \| - \|
	\| 0.7371 \| 31550 \| 0.0 \| - \|
	\| 0.7383 \| 31600 \| 0.0001 \| - \|
	\| 0.7395 \| 31650 \| 0.0001 \| - \|
	\| 0.7406 \| 31700 \| 0.0 \| - \|
	\| 0.7418 \| 31750 \| 0.0 \| - \|
	\| 0.7430 \| 31800 \| 0.0 \| - \|
	\| 0.7441 \| 31850 \| 0.0 \| - \|
	\| 0.7453 \| 31900 \| 0.0 \| - \|
	\| 0.7465 \| 31950 \| 0.0 \| - \|
	\| 0.7476 \| 32000 \| 0.0 \| - \|
	\| 0.7488 \| 32050 \| 0.0 \| - \|
	\| 0.7500 \| 32100 \| 0.0 \| - \|
	\| 0.7512 \| 32150 \| 0.0 \| - \|
	\| 0.7523 \| 32200 \| 0.0 \| - \|
	\| 0.7535 \| 32250 \| 0.0 \| - \|
	\| 0.7547 \| 32300 \| 0.0 \| - \|
	\| 0.7558 \| 32350 \| 0.0 \| - \|
	\| 0.7570 \| 32400 \| 0.0 \| - \|
	\| 0.7582 \| 32450 \| 0.0 \| - \|
	\| 0.7593 \| 32500 \| 0.0 \| - \|
	\| 0.7605 \| 32550 \| 0.0 \| - \|
	\| 0.7617 \| 32600 \| 0.0 \| - \|
	\| 0.7628 \| 32650 \| 0.0 \| - \|
	\| 0.7640 \| 32700 \| 0.0 \| - \|
	\| 0.7652 \| 32750 \| 0.0 \| - \|
	\| 0.7663 \| 32800 \| 0.0 \| - \|
	\| 0.7675 \| 32850 \| 0.0 \| - \|
	\| 0.7687 \| 32900 \| 0.0 \| - \|
	\| 0.7698 \| 32950 \| 0.0 \| - \|
	\| 0.7710 \| 33000 \| 0.0 \| - \|
	\| 0.7722 \| 33050 \| 0.0 \| - \|
	\| 0.7733 \| 33100 \| 0.0 \| - \|
	\| 0.7745 \| 33150 \| 0.0 \| - \|
	\| 0.7757 \| 33200 \| 0.0 \| - \|
	\| 0.7769 \| 33250 \| 0.0 \| - \|
	\| 0.7780 \| 33300 \| 0.0 \| - \|
	\| 0.7792 \| 33350 \| 0.0 \| - \|
	\| 0.7804 \| 33400 \| 0.0 \| - \|
	\| 0.7815 \| 33450 \| 0.0 \| - \|
	\| 0.7827 \| 33500 \| 0.0 \| - \|
	\| 0.7839 \| 33550 \| 0.0 \| - \|
	\| 0.7850 \| 33600 \| 0.0 \| - \|
	\| 0.7862 \| 33650 \| 0.0 \| - \|
	\| 0.7874 \| 33700 \| 0.0001 \| - \|
	\| 0.7885 \| 33750 \| 0.0 \| - \|
	\| 0.7897 \| 33800 \| 0.0 \| - \|
	\| 0.7909 \| 33850 \| 0.0 \| - \|
	\| 0.7920 \| 33900 \| 0.0 \| - \|
	\| 0.7932 \| 33950 \| 0.0 \| - \|
	\| 0.7944 \| 34000 \| 0.0 \| - \|
	\| 0.7955 \| 34050 \| 0.0 \| - \|
	\| 0.7967 \| 34100 \| 0.0 \| - \|
	\| 0.7979 \| 34150 \| 0.0 \| - \|
	\| 0.7990 \| 34200 \| 0.0 \| - \|
	\| 0.8002 \| 34250 \| 0.0 \| - \|
	\| 0.8014 \| 34300 \| 0.0 \| - \|
	\| 0.8026 \| 34350 \| 0.0 \| - \|
	\| 0.8037 \| 34400 \| 0.0 \| - \|
	\| 0.8049 \| 34450 \| 0.0 \| - \|
	\| 0.8061 \| 34500 \| 0.0 \| - \|
	\| 0.8072 \| 34550 \| 0.0 \| - \|
	\| 0.8084 \| 34600 \| 0.0 \| - \|
	\| 0.8096 \| 34650 \| 0.0 \| - \|
	\| 0.8107 \| 34700 \| 0.0 \| - \|
	\| 0.8119 \| 34750 \| 0.0 \| - \|
	\| 0.8131 \| 34800 \| 0.0 \| - \|
	\| 0.8142 \| 34850 \| 0.0 \| - \|
	\| 0.8154 \| 34900 \| 0.0 \| - \|
	\| 0.8166 \| 34950 \| 0.0 \| - \|
	\| 0.8177 \| 35000 \| 0.0 \| - \|
	\| 0.8189 \| 35050 \| 0.0 \| - \|
	\| 0.8201 \| 35100 \| 0.0 \| - \|
	\| 0.8212 \| 35150 \| 0.0 \| - \|
	\| 0.8224 \| 35200 \| 0.0 \| - \|
	\| 0.8236 \| 35250 \| 0.0 \| - \|
	\| 0.8247 \| 35300 \| 0.0 \| - \|
	\| 0.8259 \| 35350 \| 0.0 \| - \|
	\| 0.8271 \| 35400 \| 0.0 \| - \|
	\| 0.8283 \| 35450 \| 0.0 \| - \|
	\| 0.8294 \| 35500 \| 0.0 \| - \|
	\| 0.8306 \| 35550 \| 0.0 \| - \|
	\| 0.8318 \| 35600 \| 0.0 \| - \|
	\| 0.8329 \| 35650 \| 0.0 \| - \|
	\| 0.8341 \| 35700 \| 0.0 \| - \|
	\| 0.8353 \| 35750 \| 0.0 \| - \|
	\| 0.8364 \| 35800 \| 0.0 \| - \|
	\| 0.8376 \| 35850 \| 0.0 \| - \|
	\| 0.8388 \| 35900 \| 0.0 \| - \|
	\| 0.8399 \| 35950 \| 0.0 \| - \|
	\| 0.8411 \| 36000 \| 0.0 \| - \|
	\| 0.8423 \| 36050 \| 0.0 \| - \|
	\| 0.8434 \| 36100 \| 0.0 \| - \|
	\| 0.8446 \| 36150 \| 0.0 \| - \|
	\| 0.8458 \| 36200 \| 0.0 \| - \|
	\| 0.8469 \| 36250 \| 0.0 \| - \|
	\| 0.8481 \| 36300 \| 0.0 \| - \|
	\| 0.8493 \| 36350 \| 0.0 \| - \|
	\| 0.8504 \| 36400 \| 0.0 \| - \|
	\| 0.8516 \| 36450 \| 0.0 \| - \|
	\| 0.8528 \| 36500 \| 0.0 \| - \|
	\| 0.8540 \| 36550 \| 0.0 \| - \|
	\| 0.8551 \| 36600 \| 0.0 \| - \|
	\| 0.8563 \| 36650 \| 0.0 \| - \|
	\| 0.8575 \| 36700 \| 0.0 \| - \|
	\| 0.8586 \| 36750 \| 0.0 \| - \|
	\| 0.8598 \| 36800 \| 0.0 \| - \|
	\| 0.8610 \| 36850 \| 0.0 \| - \|
	\| 0.8621 \| 36900 \| 0.0 \| - \|
	\| 0.8633 \| 36950 \| 0.0 \| - \|
	\| 0.8645 \| 37000 \| 0.0 \| - \|
	\| 0.8656 \| 37050 \| 0.0 \| - \|
	\| 0.8668 \| 37100 \| 0.0 \| - \|
	\| 0.8680 \| 37150 \| 0.0 \| - \|
	\| 0.8691 \| 37200 \| 0.0 \| - \|
	\| 0.8703 \| 37250 \| 0.0 \| - \|
	\| 0.8715 \| 37300 \| 0.0 \| - \|
	\| 0.8726 \| 37350 \| 0.0 \| - \|
	\| 0.8738 \| 37400 \| 0.0 \| - \|
	\| 0.8750 \| 37450 \| 0.0 \| - \|
	\| 0.8761 \| 37500 \| 0.0 \| - \|
	\| 0.8773 \| 37550 \| 0.0 \| - \|
	\| 0.8785 \| 37600 \| 0.0 \| - \|
	\| 0.8797 \| 37650 \| 0.0 \| - \|
	\| 0.8808 \| 37700 \| 0.0 \| - \|
	\| 0.8820 \| 37750 \| 0.0 \| - \|
	\| 0.8832 \| 37800 \| 0.0 \| - \|
	\| 0.8843 \| 37850 \| 0.0 \| - \|
	\| 0.8855 \| 37900 \| 0.0 \| - \|
	\| 0.8867 \| 37950 \| 0.0 \| - \|
	\| 0.8878 \| 38000 \| 0.0 \| - \|
	\| 0.8890 \| 38050 \| 0.0 \| - \|
	\| 0.8902 \| 38100 \| 0.0 \| - \|
	\| 0.8913 \| 38150 \| 0.0 \| - \|
	\| 0.8925 \| 38200 \| 0.0 \| - \|
	\| 0.8937 \| 38250 \| 0.0 \| - \|
	\| 0.8948 \| 38300 \| 0.0 \| - \|
	\| 0.8960 \| 38350 \| 0.0 \| - \|
	\| 0.8972 \| 38400 \| 0.0 \| - \|
	\| 0.8983 \| 38450 \| 0.0 \| - \|
	\| 0.8995 \| 38500 \| 0.0 \| - \|
	\| 0.9007 \| 38550 \| 0.0 \| - \|
	\| 0.9018 \| 38600 \| 0.0 \| - \|
	\| 0.9030 \| 38650 \| 0.0 \| - \|
	\| 0.9042 \| 38700 \| 0.0 \| - \|
	\| 0.9054 \| 38750 \| 0.0 \| - \|
	\| 0.9065 \| 38800 \| 0.0 \| - \|
	\| 0.9077 \| 38850 \| 0.0 \| - \|
	\| 0.9089 \| 38900 \| 0.0 \| - \|
	\| 0.9100 \| 38950 \| 0.0 \| - \|
	\| 0.9112 \| 39000 \| 0.0 \| - \|
	\| 0.9124 \| 39050 \| 0.0 \| - \|
	\| 0.9135 \| 39100 \| 0.0 \| - \|
	\| 0.9147 \| 39150 \| 0.0 \| - \|
	\| 0.9159 \| 39200 \| 0.0 \| - \|
	\| 0.9170 \| 39250 \| 0.0 \| - \|
	\| 0.9182 \| 39300 \| 0.0 \| - \|
	\| 0.9194 \| 39350 \| 0.0 \| - \|
	\| 0.9205 \| 39400 \| 0.0 \| - \|
	\| 0.9217 \| 39450 \| 0.0 \| - \|
	\| 0.9229 \| 39500 \| 0.0 \| - \|
	\| 0.9240 \| 39550 \| 0.0 \| - \|
	\| 0.9252 \| 39600 \| 0.0 \| - \|
	\| 0.9264 \| 39650 \| 0.0 \| - \|
	\| 0.9275 \| 39700 \| 0.0 \| - \|
	\| 0.9287 \| 39750 \| 0.0 \| - \|
	\| 0.9299 \| 39800 \| 0.0 \| - \|
	\| 0.9311 \| 39850 \| 0.0 \| - \|
	\| 0.9322 \| 39900 \| 0.0 \| - \|
	\| 0.9334 \| 39950 \| 0.0 \| - \|
	\| 0.9346 \| 40000 \| 0.0 \| - \|
	\| 0.9357 \| 40050 \| 0.0 \| - \|
	\| 0.9369 \| 40100 \| 0.0 \| - \|
	\| 0.9381 \| 40150 \| 0.0 \| - \|
	\| 0.9392 \| 40200 \| 0.0 \| - \|
	\| 0.9404 \| 40250 \| 0.0 \| - \|
	\| 0.9416 \| 40300 \| 0.0001 \| - \|
	\| 0.9427 \| 40350 \| 0.0 \| - \|
	\| 0.9439 \| 40400 \| 0.0 \| - \|
	\| 0.9451 \| 40450 \| 0.0 \| - \|
	\| 0.9462 \| 40500 \| 0.0 \| - \|
	\| 0.9474 \| 40550 \| 0.0 \| - \|
	\| 0.9486 \| 40600 \| 0.0 \| - \|
	\| 0.9497 \| 40650 \| 0.0 \| - \|
	\| 0.9509 \| 40700 \| 0.0 \| - \|
	\| 0.9521 \| 40750 \| 0.0 \| - \|
	\| 0.9532 \| 40800 \| 0.0 \| - \|
	\| 0.9544 \| 40850 \| 0.0 \| - \|
	\| 0.9556 \| 40900 \| 0.0 \| - \|
	\| 0.9568 \| 40950 \| 0.0 \| - \|
	\| 0.9579 \| 41000 \| 0.0 \| - \|
	\| 0.9591 \| 41050 \| 0.0 \| - \|
	\| 0.9603 \| 41100 \| 0.0 \| - \|
	\| 0.9614 \| 41150 \| 0.0 \| - \|
	\| 0.9626 \| 41200 \| 0.0 \| - \|
	\| 0.9638 \| 41250 \| 0.0 \| - \|
	\| 0.9649 \| 41300 \| 0.0 \| - \|
	\| 0.9661 \| 41350 \| 0.0 \| - \|
	\| 0.9673 \| 41400 \| 0.0 \| - \|
	\| 0.9684 \| 41450 \| 0.0 \| - \|
	\| 0.9696 \| 41500 \| 0.0 \| - \|
	\| 0.9708 \| 41550 \| 0.0 \| - \|
	\| 0.9719 \| 41600 \| 0.0 \| - \|
	\| 0.9731 \| 41650 \| 0.0 \| - \|
	\| 0.9743 \| 41700 \| 0.0 \| - \|
	\| 0.9754 \| 41750 \| 0.0 \| - \|
	\| 0.9766 \| 41800 \| 0.0 \| - \|
	\| 0.9778 \| 41850 \| 0.0 \| - \|
	\| 0.9789 \| 41900 \| 0.0 \| - \|
	\| 0.9801 \| 41950 \| 0.0 \| - \|
	\| 0.9813 \| 42000 \| 0.0 \| - \|
	\| 0.9825 \| 42050 \| 0.0 \| - \|
	\| 0.9836 \| 42100 \| 0.0 \| - \|
	\| 0.9848 \| 42150 \| 0.0 \| - \|
	\| 0.9860 \| 42200 \| 0.0 \| - \|
	\| 0.9871 \| 42250 \| 0.0 \| - \|
	\| 0.9883 \| 42300 \| 0.0 \| - \|
	\| 0.9895 \| 42350 \| 0.0 \| - \|
	\| 0.9906 \| 42400 \| 0.0 \| - \|
	\| 0.9918 \| 42450 \| 0.0 \| - \|
	\| 0.9930 \| 42500 \| 0.0 \| - \|
	\| 0.9941 \| 42550 \| 0.0 \| - \|
	\| 0.9953 \| 42600 \| 0.0 \| - \|
	\| 0.9965 \| 42650 \| 0.0 \| - \|
	\| 0.9976 \| 42700 \| 0.0 \| - \|
	\| 0.9988 \| 42750 \| 0.0 \| - \|
	\| 1.0000 \| 42800 \| 0.0 \| - \|

	### Framework Versions
	- Python: 3.10.12
	- SetFit: 1.0.3
	- Sentence Transformers: 2.5.1
	- Transformers: 4.38.1
	- PyTorch: 2.1.0+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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