Add SetFit model

53aea9d verified 3 months ago

50.9 kB

	---
	base_model: BAAI/bge-small-en-v1.5
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: What tables are included in the starhub_data_asset database that relate to
	customer complaints?
	- text: What are the tables that I can access in the starhub_data_asset database?
	- text: Can I have avg Cost_Efficiency
	- text: Analyze product category revenue impact.
	- text: Retrieve data_asset_kpi_ma_product details.
	inference: true
	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: 0.9914529914529915
	name: Accuracy
	---

	# 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: 7 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 \|
	\|:-------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| Generalreply \| <ul><li>'Can you recommend a good movie to watch?'</li><li>"Oh, that's a tough one! There are so many good memories to choose from. But if I had to pick just one, I think it would be spending summers at my grandparent's house. We would play board games, make homemade ice cream, and have big family dinners. It was always so much fun!"</li><li>'Oh, I love reading books! My favorite genre is definitely fantasy. How about you? What kind of books do you like to read?'</li></ul> \|
	\| Lookup_1 \| <ul><li>'Get me data_asset_kpi_cf cash flow.'</li><li>'Display data_asset_001_pcc for electronics category.'</li><li>'Calculate Gross Profit Margin Trends.'</li></ul> \|
	\| Lookup \| <ul><li>"What are the products in the 'Clothing' category?"</li><li>"Get me the phone numbers of customers with the last name 'Johnson'."</li><li>"Can you filter by employees who have the last name 'Brown'?"</li></ul> \|
	\| Aggregation \| <ul><li>'Get me max Accumulated Amortisation and Impairment.'</li><li>'Can I have mode of Revenue'</li><li>'Show me count company_name'</li></ul> \|
	\| Tablejoin \| <ul><li>'Could you merge the Orders and Employees tables to identify which employees have processed high-value orders?'</li><li>'Could you connect the Products and Orders tables to analyze sales data by product category?'</li><li>'How can I connect the Customers and Orders tables to find customers who made purchases during a specific promotion?'</li></ul> \|
	\| Viewtables \| <ul><li>'What are the tables in the starhub_data_asset database that a user can join to perform a sales analysis?'</li><li>'What tables can be found in the asset-tracking section of the starhub_data_asset database?'</li><li>'What tables exist in the starhub_data_asset database?'</li></ul> \|
	\| Rejection \| <ul><li>"Let's avoid creating any new data sets."</li><li>"I'd prefer to avoid generating data fields."</li><li>"I'm not interested in filtering this collection."</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9915 \|

	## 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("nazhan/bge-small-en-v1.5-brahmaputra-iter-10")
	# Run inference
	preds = model("Can I have avg Cost_Efficiency")
	```

	<!--
	### 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 \| 8.6563 \| 62 \|

	\| Label \| Training Sample Count \|
	\|:-------------\|:----------------------\|
	\| Tablejoin \| 129 \|
	\| Rejection \| 77 \|
	\| Aggregation \| 282 \|
	\| Lookup \| 60 \|
	\| Generalreply \| 63 \|
	\| Viewtables \| 74 \|
	\| Lookup_1 \| 150 \|

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

	### Training Results
	\| Epoch \| Step \| Training Loss \| Validation Loss \|
	\|:-------:\|:---------:\|:-------------:\|:---------------:\|
	\| 0.0000 \| 1 \| 0.2038 \| - \|
	\| 0.0014 \| 50 \| 0.2019 \| - \|
	\| 0.0029 \| 100 \| 0.1983 \| - \|
	\| 0.0043 \| 150 \| 0.206 \| - \|
	\| 0.0057 \| 200 \| 0.2268 \| - \|
	\| 0.0071 \| 250 \| 0.2025 \| - \|
	\| 0.0086 \| 300 \| 0.2041 \| - \|
	\| 0.0100 \| 350 \| 0.1426 \| - \|
	\| 0.0114 \| 400 \| 0.1513 \| - \|
	\| 0.0129 \| 450 \| 0.1215 \| - \|
	\| 0.0143 \| 500 \| 0.1426 \| - \|
	\| 0.0157 \| 550 \| 0.0859 \| - \|
	\| 0.0172 \| 600 \| 0.0486 \| - \|
	\| 0.0186 \| 650 \| 0.0378 \| - \|
	\| 0.0200 \| 700 \| 0.0519 \| - \|
	\| 0.0214 \| 750 \| 0.0717 \| - \|
	\| 0.0229 \| 800 \| 0.1161 \| - \|
	\| 0.0243 \| 850 \| 0.0771 \| - \|
	\| 0.0257 \| 900 \| 0.074 \| - \|
	\| 0.0272 \| 950 \| 0.0567 \| - \|
	\| 0.0286 \| 1000 \| 0.0223 \| - \|
	\| 0.0300 \| 1050 \| 0.0266 \| - \|
	\| 0.0315 \| 1100 \| 0.0261 \| - \|
	\| 0.0329 \| 1150 \| 0.0333 \| - \|
	\| 0.0343 \| 1200 \| 0.0107 \| - \|
	\| 0.0357 \| 1250 \| 0.0123 \| - \|
	\| 0.0372 \| 1300 \| 0.0193 \| - \|
	\| 0.0386 \| 1350 \| 0.0039 \| - \|
	\| 0.0400 \| 1400 \| 0.0079 \| - \|
	\| 0.0415 \| 1450 \| 0.0035 \| - \|
	\| 0.0429 \| 1500 \| 0.003 \| - \|
	\| 0.0443 \| 1550 \| 0.0041 \| - \|
	\| 0.0457 \| 1600 \| 0.0038 \| - \|
	\| 0.0472 \| 1650 \| 0.002 \| - \|
	\| 0.0486 \| 1700 \| 0.0028 \| - \|
	\| 0.0500 \| 1750 \| 0.0056 \| - \|
	\| 0.0515 \| 1800 \| 0.0035 \| - \|
	\| 0.0529 \| 1850 \| 0.0027 \| - \|
	\| 0.0543 \| 1900 \| 0.0028 \| - \|
	\| 0.0558 \| 1950 \| 0.0028 \| - \|
	\| 0.0572 \| 2000 \| 0.0019 \| - \|
	\| 0.0586 \| 2050 \| 0.0046 \| - \|
	\| 0.0600 \| 2100 \| 0.0017 \| - \|
	\| 0.0615 \| 2150 \| 0.0016 \| - \|
	\| 0.0629 \| 2200 \| 0.0022 \| - \|
	\| 0.0643 \| 2250 \| 0.002 \| - \|
	\| 0.0658 \| 2300 \| 0.0029 \| - \|
	\| 0.0672 \| 2350 \| 0.0032 \| - \|
	\| 0.0686 \| 2400 \| 0.0018 \| - \|
	\| 0.0701 \| 2450 \| 0.0015 \| - \|
	\| 0.0715 \| 2500 \| 0.0015 \| - \|
	\| 0.0729 \| 2550 \| 0.0016 \| - \|
	\| 0.0743 \| 2600 \| 0.0012 \| - \|
	\| 0.0758 \| 2650 \| 0.0014 \| - \|
	\| 0.0772 \| 2700 \| 0.0015 \| - \|
	\| 0.0786 \| 2750 \| 0.0018 \| - \|
	\| 0.0801 \| 2800 \| 0.0012 \| - \|
	\| 0.0815 \| 2850 \| 0.0009 \| - \|
	\| 0.0829 \| 2900 \| 0.001 \| - \|
	\| 0.0843 \| 2950 \| 0.0011 \| - \|
	\| 0.0858 \| 3000 \| 0.0011 \| - \|
	\| 0.0872 \| 3050 \| 0.001 \| - \|
	\| 0.0886 \| 3100 \| 0.0012 \| - \|
	\| 0.0901 \| 3150 \| 0.0006 \| - \|
	\| 0.0915 \| 3200 \| 0.0013 \| - \|
	\| 0.0929 \| 3250 \| 0.0007 \| - \|
	\| 0.0944 \| 3300 \| 0.0007 \| - \|
	\| 0.0958 \| 3350 \| 0.0009 \| - \|
	\| 0.0972 \| 3400 \| 0.0008 \| - \|
	\| 0.0986 \| 3450 \| 0.0005 \| - \|
	\| 0.1001 \| 3500 \| 0.001 \| - \|
	\| 0.1015 \| 3550 \| 0.001 \| - \|
	\| 0.1029 \| 3600 \| 0.0008 \| - \|
	\| 0.1044 \| 3650 \| 0.0007 \| - \|
	\| 0.1058 \| 3700 \| 0.0006 \| - \|
	\| 0.1072 \| 3750 \| 0.0009 \| - \|
	\| 0.1086 \| 3800 \| 0.0012 \| - \|
	\| 0.1101 \| 3850 \| 0.0007 \| - \|
	\| 0.1115 \| 3900 \| 0.0008 \| - \|
	\| 0.1129 \| 3950 \| 0.0009 \| - \|
	\| 0.1144 \| 4000 \| 0.0007 \| - \|
	\| 0.1158 \| 4050 \| 0.0007 \| - \|
	\| 0.1172 \| 4100 \| 0.0007 \| - \|
	\| 0.1187 \| 4150 \| 0.0006 \| - \|
	\| 0.1201 \| 4200 \| 0.0006 \| - \|
	\| 0.1215 \| 4250 \| 0.0011 \| - \|
	\| 0.1229 \| 4300 \| 0.0012 \| - \|
	\| 0.1244 \| 4350 \| 0.0007 \| - \|
	\| 0.1258 \| 4400 \| 0.0007 \| - \|
	\| 0.1272 \| 4450 \| 0.0006 \| - \|
	\| 0.1287 \| 4500 \| 0.0005 \| - \|
	\| 0.1301 \| 4550 \| 0.0008 \| - \|
	\| 0.1315 \| 4600 \| 0.0006 \| - \|
	\| 0.1330 \| 4650 \| 0.0007 \| - \|
	\| 0.1344 \| 4700 \| 0.0006 \| - \|
	\| 0.1358 \| 4750 \| 0.0005 \| - \|
	\| 0.1372 \| 4800 \| 0.0006 \| - \|
	\| 0.1387 \| 4850 \| 0.0008 \| - \|
	\| 0.1401 \| 4900 \| 0.0008 \| - \|
	\| 0.1415 \| 4950 \| 0.0004 \| - \|
	\| 0.1430 \| 5000 \| 0.0005 \| - \|
	\| 0.1444 \| 5050 \| 0.0005 \| - \|
	\| 0.1458 \| 5100 \| 0.0007 \| - \|
	\| 0.1472 \| 5150 \| 0.0006 \| - \|
	\| 0.1487 \| 5200 \| 0.0006 \| - \|
	\| 0.1501 \| 5250 \| 0.0004 \| - \|
	\| 0.1515 \| 5300 \| 0.0005 \| - \|
	\| 0.1530 \| 5350 \| 0.0007 \| - \|
	\| 0.1544 \| 5400 \| 0.0007 \| - \|
	\| 0.1558 \| 5450 \| 0.0005 \| - \|
	\| 0.1573 \| 5500 \| 0.0007 \| - \|
	\| 0.1587 \| 5550 \| 0.0004 \| - \|
	\| 0.1601 \| 5600 \| 0.0004 \| - \|
	\| 0.1615 \| 5650 \| 0.0006 \| - \|
	\| 0.1630 \| 5700 \| 0.0005 \| - \|
	\| 0.1644 \| 5750 \| 0.0006 \| - \|
	\| 0.1658 \| 5800 \| 0.0004 \| - \|
	\| 0.1673 \| 5850 \| 0.0005 \| - \|
	\| 0.1687 \| 5900 \| 0.0007 \| - \|
	\| 0.1701 \| 5950 \| 0.0005 \| - \|
	\| 0.1716 \| 6000 \| 0.0005 \| - \|
	\| 0.1730 \| 6050 \| 0.0003 \| - \|
	\| 0.1744 \| 6100 \| 0.0003 \| - \|
	\| 0.1758 \| 6150 \| 0.0005 \| - \|
	\| 0.1773 \| 6200 \| 0.0007 \| - \|
	\| 0.1787 \| 6250 \| 0.0004 \| - \|
	\| 0.1801 \| 6300 \| 0.0006 \| - \|
	\| 0.1816 \| 6350 \| 0.0004 \| - \|
	\| 0.1830 \| 6400 \| 0.0003 \| - \|
	\| 0.1844 \| 6450 \| 0.0005 \| - \|
	\| 0.1858 \| 6500 \| 0.0004 \| - \|
	\| 0.1873 \| 6550 \| 0.0006 \| - \|
	\| 0.1887 \| 6600 \| 0.0005 \| - \|
	\| 0.1901 \| 6650 \| 0.0005 \| - \|
	\| 0.1916 \| 6700 \| 0.0003 \| - \|
	\| 0.1930 \| 6750 \| 0.0004 \| - \|
	\| 0.1944 \| 6800 \| 0.0004 \| - \|
	\| 0.1959 \| 6850 \| 0.0004 \| - \|
	\| 0.1973 \| 6900 \| 0.0003 \| - \|
	\| 0.1987 \| 6950 \| 0.0004 \| - \|
	\| 0.2001 \| 7000 \| 0.0004 \| - \|
	\| 0.2016 \| 7050 \| 0.0003 \| - \|
	\| 0.2030 \| 7100 \| 0.0003 \| - \|
	\| 0.2044 \| 7150 \| 0.0005 \| - \|
	\| 0.2059 \| 7200 \| 0.0004 \| - \|
	\| 0.2073 \| 7250 \| 0.0003 \| - \|
	\| 0.2087 \| 7300 \| 0.0002 \| - \|
	\| 0.2102 \| 7350 \| 0.0003 \| - \|
	\| 0.2116 \| 7400 \| 0.0004 \| - \|
	\| 0.2130 \| 7450 \| 0.0006 \| - \|
	\| 0.2144 \| 7500 \| 0.0003 \| - \|
	\| 0.2159 \| 7550 \| 0.0002 \| - \|
	\| 0.2173 \| 7600 \| 0.0004 \| - \|
	\| 0.2187 \| 7650 \| 0.0003 \| - \|
	\| 0.2202 \| 7700 \| 0.0005 \| - \|
	\| 0.2216 \| 7750 \| 0.0004 \| - \|
	\| 0.2230 \| 7800 \| 0.0004 \| - \|
	\| 0.2244 \| 7850 \| 0.0004 \| - \|
	\| 0.2259 \| 7900 \| 0.0003 \| - \|
	\| 0.2273 \| 7950 \| 0.0005 \| - \|
	\| 0.2287 \| 8000 \| 0.0003 \| - \|
	\| 0.2302 \| 8050 \| 0.0003 \| - \|
	\| 0.2316 \| 8100 \| 0.0003 \| - \|
	\| 0.2330 \| 8150 \| 0.0002 \| - \|
	\| 0.2345 \| 8200 \| 0.0002 \| - \|
	\| 0.2359 \| 8250 \| 0.0004 \| - \|
	\| 0.2373 \| 8300 \| 0.0004 \| - \|
	\| 0.2387 \| 8350 \| 0.0004 \| - \|
	\| 0.2402 \| 8400 \| 0.0003 \| - \|
	\| 0.2416 \| 8450 \| 0.0002 \| - \|
	\| 0.2430 \| 8500 \| 0.0002 \| - \|
	\| 0.2445 \| 8550 \| 0.0003 \| - \|
	\| 0.2459 \| 8600 \| 0.0004 \| - \|
	\| 0.2473 \| 8650 \| 0.0004 \| - \|
	\| 0.2487 \| 8700 \| 0.0003 \| - \|
	\| 0.2502 \| 8750 \| 0.0002 \| - \|
	\| 0.2516 \| 8800 \| 0.0003 \| - \|
	\| 0.2530 \| 8850 \| 0.0003 \| - \|
	\| 0.2545 \| 8900 \| 0.0004 \| - \|
	\| 0.2559 \| 8950 \| 0.0003 \| - \|
	\| 0.2573 \| 9000 \| 0.0002 \| - \|
	\| 0.2588 \| 9050 \| 0.0003 \| - \|
	\| 0.2602 \| 9100 \| 0.0003 \| - \|
	\| 0.2616 \| 9150 \| 0.0003 \| - \|
	\| 0.2630 \| 9200 \| 0.0003 \| - \|
	\| 0.2645 \| 9250 \| 0.0002 \| - \|
	\| 0.2659 \| 9300 \| 0.0002 \| - \|
	\| 0.2673 \| 9350 \| 0.0003 \| - \|
	\| 0.2688 \| 9400 \| 0.0552 \| - \|
	\| 0.2702 \| 9450 \| 0.0003 \| - \|
	\| 0.2716 \| 9500 \| 0.0003 \| - \|
	\| 0.2731 \| 9550 \| 0.0004 \| - \|
	\| 0.2745 \| 9600 \| 0.0004 \| - \|
	\| 0.2759 \| 9650 \| 0.0005 \| - \|
	\| 0.2773 \| 9700 \| 0.0003 \| - \|
	\| 0.2788 \| 9750 \| 0.0003 \| - \|
	\| 0.2802 \| 9800 \| 0.0003 \| - \|
	\| 0.2816 \| 9850 \| 0.0003 \| - \|
	\| 0.2831 \| 9900 \| 0.0004 \| - \|
	\| 0.2845 \| 9950 \| 0.0003 \| - \|
	\| 0.2859 \| 10000 \| 0.0003 \| - \|
	\| 0.2873 \| 10050 \| 0.0004 \| - \|
	\| 0.2888 \| 10100 \| 0.0005 \| - \|
	\| 0.2902 \| 10150 \| 0.0003 \| - \|
	\| 0.2916 \| 10200 \| 0.0004 \| - \|
	\| 0.2931 \| 10250 \| 0.0002 \| - \|
	\| 0.2945 \| 10300 \| 0.0005 \| - \|
	\| 0.2959 \| 10350 \| 0.0003 \| - \|
	\| 0.2974 \| 10400 \| 0.0003 \| - \|
	\| 0.2988 \| 10450 \| 0.0002 \| - \|
	\| 0.3002 \| 10500 \| 0.0003 \| - \|
	\| 0.3016 \| 10550 \| 0.0004 \| - \|
	\| 0.3031 \| 10600 \| 0.0003 \| - \|
	\| 0.3045 \| 10650 \| 0.0003 \| - \|
	\| 0.3059 \| 10700 \| 0.0004 \| - \|
	\| 0.3074 \| 10750 \| 0.0003 \| - \|
	\| 0.3088 \| 10800 \| 0.0003 \| - \|
	\| 0.3102 \| 10850 \| 0.0003 \| - \|
	\| 0.3117 \| 10900 \| 0.0002 \| - \|
	\| 0.3131 \| 10950 \| 0.0005 \| - \|
	\| 0.3145 \| 11000 \| 0.0003 \| - \|
	\| 0.3159 \| 11050 \| 0.0002 \| - \|
	\| 0.3174 \| 11100 \| 0.0003 \| - \|
	\| 0.3188 \| 11150 \| 0.0004 \| - \|
	\| 0.3202 \| 11200 \| 0.0004 \| - \|
	\| 0.3217 \| 11250 \| 0.0002 \| - \|
	\| 0.3231 \| 11300 \| 0.0003 \| - \|
	\| 0.3245 \| 11350 \| 0.0003 \| - \|
	\| 0.3259 \| 11400 \| 0.0003 \| - \|
	\| 0.3274 \| 11450 \| 0.0004 \| - \|
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	\| 0.3302 \| 11550 \| 0.0003 \| - \|
	\| 0.3317 \| 11600 \| 0.0003 \| - \|
	\| 0.3331 \| 11650 \| 0.0002 \| - \|
	\| 0.3345 \| 11700 \| 0.0004 \| - \|
	\| 0.3360 \| 11750 \| 0.0002 \| - \|
	\| 0.3374 \| 11800 \| 0.0003 \| - \|
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	\| 0.3402 \| 11900 \| 0.0003 \| - \|
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	\| 0.3431 \| 12000 \| 0.0004 \| - \|
	\| 0.3445 \| 12050 \| 0.0003 \| - \|
	\| 0.3460 \| 12100 \| 0.0004 \| - \|
	\| 0.3474 \| 12150 \| 0.0005 \| - \|
	\| 0.3488 \| 12200 \| 0.0004 \| - \|
	\| 0.3503 \| 12250 \| 0.0004 \| - \|
	\| 0.3517 \| 12300 \| 0.0002 \| - \|
	\| 0.3531 \| 12350 \| 0.0002 \| - \|
	\| 0.3545 \| 12400 \| 0.0004 \| - \|
	\| 0.3560 \| 12450 \| 0.0002 \| - \|
	\| 0.3574 \| 12500 \| 0.0002 \| - \|
	\| 0.3588 \| 12550 \| 0.0003 \| - \|
	\| 0.3603 \| 12600 \| 0.0005 \| - \|
	\| 0.3617 \| 12650 \| 0.0003 \| - \|
	\| 0.3631 \| 12700 \| 0.0003 \| - \|
	\| 0.3645 \| 12750 \| 0.0002 \| - \|
	\| 0.3660 \| 12800 \| 0.0003 \| - \|
	\| 0.3674 \| 12850 \| 0.0002 \| - \|
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	\| 0.3717 \| 13000 \| 0.0002 \| - \|
	\| 0.3731 \| 13050 \| 0.0003 \| - \|
	\| 0.3746 \| 13100 \| 0.0003 \| - \|
	\| 0.3760 \| 13150 \| 0.0002 \| - \|
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	\| 0.3788 \| 13250 \| 0.0003 \| - \|
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	\| 0.4003 \| 14000 \| 0.0003 \| - \|
	\| 0.4017 \| 14050 \| 0.0001 \| - \|
	\| 0.4031 \| 14100 \| 0.0002 \| - \|
	\| 0.4046 \| 14150 \| 0.0001 \| - \|
	\| 0.4060 \| 14200 \| 0.0002 \| - \|
	\| 0.4074 \| 14250 \| 0.0002 \| - \|
	\| 0.4089 \| 14300 \| 0.0002 \| - \|
	\| 0.4103 \| 14350 \| 0.0003 \| - \|
	\| 0.4117 \| 14400 \| 0.0003 \| - \|
	\| 0.4132 \| 14450 \| 0.0002 \| - \|
	\| 0.4146 \| 14500 \| 0.0003 \| - \|
	\| 0.4160 \| 14550 \| 0.0003 \| - \|
	\| 0.4174 \| 14600 \| 0.0002 \| - \|
	\| 0.4189 \| 14650 \| 0.0002 \| - \|
	\| 0.4203 \| 14700 \| 0.0003 \| - \|
	\| 0.4217 \| 14750 \| 0.0003 \| - \|
	\| 0.4232 \| 14800 \| 0.0002 \| - \|
	\| 0.4246 \| 14850 \| 0.0003 \| - \|
	\| 0.4260 \| 14900 \| 0.0003 \| - \|
	\| 0.4274 \| 14950 \| 0.0003 \| - \|
	\| 0.4289 \| 15000 \| 0.0002 \| - \|
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	\| 0.4317 \| 15100 \| 0.0002 \| - \|
	\| 0.4332 \| 15150 \| 0.0004 \| - \|
	\| 0.4346 \| 15200 \| 0.0003 \| - \|
	\| 0.4360 \| 15250 \| 0.0001 \| - \|
	\| 0.4375 \| 15300 \| 0.0002 \| - \|
	\| 0.4389 \| 15350 \| 0.0001 \| - \|
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	\| 0.4489 \| 15700 \| 0.0003 \| - \|
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	\| 0.4618 \| 16150 \| 0.0002 \| - \|
	\| 0.4632 \| 16200 \| 0.0003 \| - \|
	\| 0.4646 \| 16250 \| 0.0002 \| - \|
	\| 0.4660 \| 16300 \| 0.0002 \| - \|
	\| 0.4675 \| 16350 \| 0.0002 \| - \|
	\| 0.4689 \| 16400 \| 0.0002 \| - \|
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	\| 0.4718 \| 16500 \| 0.0002 \| - \|
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	\| 0.4746 \| 16600 \| 0.0003 \| - \|
	\| 0.4761 \| 16650 \| 0.0002 \| - \|
	\| 0.4775 \| 16700 \| 0.0002 \| - \|
	\| 0.4789 \| 16750 \| 0.0002 \| - \|
	\| 0.4803 \| 16800 \| 0.0002 \| - \|
	\| 0.4818 \| 16850 \| 0.0001 \| - \|
	\| 0.4832 \| 16900 \| 0.0003 \| - \|
	\| 0.4846 \| 16950 \| 0.0002 \| - \|
	\| 0.4861 \| 17000 \| 0.0002 \| - \|
	\| 0.4875 \| 17050 \| 0.0002 \| - \|
	\| 0.4889 \| 17100 \| 0.0002 \| - \|
	\| 0.4904 \| 17150 \| 0.0002 \| - \|
	\| 0.4918 \| 17200 \| 0.0002 \| - \|
	\| 0.4932 \| 17250 \| 0.0002 \| - \|
	\| 0.4946 \| 17300 \| 0.0002 \| - \|
	\| 0.4961 \| 17350 \| 0.0002 \| - \|
	\| 0.4975 \| 17400 \| 0.0002 \| - \|
	\| 0.4989 \| 17450 \| 0.0001 \| - \|
	\| 0.5004 \| 17500 \| 0.0001 \| - \|
	\| 0.5018 \| 17550 \| 0.0002 \| - \|
	\| 0.5032 \| 17600 \| 0.0002 \| - \|
	\| 0.5046 \| 17650 \| 0.0002 \| - \|
	\| 0.5061 \| 17700 \| 0.0002 \| - \|
	\| 0.5075 \| 17750 \| 0.0002 \| - \|
	\| 0.5089 \| 17800 \| 0.0002 \| - \|
	\| 0.5104 \| 17850 \| 0.0002 \| - \|
	\| 0.5118 \| 17900 \| 0.0002 \| - \|
	\| 0.5132 \| 17950 \| 0.0003 \| - \|
	\| 0.5147 \| 18000 \| 0.0002 \| - \|
	\| 0.5161 \| 18050 \| 0.0002 \| - \|
	\| 0.5175 \| 18100 \| 0.0002 \| - \|
	\| 0.5189 \| 18150 \| 0.0002 \| - \|
	\| 0.5204 \| 18200 \| 0.0002 \| - \|
	\| 0.5218 \| 18250 \| 0.0002 \| - \|
	\| 0.5232 \| 18300 \| 0.0002 \| - \|
	\| 0.5247 \| 18350 \| 0.0002 \| - \|
	\| 0.5261 \| 18400 \| 0.0002 \| - \|
	\| 0.5275 \| 18450 \| 0.0003 \| - \|
	\| 0.5289 \| 18500 \| 0.0001 \| - \|
	\| 0.5304 \| 18550 \| 0.0002 \| - \|
	\| 0.5318 \| 18600 \| 0.0001 \| - \|
	\| 0.5332 \| 18650 \| 0.0002 \| - \|
	\| 0.5347 \| 18700 \| 0.0002 \| - \|
	\| 0.5361 \| 18750 \| 0.0002 \| - \|
	\| 0.5375 \| 18800 \| 0.0002 \| - \|
	\| 0.5390 \| 18850 \| 0.0001 \| - \|
	\| 0.5404 \| 18900 \| 0.0001 \| - \|
	\| 0.5418 \| 18950 \| 0.0001 \| - \|
	\| 0.5432 \| 19000 \| 0.0002 \| - \|
	\| 0.5447 \| 19050 \| 0.0002 \| - \|
	\| 0.5461 \| 19100 \| 0.0002 \| - \|
	\| 0.5475 \| 19150 \| 0.0002 \| - \|
	\| 0.5490 \| 19200 \| 0.0002 \| - \|
	\| 0.5504 \| 19250 \| 0.0002 \| - \|
	\| 0.5518 \| 19300 \| 0.0001 \| - \|
	\| 0.5533 \| 19350 \| 0.0002 \| - \|
	\| 0.5547 \| 19400 \| 0.0002 \| - \|
	\| 0.5561 \| 19450 \| 0.0004 \| - \|
	\| 0.5575 \| 19500 \| 0.0002 \| - \|
	\| 0.5590 \| 19550 \| 0.0002 \| - \|
	\| 0.5604 \| 19600 \| 0.0003 \| - \|
	\| 0.5618 \| 19650 \| 0.0003 \| - \|
	\| 0.5633 \| 19700 \| 0.0002 \| - \|
	\| 0.5647 \| 19750 \| 0.0002 \| - \|
	\| 0.5661 \| 19800 \| 0.0001 \| - \|
	\| 0.5675 \| 19850 \| 0.0003 \| - \|
	\| 0.5690 \| 19900 \| 0.0002 \| - \|
	\| 0.5704 \| 19950 \| 0.0002 \| - \|
	\| 0.5718 \| 20000 \| 0.0001 \| - \|
	\| 0.5733 \| 20050 \| 0.0003 \| - \|
	\| 0.5747 \| 20100 \| 0.0001 \| - \|
	\| 0.5761 \| 20150 \| 0.0002 \| - \|
	\| 0.5776 \| 20200 \| 0.0003 \| - \|
	\| 0.5790 \| 20250 \| 0.0003 \| - \|
	\| 0.5804 \| 20300 \| 0.0002 \| - \|
	\| 0.5818 \| 20350 \| 0.0003 \| - \|
	\| 0.5833 \| 20400 \| 0.0002 \| - \|
	\| 0.5847 \| 20450 \| 0.0002 \| - \|
	\| 0.5861 \| 20500 \| 0.0002 \| - \|
	\| 0.5876 \| 20550 \| 0.0001 \| - \|
	\| 0.5890 \| 20600 \| 0.0002 \| - \|
	\| 0.5904 \| 20650 \| 0.0002 \| - \|
	\| 0.5919 \| 20700 \| 0.0002 \| - \|
	\| 0.5933 \| 20750 \| 0.0002 \| - \|
	\| 0.5947 \| 20800 \| 0.0001 \| - \|
	\| 0.5961 \| 20850 \| 0.0001 \| - \|
	\| 0.5976 \| 20900 \| 0.0001 \| - \|
	\| 0.5990 \| 20950 \| 0.0001 \| - \|
	\| 0.6004 \| 21000 \| 0.0002 \| - \|
	\| 0.6019 \| 21050 \| 0.0001 \| - \|
	\| 0.6033 \| 21100 \| 0.0002 \| - \|
	\| 0.6047 \| 21150 \| 0.0001 \| - \|
	\| 0.6061 \| 21200 \| 0.0002 \| - \|
	\| 0.6076 \| 21250 \| 0.0002 \| - \|
	\| 0.6090 \| 21300 \| 0.0001 \| - \|
	\| 0.6104 \| 21350 \| 0.0002 \| - \|
	\| 0.6119 \| 21400 \| 0.0001 \| - \|
	\| 0.6133 \| 21450 \| 0.0002 \| - \|
	\| 0.6147 \| 21500 \| 0.0001 \| - \|
	\| 0.6162 \| 21550 \| 0.0002 \| - \|
	\| 0.6176 \| 21600 \| 0.0001 \| - \|
	\| 0.6190 \| 21650 \| 0.0001 \| - \|
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	\| 0.6219 \| 21750 \| 0.0002 \| - \|
	\| 0.6233 \| 21800 \| 0.0001 \| - \|
	\| 0.6247 \| 21850 \| 0.0001 \| - \|
	\| 0.6262 \| 21900 \| 0.0001 \| - \|
	\| 0.6276 \| 21950 \| 0.0002 \| - \|
	\| 0.6290 \| 22000 \| 0.0002 \| - \|
	\| 0.6305 \| 22050 \| 0.0001 \| - \|
	\| 0.6319 \| 22100 \| 0.0002 \| - \|
	\| 0.6333 \| 22150 \| 0.0001 \| - \|
	\| 0.6347 \| 22200 \| 0.0001 \| - \|
	\| 0.6362 \| 22250 \| 0.0001 \| - \|
	\| 0.6376 \| 22300 \| 0.0002 \| - \|
	\| 0.6390 \| 22350 \| 0.0001 \| - \|
	\| 0.6405 \| 22400 \| 0.0003 \| - \|
	\| 0.6419 \| 22450 \| 0.0002 \| - \|
	\| 0.6433 \| 22500 \| 0.0002 \| - \|
	\| 0.6447 \| 22550 \| 0.0001 \| - \|
	\| 0.6462 \| 22600 \| 0.0002 \| - \|
	\| 0.6476 \| 22650 \| 0.0002 \| - \|
	\| 0.6490 \| 22700 \| 0.0002 \| - \|
	\| 0.6505 \| 22750 \| 0.0002 \| - \|
	\| 0.6519 \| 22800 \| 0.0001 \| - \|
	\| 0.6533 \| 22850 \| 0.0002 \| - \|
	\| 0.6548 \| 22900 \| 0.0002 \| - \|
	\| 0.6562 \| 22950 \| 0.0002 \| - \|
	\| 0.6576 \| 23000 \| 0.0002 \| - \|
	\| 0.6590 \| 23050 \| 0.0002 \| - \|
	\| 0.6605 \| 23100 \| 0.0002 \| - \|
	\| 0.6619 \| 23150 \| 0.0002 \| - \|
	\| 0.6633 \| 23200 \| 0.0002 \| - \|
	\| 0.6648 \| 23250 \| 0.0002 \| - \|
	\| 0.6662 \| 23300 \| 0.0002 \| - \|
	\| 0.6676 \| 23350 \| 0.0001 \| - \|
	\| 0.6690 \| 23400 \| 0.0002 \| - \|
	\| 0.6705 \| 23450 \| 0.0002 \| - \|
	\| 0.6719 \| 23500 \| 0.0001 \| - \|
	\| 0.6733 \| 23550 \| 0.0002 \| - \|
	\| 0.6748 \| 23600 \| 0.0001 \| - \|
	\| 0.6762 \| 23650 \| 0.0002 \| - \|
	\| 0.6776 \| 23700 \| 0.0002 \| - \|
	\| 0.6791 \| 23750 \| 0.0002 \| - \|
	\| 0.6805 \| 23800 \| 0.0001 \| - \|
	\| 0.6819 \| 23850 \| 0.0002 \| - \|
	\| 0.6833 \| 23900 \| 0.0003 \| - \|
	\| 0.6848 \| 23950 \| 0.0002 \| - \|
	\| 0.6862 \| 24000 \| 0.0002 \| - \|
	\| 0.6876 \| 24050 \| 0.0001 \| - \|
	\| 0.6891 \| 24100 \| 0.0002 \| - \|
	\| 0.6905 \| 24150 \| 0.0001 \| - \|
	\| 0.6919 \| 24200 \| 0.0003 \| - \|
	\| 0.6934 \| 24250 \| 0.0002 \| - \|
	\| 0.6948 \| 24300 \| 0.0001 \| - \|
	\| 0.6962 \| 24350 \| 0.0001 \| - \|
	\| 0.6976 \| 24400 \| 0.0001 \| - \|
	\| 0.6991 \| 24450 \| 0.0001 \| - \|
	\| 0.7005 \| 24500 \| 0.0001 \| - \|
	\| 0.7019 \| 24550 \| 0.0002 \| - \|
	\| 0.7034 \| 24600 \| 0.0001 \| - \|
	\| 0.7048 \| 24650 \| 0.0002 \| - \|
	\| 0.7062 \| 24700 \| 0.0001 \| - \|
	\| 0.7076 \| 24750 \| 0.0002 \| - \|
	\| 0.7091 \| 24800 \| 0.0002 \| - \|
	\| 0.7105 \| 24850 \| 0.0002 \| - \|
	\| 0.7119 \| 24900 \| 0.0002 \| - \|
	\| 0.7134 \| 24950 \| 0.0001 \| - \|
	\| 0.7148 \| 25000 \| 0.0002 \| - \|
	\| 0.7162 \| 25050 \| 0.0001 \| - \|
	\| 0.7177 \| 25100 \| 0.0002 \| - \|
	\| 0.7191 \| 25150 \| 0.0001 \| - \|
	\| 0.7205 \| 25200 \| 0.0001 \| - \|
	\| 0.7219 \| 25250 \| 0.0002 \| - \|
	\| 0.7234 \| 25300 \| 0.0002 \| - \|
	\| 0.7248 \| 25350 \| 0.0002 \| - \|
	\| 0.7262 \| 25400 \| 0.0001 \| - \|
	\| 0.7277 \| 25450 \| 0.0002 \| - \|
	\| 0.7291 \| 25500 \| 0.0002 \| - \|
	\| 0.7305 \| 25550 \| 0.0002 \| - \|
	\| 0.7320 \| 25600 \| 0.0001 \| - \|
	\| 0.7334 \| 25650 \| 0.0002 \| - \|
	\| 0.7348 \| 25700 \| 0.0002 \| - \|
	\| 0.7362 \| 25750 \| 0.0002 \| - \|
	\| 0.7377 \| 25800 \| 0.0002 \| - \|
	\| 0.7391 \| 25850 \| 0.0001 \| - \|
	\| 0.7405 \| 25900 \| 0.0002 \| - \|
	\| 0.7420 \| 25950 \| 0.0002 \| - \|
	\| 0.7434 \| 26000 \| 0.0001 \| - \|
	\| 0.7448 \| 26050 \| 0.0001 \| - \|
	\| 0.7462 \| 26100 \| 0.0001 \| - \|
	\| 0.7477 \| 26150 \| 0.0001 \| - \|
	\| 0.7491 \| 26200 \| 0.0001 \| - \|
	\| 0.7505 \| 26250 \| 0.0002 \| - \|
	\| 0.7520 \| 26300 \| 0.0001 \| - \|
	\| 0.7534 \| 26350 \| 0.0001 \| - \|
	\| 0.7548 \| 26400 \| 0.0001 \| - \|
	\| 0.7563 \| 26450 \| 0.0002 \| - \|
	\| 0.7577 \| 26500 \| 0.0001 \| - \|
	\| 0.7591 \| 26550 \| 0.0002 \| - \|
	\| 0.7605 \| 26600 \| 0.0003 \| - \|
	\| 0.7620 \| 26650 \| 0.0002 \| - \|
	\| 0.7634 \| 26700 \| 0.0002 \| - \|
	\| 0.7648 \| 26750 \| 0.0001 \| - \|
	\| 0.7663 \| 26800 \| 0.0001 \| - \|
	\| 0.7677 \| 26850 \| 0.0002 \| - \|
	\| 0.7691 \| 26900 \| 0.0002 \| - \|
	\| 0.7706 \| 26950 \| 0.0001 \| - \|
	\| 0.7720 \| 27000 \| 0.0001 \| - \|
	\| 0.7734 \| 27050 \| 0.0001 \| - \|
	\| 0.7748 \| 27100 \| 0.0001 \| - \|
	\| 0.7763 \| 27150 \| 0.0001 \| - \|
	\| 0.7777 \| 27200 \| 0.0002 \| - \|
	\| 0.7791 \| 27250 \| 0.0001 \| - \|
	\| 0.7806 \| 27300 \| 0.0001 \| - \|
	\| 0.7820 \| 27350 \| 0.0001 \| - \|
	\| 0.7834 \| 27400 \| 0.0002 \| - \|
	\| 0.7848 \| 27450 \| 0.0001 \| - \|
	\| 0.7863 \| 27500 \| 0.0001 \| - \|
	\| 0.7877 \| 27550 \| 0.0001 \| - \|
	\| 0.7891 \| 27600 \| 0.0001 \| - \|
	\| 0.7906 \| 27650 \| 0.0001 \| - \|
	\| 0.7920 \| 27700 \| 0.0001 \| - \|
	\| 0.7934 \| 27750 \| 0.0001 \| - \|
	\| 0.7949 \| 27800 \| 0.0001 \| - \|
	\| 0.7963 \| 27850 \| 0.0001 \| - \|
	\| 0.7977 \| 27900 \| 0.0001 \| - \|
	\| 0.7991 \| 27950 \| 0.0003 \| - \|
	\| 0.8006 \| 28000 \| 0.0001 \| - \|
	\| 0.8020 \| 28050 \| 0.0002 \| - \|
	\| 0.8034 \| 28100 \| 0.0001 \| - \|
	\| 0.8049 \| 28150 \| 0.0002 \| - \|
	\| 0.8063 \| 28200 \| 0.0 \| - \|
	\| 0.8077 \| 28250 \| 0.0001 \| - \|
	\| 0.8091 \| 28300 \| 0.0001 \| - \|
	\| 0.8106 \| 28350 \| 0.0001 \| - \|
	\| 0.8120 \| 28400 \| 0.0001 \| - \|
	\| 0.8134 \| 28450 \| 0.0002 \| - \|
	\| 0.8149 \| 28500 \| 0.0001 \| - \|
	\| 0.8163 \| 28550 \| 0.0001 \| - \|
	\| 0.8177 \| 28600 \| 0.0001 \| - \|
	\| 0.8192 \| 28650 \| 0.0001 \| - \|
	\| 0.8206 \| 28700 \| 0.0001 \| - \|
	\| 0.8220 \| 28750 \| 0.0002 \| - \|
	\| 0.8234 \| 28800 \| 0.0002 \| - \|
	\| 0.8249 \| 28850 \| 0.0002 \| - \|
	\| 0.8263 \| 28900 \| 0.0001 \| - \|
	\| 0.8277 \| 28950 \| 0.0002 \| - \|
	\| 0.8292 \| 29000 \| 0.0001 \| - \|
	\| 0.8306 \| 29050 \| 0.0002 \| - \|
	\| 0.8320 \| 29100 \| 0.0001 \| - \|
	\| 0.8335 \| 29150 \| 0.0001 \| - \|
	\| 0.8349 \| 29200 \| 0.0001 \| - \|
	\| 0.8363 \| 29250 \| 0.0001 \| - \|
	\| 0.8377 \| 29300 \| 0.0001 \| - \|
	\| 0.8392 \| 29350 \| 0.0001 \| - \|
	\| 0.8406 \| 29400 \| 0.0001 \| - \|
	\| 0.8420 \| 29450 \| 0.0002 \| - \|
	\| 0.8435 \| 29500 \| 0.0001 \| - \|
	\| 0.8449 \| 29550 \| 0.0001 \| - \|
	\| 0.8463 \| 29600 \| 0.0001 \| - \|
	\| 0.8477 \| 29650 \| 0.0001 \| - \|
	\| 0.8492 \| 29700 \| 0.0001 \| - \|
	\| 0.8506 \| 29750 \| 0.0002 \| - \|
	\| 0.8520 \| 29800 \| 0.0002 \| - \|
	\| 0.8535 \| 29850 \| 0.0001 \| - \|
	\| 0.8549 \| 29900 \| 0.0002 \| - \|
	\| 0.8563 \| 29950 \| 0.0002 \| - \|
	\| 0.8578 \| 30000 \| 0.0002 \| - \|
	\| 0.8592 \| 30050 \| 0.0001 \| - \|
	\| 0.8606 \| 30100 \| 0.0002 \| - \|
	\| 0.8620 \| 30150 \| 0.0002 \| - \|
	\| 0.8635 \| 30200 \| 0.0003 \| - \|
	\| 0.8649 \| 30250 \| 0.0001 \| - \|
	\| 0.8663 \| 30300 \| 0.0001 \| - \|
	\| 0.8678 \| 30350 \| 0.0001 \| - \|
	\| 0.8692 \| 30400 \| 0.0001 \| - \|
	\| 0.8706 \| 30450 \| 0.0002 \| - \|
	\| 0.8721 \| 30500 \| 0.0001 \| - \|
	\| 0.8735 \| 30550 \| 0.0001 \| - \|
	\| 0.8749 \| 30600 \| 0.0001 \| - \|
	\| 0.8763 \| 30650 \| 0.0002 \| - \|
	\| 0.8778 \| 30700 \| 0.0002 \| - \|
	\| 0.8792 \| 30750 \| 0.0001 \| - \|
	\| 0.8806 \| 30800 \| 0.0002 \| - \|
	\| 0.8821 \| 30850 \| 0.0002 \| - \|
	\| 0.8835 \| 30900 \| 0.0001 \| - \|
	\| 0.8849 \| 30950 \| 0.0002 \| - \|
	\| 0.8863 \| 31000 \| 0.0002 \| - \|
	\| 0.8878 \| 31050 \| 0.0002 \| - \|
	\| 0.8892 \| 31100 \| 0.0001 \| - \|
	\| 0.8906 \| 31150 \| 0.0001 \| - \|
	\| 0.8921 \| 31200 \| 0.0001 \| - \|
	\| 0.8935 \| 31250 \| 0.0001 \| - \|
	\| 0.8949 \| 31300 \| 0.0002 \| - \|
	\| 0.8964 \| 31350 \| 0.0002 \| - \|
	\| 0.8978 \| 31400 \| 0.0001 \| - \|
	\| 0.8992 \| 31450 \| 0.0001 \| - \|
	\| 0.9006 \| 31500 \| 0.0002 \| - \|
	\| 0.9021 \| 31550 \| 0.0002 \| - \|
	\| 0.9035 \| 31600 \| 0.0001 \| - \|
	\| 0.9049 \| 31650 \| 0.0002 \| - \|
	\| 0.9064 \| 31700 \| 0.0001 \| - \|
	\| 0.9078 \| 31750 \| 0.0001 \| - \|
	\| 0.9092 \| 31800 \| 0.0001 \| - \|
	\| 0.9107 \| 31850 \| 0.0002 \| - \|
	\| 0.9121 \| 31900 \| 0.0002 \| - \|
	\| 0.9135 \| 31950 \| 0.0001 \| - \|
	\| 0.9149 \| 32000 \| 0.0001 \| - \|
	\| 0.9164 \| 32050 \| 0.0001 \| - \|
	\| 0.9178 \| 32100 \| 0.0001 \| - \|
	\| 0.9192 \| 32150 \| 0.0001 \| - \|
	\| 0.9207 \| 32200 \| 0.0001 \| - \|
	\| 0.9221 \| 32250 \| 0.0001 \| - \|
	\| 0.9235 \| 32300 \| 0.0002 \| - \|
	\| 0.9249 \| 32350 \| 0.0001 \| - \|
	\| 0.9264 \| 32400 \| 0.0001 \| - \|
	\| 0.9278 \| 32450 \| 0.0002 \| - \|
	\| 0.9292 \| 32500 \| 0.0001 \| - \|
	\| 0.9307 \| 32550 \| 0.0001 \| - \|
	\| 0.9321 \| 32600 \| 0.0002 \| - \|
	\| 0.9335 \| 32650 \| 0.0001 \| - \|
	\| 0.9350 \| 32700 \| 0.0001 \| - \|
	\| 0.9364 \| 32750 \| 0.0001 \| - \|
	\| 0.9378 \| 32800 \| 0.0001 \| - \|
	\| 0.9392 \| 32850 \| 0.0001 \| - \|
	\| 0.9407 \| 32900 \| 0.0002 \| - \|
	\| 0.9421 \| 32950 \| 0.0002 \| - \|
	\| 0.9435 \| 33000 \| 0.0 \| - \|
	\| 0.9450 \| 33050 \| 0.0001 \| - \|
	\| 0.9464 \| 33100 \| 0.0001 \| - \|
	\| 0.9478 \| 33150 \| 0.0001 \| - \|
	\| 0.9492 \| 33200 \| 0.0001 \| - \|
	\| 0.9507 \| 33250 \| 0.0001 \| - \|
	\| 0.9521 \| 33300 \| 0.0001 \| - \|
	\| 0.9535 \| 33350 \| 0.0002 \| - \|
	\| 0.9550 \| 33400 \| 0.0001 \| - \|
	\| 0.9564 \| 33450 \| 0.0001 \| - \|
	\| 0.9578 \| 33500 \| 0.0002 \| - \|
	\| 0.9593 \| 33550 \| 0.0001 \| - \|
	\| 0.9607 \| 33600 \| 0.0001 \| - \|
	\| 0.9621 \| 33650 \| 0.0002 \| - \|
	\| 0.9635 \| 33700 \| 0.0002 \| - \|
	\| 0.9650 \| 33750 \| 0.0001 \| - \|
	\| 0.9664 \| 33800 \| 0.0001 \| - \|
	\| 0.9678 \| 33850 \| 0.0001 \| - \|
	\| 0.9693 \| 33900 \| 0.0001 \| - \|
	\| 0.9707 \| 33950 \| 0.0 \| - \|
	\| 0.9721 \| 34000 \| 0.0002 \| - \|
	\| 0.9736 \| 34050 \| 0.0001 \| - \|
	\| 0.9750 \| 34100 \| 0.0001 \| - \|
	\| 0.9764 \| 34150 \| 0.0001 \| - \|
	\| 0.9778 \| 34200 \| 0.0001 \| - \|
	\| 0.9793 \| 34250 \| 0.0002 \| - \|
	\| 0.9807 \| 34300 \| 0.0002 \| - \|
	\| 0.9821 \| 34350 \| 0.0001 \| - \|
	\| 0.9836 \| 34400 \| 0.0001 \| - \|
	\| 0.9850 \| 34450 \| 0.0001 \| - \|
	\| 0.9864 \| 34500 \| 0.0001 \| - \|
	\| 0.9878 \| 34550 \| 0.0001 \| - \|
	\| 0.9893 \| 34600 \| 0.0001 \| - \|
	\| 0.9907 \| 34650 \| 0.0001 \| - \|
	\| 0.9921 \| 34700 \| 0.0001 \| - \|
	\| 0.9936 \| 34750 \| 0.0001 \| - \|
	\| 0.9950 \| 34800 \| 0.0001 \| - \|
	\| 0.9964 \| 34850 \| 0.0001 \| - \|
	\| 0.9979 \| 34900 \| 0.0002 \| - \|
	\| 0.9993 \| 34950 \| 0.0002 \| - \|
	\| 1.0 \| 34975 \| - \| 0.0221 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.11.9
	- SetFit: 1.0.3
	- Sentence Transformers: 2.7.0
	- Transformers: 4.42.4
	- PyTorch: 2.4.0+cu121
	- Datasets: 2.21.0
	- Tokenizers: 0.19.1

	## Citation

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

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