Add SetFit model

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	---
	base_model: BAAI/bge-large-en-v1.5
	datasets:
	- nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
	library_name: setfit
	metrics:
	- accuracy
	pipeline_tag: text-classification
	tags:
	- setfit
	- sentence-transformers
	- text-classification
	- generated_from_setfit_trainer
	widget:
	- text: Can you filter by the 'Fashion' category and show me the products available?
	- text: Get forecast by service type.
	- text: How many orders were placed in each quarter?
	- text: What are the details of customers with no phone number listed?
	- text: I don't want to filter the database currently.
	inference: true
	model-index:
	- name: SetFit with BAAI/bge-large-en-v1.5
	results:
	- task:
	type: text-classification
	name: Text Classification
	dataset:
	name: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
	type: nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed
	split: test
	metrics:
	- type: accuracy
	value: 0.9739130434782609
	name: Accuracy
	---

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

	This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed) dataset that can be used for Text Classification. This SetFit model uses [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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: [nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed](https://huggingface.co/datasets/nazhan/brahmaputra-full-datasets-iter-8-2nd-fixed)
	<!-- - 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 \|
	\|:-------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| Aggregation \| <ul><li>'How many unique customers made purchases last year?'</li><li>'Determine the minimum order amount for each customer.'</li><li>'Get me sum of total_revenue.'</li></ul> \|
	\| Tablejoin \| <ul><li>'Show me a join of cash flow and variance.'</li><li>'Join data_asset_001_forecast with data_asset_kpi_bs tables.'</li><li>'Join data_asset_kpi_ma_product with data_asset_001_variance.'</li></ul> \|
	\| Lookup_1 \| <ul><li>'Show me asset impairment by year.'</li><li>'Get me data_asset_001_pcc group by category.'</li><li>'Show me data_asset_001_variance group by category.'</li></ul> \|
	\| Viewtables \| <ul><li>'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'</li><li>'How can I access the table directory for starhub_data_asset database to view all the available tables?'</li><li>'Please show me the tables that contain data related to customer transactions present in the starhub_data_asset database.'</li></ul> \|
	\| Generalreply \| <ul><li>"Oh my favorite food? That's a tough one. I love so many different kinds of food, but if I had to choose one it would probably be pizza. What about you? What's your favorite food?"</li><li>"Hmm, let me think... I'm actually pretty good at playing guitar! I've been playing for a few years now and it's always been one of my favorite hobbies. How about you, do you play any instruments or have any interesting hobbies?"</li><li>'What is your favorite color?'</li></ul> \|
	\| Lookup \| <ul><li>"Get me all the customers who haven't placed any orders."</li><li>'Get me the list of customers who have a phone number listed.'</li><li>'Can you filter by customers who registered without an email address?'</li></ul> \|
	\| Rejection \| <ul><li>"I'm not keen on producing any new data sets."</li><li>"Please don't generate any new data."</li><li>"I don't want to create any new data outputs."</li></ul> \|

	## Evaluation

	### Metrics
	\| Label \| Accuracy \|
	\|:--------\|:---------\|
	\| all \| 0.9739 \|

	## 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-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch")
	# Run inference
	preds = model("Get forecast by service type.")
	```

	<!--
	### Downstream Use

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

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

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

	<!--
	## Bias, Risks and Limitations

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

	<!--
	### Recommendations

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

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:-------------\|:----\|:-------\|:----\|
	\| Word count \| 2 \| 8.8252 \| 62 \|

	\| Label \| Training Sample Count \|
	\|:-------------\|:----------------------\|
	\| Tablejoin \| 129 \|
	\| Rejection \| 74 \|
	\| Aggregation \| 210 \|
	\| Lookup \| 60 \|
	\| Generalreply \| 59 \|
	\| Viewtables \| 75 \|
	\| Lookup_1 \| 217 \|

	### 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 \| 0.1706 \| - \|
	\| 0.0014 \| 50 \| 0.1976 \| - \|
	\| 0.0029 \| 100 \| 0.2045 \| - \|
	\| 0.0043 \| 150 \| 0.1846 \| - \|
	\| 0.0058 \| 200 \| 0.1608 \| - \|
	\| 0.0072 \| 250 \| 0.105 \| - \|
	\| 0.0087 \| 300 \| 0.1618 \| - \|
	\| 0.0101 \| 350 \| 0.1282 \| - \|
	\| 0.0116 \| 400 \| 0.0382 \| - \|
	\| 0.0130 \| 450 \| 0.0328 \| - \|
	\| 0.0145 \| 500 \| 0.0483 \| - \|
	\| 0.0159 \| 550 \| 0.0245 \| - \|
	\| 0.0174 \| 600 \| 0.0093 \| - \|
	\| 0.0188 \| 650 \| 0.0084 \| - \|
	\| 0.0203 \| 700 \| 0.0042 \| - \|
	\| 0.0217 \| 750 \| 0.0044 \| - \|
	\| 0.0231 \| 800 \| 0.0035 \| - \|
	\| 0.0246 \| 850 \| 0.0065 \| - \|
	\| 0.0260 \| 900 \| 0.0036 \| - \|
	\| 0.0275 \| 950 \| 0.0039 \| - \|
	\| 0.0289 \| 1000 \| 0.0037 \| - \|
	\| 0.0304 \| 1050 \| 0.005 \| - \|
	\| 0.0318 \| 1100 \| 0.0024 \| - \|
	\| 0.0333 \| 1150 \| 0.0023 \| - \|
	\| 0.0347 \| 1200 \| 0.0023 \| - \|
	\| 0.0362 \| 1250 \| 0.0019 \| - \|
	\| 0.0376 \| 1300 \| 0.0015 \| - \|
	\| 0.0391 \| 1350 \| 0.0023 \| - \|
	\| 0.0405 \| 1400 \| 0.0011 \| - \|
	\| 0.0420 \| 1450 \| 0.0017 \| - \|
	\| 0.0434 \| 1500 \| 0.0015 \| - \|
	\| 0.0448 \| 1550 \| 0.0014 \| - \|
	\| 0.0463 \| 1600 \| 0.0014 \| - \|
	\| 0.0477 \| 1650 \| 0.0013 \| - \|
	\| 0.0492 \| 1700 \| 0.0013 \| - \|
	\| 0.0506 \| 1750 \| 0.001 \| - \|
	\| 0.0521 \| 1800 \| 0.0013 \| - \|
	\| 0.0535 \| 1850 \| 0.0013 \| - \|
	\| 0.0550 \| 1900 \| 0.0011 \| - \|
	\| 0.0564 \| 1950 \| 0.0012 \| - \|
	\| 0.0579 \| 2000 \| 0.001 \| - \|
	\| 0.0593 \| 2050 \| 0.0012 \| - \|
	\| 0.0608 \| 2100 \| 0.0008 \| - \|
	\| 0.0622 \| 2150 \| 0.0008 \| - \|
	\| 0.0637 \| 2200 \| 0.001 \| - \|
	\| 0.0651 \| 2250 \| 0.0007 \| - \|
	\| 0.0665 \| 2300 \| 0.0006 \| - \|
	\| 0.0680 \| 2350 \| 0.0007 \| - \|
	\| 0.0694 \| 2400 \| 0.0008 \| - \|
	\| 0.0709 \| 2450 \| 0.0008 \| - \|
	\| 0.0723 \| 2500 \| 0.0006 \| - \|
	\| 0.0738 \| 2550 \| 0.0006 \| - \|
	\| 0.0752 \| 2600 \| 0.0007 \| - \|
	\| 0.0767 \| 2650 \| 0.0008 \| - \|
	\| 0.0781 \| 2700 \| 0.0005 \| - \|
	\| 0.0796 \| 2750 \| 0.0008 \| - \|
	\| 0.0810 \| 2800 \| 0.0006 \| - \|
	\| 0.0825 \| 2850 \| 0.0007 \| - \|
	\| 0.0839 \| 2900 \| 0.0007 \| - \|
	\| 0.0854 \| 2950 \| 0.0005 \| - \|
	\| 0.0868 \| 3000 \| 0.0007 \| - \|
	\| 0.0882 \| 3050 \| 0.0005 \| - \|
	\| 0.0897 \| 3100 \| 0.0005 \| - \|
	\| 0.0911 \| 3150 \| 0.0007 \| - \|
	\| 0.0926 \| 3200 \| 0.0005 \| - \|
	\| 0.0940 \| 3250 \| 0.0005 \| - \|
	\| 0.0955 \| 3300 \| 0.0007 \| - \|
	\| 0.0969 \| 3350 \| 0.0004 \| - \|
	\| 0.0984 \| 3400 \| 0.0005 \| - \|
	\| 0.0998 \| 3450 \| 0.0004 \| - \|
	\| 0.1013 \| 3500 \| 0.0007 \| - \|
	\| 0.1027 \| 3550 \| 0.0004 \| - \|
	\| 0.1042 \| 3600 \| 0.0004 \| - \|
	\| 0.1056 \| 3650 \| 0.0006 \| - \|
	\| 0.1071 \| 3700 \| 0.0005 \| - \|
	\| 0.1085 \| 3750 \| 0.0004 \| - \|
	\| 0.1100 \| 3800 \| 0.0005 \| - \|
	\| 0.1114 \| 3850 \| 0.0004 \| - \|
	\| 0.1128 \| 3900 \| 0.0004 \| - \|
	\| 0.1143 \| 3950 \| 0.0003 \| - \|
	\| 0.1157 \| 4000 \| 0.0004 \| - \|
	\| 0.1172 \| 4050 \| 0.0004 \| - \|
	\| 0.1186 \| 4100 \| 0.0004 \| - \|
	\| 0.1201 \| 4150 \| 0.0004 \| - \|
	\| 0.1215 \| 4200 \| 0.0004 \| - \|
	\| 0.1230 \| 4250 \| 0.0004 \| - \|
	\| 0.1244 \| 4300 \| 0.0003 \| - \|
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	\| 0.1273 \| 4400 \| 0.0003 \| - \|
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	\| 0.1331 \| 4600 \| 0.0003 \| - \|
	\| 0.1345 \| 4650 \| 0.0004 \| - \|
	\| 0.1360 \| 4700 \| 0.0003 \| - \|
	\| 0.1374 \| 4750 \| 0.0003 \| - \|
	\| 0.1389 \| 4800 \| 0.0002 \| - \|
	\| 0.1403 \| 4850 \| 0.0003 \| - \|
	\| 0.1418 \| 4900 \| 0.0003 \| - \|
	\| 0.1432 \| 4950 \| 0.0003 \| - \|
	\| 0.1447 \| 5000 \| 0.0002 \| - \|
	\| 0.1461 \| 5050 \| 0.0002 \| - \|
	\| 0.1476 \| 5100 \| 0.0003 \| - \|
	\| 0.1490 \| 5150 \| 0.0002 \| - \|
	\| 0.1505 \| 5200 \| 0.0004 \| - \|
	\| 0.1519 \| 5250 \| 0.0003 \| - \|
	\| 0.1534 \| 5300 \| 0.0003 \| - \|
	\| 0.1548 \| 5350 \| 0.0002 \| - \|
	\| 0.1562 \| 5400 \| 0.0003 \| - \|
	\| 0.1577 \| 5450 \| 0.0002 \| - \|
	\| 0.1591 \| 5500 \| 0.0002 \| - \|
	\| 0.1606 \| 5550 \| 0.0002 \| - \|
	\| 0.1620 \| 5600 \| 0.0002 \| - \|
	\| 0.1635 \| 5650 \| 0.0002 \| - \|
	\| 0.1649 \| 5700 \| 0.0003 \| - \|
	\| 0.1664 \| 5750 \| 0.0002 \| - \|
	\| 0.1678 \| 5800 \| 0.0003 \| - \|
	\| 0.1693 \| 5850 \| 0.0003 \| - \|
	\| 0.1707 \| 5900 \| 0.0002 \| - \|
	\| 0.1722 \| 5950 \| 0.0007 \| - \|
	\| 0.1736 \| 6000 \| 0.0003 \| - \|
	\| 0.1751 \| 6050 \| 0.0002 \| - \|
	\| 0.1765 \| 6100 \| 0.0002 \| - \|
	\| 0.1779 \| 6150 \| 0.0003 \| - \|
	\| 0.1794 \| 6200 \| 0.0002 \| - \|
	\| 0.1808 \| 6250 \| 0.0002 \| - \|
	\| 0.1823 \| 6300 \| 0.0002 \| - \|
	\| 0.1837 \| 6350 \| 0.0003 \| - \|
	\| 0.1852 \| 6400 \| 0.0002 \| - \|
	\| 0.1866 \| 6450 \| 0.0003 \| - \|
	\| 0.1881 \| 6500 \| 0.0002 \| - \|
	\| 0.1895 \| 6550 \| 0.0003 \| - \|
	\| 0.1910 \| 6600 \| 0.0002 \| - \|
	\| 0.1924 \| 6650 \| 0.0003 \| - \|
	\| 0.1939 \| 6700 \| 0.0002 \| - \|
	\| 0.1953 \| 6750 \| 0.0002 \| - \|
	\| 0.1968 \| 6800 \| 0.0002 \| - \|
	\| 0.1982 \| 6850 \| 0.0003 \| - \|
	\| 0.1996 \| 6900 \| 0.0003 \| - \|
	\| 0.2011 \| 6950 \| 0.0002 \| - \|
	\| 0.2025 \| 7000 \| 0.0002 \| - \|
	\| 0.2040 \| 7050 \| 0.0001 \| - \|
	\| 0.2054 \| 7100 \| 0.0002 \| - \|
	\| 0.2069 \| 7150 \| 0.0002 \| - \|
	\| 0.2083 \| 7200 \| 0.0002 \| - \|
	\| 0.2098 \| 7250 \| 0.0002 \| - \|
	\| 0.2112 \| 7300 \| 0.0002 \| - \|
	\| 0.2127 \| 7350 \| 0.0002 \| - \|
	\| 0.2141 \| 7400 \| 0.0002 \| - \|
	\| 0.2156 \| 7450 \| 0.0004 \| - \|
	\| 0.2170 \| 7500 \| 0.0002 \| - \|
	\| 0.2185 \| 7550 \| 0.0002 \| - \|
	\| 0.2199 \| 7600 \| 0.0003 \| - \|
	\| 0.2213 \| 7650 \| 0.0002 \| - \|
	\| 0.2228 \| 7700 \| 0.0003 \| - \|
	\| 0.2242 \| 7750 \| 0.0002 \| - \|
	\| 0.2257 \| 7800 \| 0.0001 \| - \|
	\| 0.2271 \| 7850 \| 0.0001 \| - \|
	\| 0.2286 \| 7900 \| 0.0002 \| - \|
	\| 0.2300 \| 7950 \| 0.0002 \| - \|
	\| 0.2315 \| 8000 \| 0.0001 \| - \|
	\| 0.2329 \| 8050 \| 0.0002 \| - \|
	\| 0.2344 \| 8100 \| 0.0002 \| - \|
	\| 0.2358 \| 8150 \| 0.0002 \| - \|
	\| 0.2373 \| 8200 \| 0.0002 \| - \|
	\| 0.2387 \| 8250 \| 0.0002 \| - \|
	\| 0.2402 \| 8300 \| 0.0001 \| - \|
	\| 0.2416 \| 8350 \| 0.0005 \| - \|
	\| 0.2430 \| 8400 \| 0.002 \| - \|
	\| 0.2445 \| 8450 \| 0.0037 \| - \|
	\| 0.2459 \| 8500 \| 0.0516 \| - \|
	\| 0.2474 \| 8550 \| 0.0028 \| - \|
	\| 0.2488 \| 8600 \| 0.0013 \| - \|
	\| 0.2503 \| 8650 \| 0.0017 \| - \|
	\| 0.2517 \| 8700 \| 0.0012 \| - \|
	\| 0.2532 \| 8750 \| 0.0513 \| - \|
	\| 0.2546 \| 8800 \| 0.001 \| - \|
	\| 0.2561 \| 8850 \| 0.035 \| - \|
	\| 0.2575 \| 8900 \| 0.0005 \| - \|
	\| 0.2590 \| 8950 \| 0.0076 \| - \|
	\| 0.2604 \| 9000 \| 0.0113 \| - \|
	\| 0.2619 \| 9050 \| 0.0006 \| - \|
	\| 0.2633 \| 9100 \| 0.0006 \| - \|
	\| 0.2647 \| 9150 \| 0.0018 \| - \|
	\| 0.2662 \| 9200 \| 0.0025 \| - \|
	\| 0.2676 \| 9250 \| 0.0011 \| - \|
	\| 0.2691 \| 9300 \| 0.001 \| - \|
	\| 0.2705 \| 9350 \| 0.0011 \| - \|
	\| 0.2720 \| 9400 \| 0.0004 \| - \|
	\| 0.2734 \| 9450 \| 0.0012 \| - \|
	\| 0.2749 \| 9500 \| 0.0011 \| - \|
	\| 0.2763 \| 9550 \| 0.0009 \| - \|
	\| 0.2778 \| 9600 \| 0.0003 \| - \|
	\| 0.2792 \| 9650 \| 0.0005 \| - \|
	\| 0.2807 \| 9700 \| 0.0006 \| - \|
	\| 0.2821 \| 9750 \| 0.0004 \| - \|
	\| 0.2836 \| 9800 \| 0.0004 \| - \|
	\| 0.2850 \| 9850 \| 0.0009 \| - \|
	\| 0.2865 \| 9900 \| 0.0014 \| - \|
	\| 0.2879 \| 9950 \| 0.0007 \| - \|
	\| 0.2893 \| 10000 \| 0.0014 \| - \|
	\| 0.2908 \| 10050 \| 0.0007 \| - \|
	\| 0.2922 \| 10100 \| 0.0003 \| - \|
	\| 0.2937 \| 10150 \| 0.0015 \| - \|
	\| 0.2951 \| 10200 \| 0.0003 \| - \|
	\| 0.2966 \| 10250 \| 0.0006 \| - \|
	\| 0.2980 \| 10300 \| 0.0003 \| - \|
	\| 0.2995 \| 10350 \| 0.0003 \| - \|
	\| 0.3009 \| 10400 \| 0.0004 \| - \|
	\| 0.3024 \| 10450 \| 0.0003 \| - \|
	\| 0.3038 \| 10500 \| 0.0008 \| - \|
	\| 0.3053 \| 10550 \| 0.0002 \| - \|
	\| 0.3067 \| 10600 \| 0.0005 \| - \|
	\| 0.3082 \| 10650 \| 0.0004 \| - \|
	\| 0.3096 \| 10700 \| 0.0006 \| - \|
	\| 0.3110 \| 10750 \| 0.0002 \| - \|
	\| 0.3125 \| 10800 \| 0.0008 \| - \|
	\| 0.3139 \| 10850 \| 0.0005 \| - \|
	\| 0.3154 \| 10900 \| 0.0004 \| - \|
	\| 0.3168 \| 10950 \| 0.0002 \| - \|
	\| 0.3183 \| 11000 \| 0.0002 \| - \|
	\| 0.3197 \| 11050 \| 0.0002 \| - \|
	\| 0.3212 \| 11100 \| 0.0006 \| - \|
	\| 0.3226 \| 11150 \| 0.0003 \| - \|
	\| 0.3241 \| 11200 \| 0.0002 \| - \|
	\| 0.3255 \| 11250 \| 0.0002 \| - \|
	\| 0.3270 \| 11300 \| 0.0003 \| - \|
	\| 0.3284 \| 11350 \| 0.0001 \| - \|
	\| 0.3299 \| 11400 \| 0.0002 \| - \|
	\| 0.3313 \| 11450 \| 0.0004 \| - \|
	\| 0.3327 \| 11500 \| 0.0006 \| - \|
	\| 0.3342 \| 11550 \| 0.0003 \| - \|
	\| 0.3356 \| 11600 \| 0.0003 \| - \|
	\| 0.3371 \| 11650 \| 0.0002 \| - \|
	\| 0.3385 \| 11700 \| 0.0002 \| - \|
	\| 0.3400 \| 11750 \| 0.0005 \| - \|
	\| 0.3414 \| 11800 \| 0.0003 \| - \|
	\| 0.3429 \| 11850 \| 0.0004 \| - \|
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	\| 0.3472 \| 12000 \| 0.0004 \| - \|
	\| 0.3487 \| 12050 \| 0.0002 \| - \|
	\| 0.3501 \| 12100 \| 0.0002 \| - \|
	\| 0.3516 \| 12150 \| 0.0002 \| - \|
	\| 0.3530 \| 12200 \| 0.0002 \| - \|
	\| 0.3544 \| 12250 \| 0.0002 \| - \|
	\| 0.3559 \| 12300 \| 0.0002 \| - \|
	\| 0.3573 \| 12350 \| 0.0003 \| - \|
	\| 0.3588 \| 12400 \| 0.0002 \| - \|
	\| 0.3602 \| 12450 \| 0.0002 \| - \|
	\| 0.3617 \| 12500 \| 0.0002 \| - \|
	\| 0.3631 \| 12550 \| 0.0005 \| - \|
	\| 0.3646 \| 12600 \| 0.0003 \| - \|
	\| 0.3660 \| 12650 \| 0.0003 \| - \|
	\| 0.3675 \| 12700 \| 0.0002 \| - \|
	\| 0.3689 \| 12750 \| 0.0004 \| - \|
	\| 0.3704 \| 12800 \| 0.0003 \| - \|
	\| 0.3718 \| 12850 \| 0.0003 \| - \|
	\| 0.3733 \| 12900 \| 0.0001 \| - \|
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	\| 0.3761 \| 13000 \| 0.0001 \| - \|
	\| 0.3776 \| 13050 \| 0.0002 \| - \|
	\| 0.3790 \| 13100 \| 0.0001 \| - \|
	\| 0.3805 \| 13150 \| 0.0001 \| - \|
	\| 0.3819 \| 13200 \| 0.0002 \| - \|
	\| 0.3834 \| 13250 \| 0.0003 \| - \|
	\| 0.3848 \| 13300 \| 0.0001 \| - \|
	\| 0.3863 \| 13350 \| 0.0003 \| - \|
	\| 0.3877 \| 13400 \| 0.0002 \| - \|
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	\| 0.3906 \| 13500 \| 0.0003 \| - \|
	\| 0.3921 \| 13550 \| 0.0002 \| - \|
	\| 0.3935 \| 13600 \| 0.0002 \| - \|
	\| 0.3950 \| 13650 \| 0.0001 \| - \|
	\| 0.3964 \| 13700 \| 0.0004 \| - \|
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	\| 0.3993 \| 13800 \| 0.0002 \| - \|
	\| 0.4007 \| 13850 \| 0.0003 \| - \|
	\| 0.4022 \| 13900 \| 0.0002 \| - \|
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	\| 0.4051 \| 14000 \| 0.0003 \| - \|
	\| 0.4065 \| 14050 \| 0.0003 \| - \|
	\| 0.4080 \| 14100 \| 0.0002 \| - \|
	\| 0.4094 \| 14150 \| 0.0002 \| - \|
	\| 0.4109 \| 14200 \| 0.0002 \| - \|
	\| 0.4123 \| 14250 \| 0.0002 \| - \|
	\| 0.4138 \| 14300 \| 0.0002 \| - \|
	\| 0.4152 \| 14350 \| 0.0002 \| - \|
	\| 0.4167 \| 14400 \| 0.0002 \| - \|
	\| 0.4181 \| 14450 \| 0.0003 \| - \|
	\| 0.4195 \| 14500 \| 0.0002 \| - \|
	\| 0.4210 \| 14550 \| 0.0002 \| - \|
	\| 0.4224 \| 14600 \| 0.0001 \| - \|
	\| 0.4239 \| 14650 \| 0.0003 \| - \|
	\| 0.4253 \| 14700 \| 0.0002 \| - \|
	\| 0.4268 \| 14750 \| 0.0002 \| - \|
	\| 0.4282 \| 14800 \| 0.0002 \| - \|
	\| 0.4297 \| 14850 \| 0.0002 \| - \|
	\| 0.4311 \| 14900 \| 0.0002 \| - \|
	\| 0.4326 \| 14950 \| 0.0003 \| - \|
	\| 0.4340 \| 15000 \| 0.0002 \| - \|
	\| 0.4355 \| 15050 \| 0.0002 \| - \|
	\| 0.4369 \| 15100 \| 0.0002 \| - \|
	\| 0.4384 \| 15150 \| 0.0002 \| - \|
	\| 0.4398 \| 15200 \| 0.0002 \| - \|
	\| 0.4412 \| 15250 \| 0.0001 \| - \|
	\| 0.4427 \| 15300 \| 0.0002 \| - \|
	\| 0.4441 \| 15350 \| 0.0003 \| - \|
	\| 0.4456 \| 15400 \| 0.0003 \| - \|
	\| 0.4470 \| 15450 \| 0.0003 \| - \|
	\| 0.4485 \| 15500 \| 0.0002 \| - \|
	\| 0.4499 \| 15550 \| 0.0001 \| - \|
	\| 0.4514 \| 15600 \| 0.0001 \| - \|
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	\| 0.4543 \| 15700 \| 0.0001 \| - \|
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	\| 0.4601 \| 15900 \| 0.0003 \| - \|
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	\| 0.4673 \| 16150 \| 0.0001 \| - \|
	\| 0.4687 \| 16200 \| 0.0001 \| - \|
	\| 0.4702 \| 16250 \| 0.0002 \| - \|
	\| 0.4716 \| 16300 \| 0.0003 \| - \|
	\| 0.4731 \| 16350 \| 0.0001 \| - \|
	\| 0.4745 \| 16400 \| 0.0001 \| - \|
	\| 0.4760 \| 16450 \| 0.0001 \| - \|
	\| 0.4774 \| 16500 \| 0.0002 \| - \|
	\| 0.4789 \| 16550 \| 0.0006 \| - \|
	\| 0.4803 \| 16600 \| 0.0002 \| - \|
	\| 0.4818 \| 16650 \| 0.0001 \| - \|
	\| 0.4832 \| 16700 \| 0.0002 \| - \|
	\| 0.4847 \| 16750 \| 0.0001 \| - \|
	\| 0.4861 \| 16800 \| 0.0003 \| - \|
	\| 0.4875 \| 16850 \| 0.0001 \| - \|
	\| 0.4890 \| 16900 \| 0.0002 \| - \|
	\| 0.4904 \| 16950 \| 0.0002 \| - \|
	\| 0.4919 \| 17000 \| 0.0001 \| - \|
	\| 0.4933 \| 17050 \| 0.0002 \| - \|
	\| 0.4948 \| 17100 \| 0.0001 \| - \|
	\| 0.4962 \| 17150 \| 0.0002 \| - \|
	\| 0.4977 \| 17200 \| 0.0002 \| - \|
	\| 0.4991 \| 17250 \| 0.0001 \| - \|
	\| 0.5006 \| 17300 \| 0.0002 \| - \|
	\| 0.5020 \| 17350 \| 0.0002 \| - \|
	\| 0.5035 \| 17400 \| 0.0001 \| - \|
	\| 0.5049 \| 17450 \| 0.0002 \| - \|
	\| 0.5064 \| 17500 \| 0.0003 \| - \|
	\| 0.5078 \| 17550 \| 0.0001 \| - \|
	\| 0.5092 \| 17600 \| 0.0002 \| - \|
	\| 0.5107 \| 17650 \| 0.0001 \| - \|
	\| 0.5121 \| 17700 \| 0.0002 \| - \|
	\| 0.5136 \| 17750 \| 0.0002 \| - \|
	\| 0.5150 \| 17800 \| 0.0003 \| - \|
	\| 0.5165 \| 17850 \| 0.0002 \| - \|
	\| 0.5179 \| 17900 \| 0.0002 \| - \|
	\| 0.5194 \| 17950 \| 0.0001 \| - \|
	\| 0.5208 \| 18000 \| 0.0002 \| - \|
	\| 0.5223 \| 18050 \| 0.0001 \| - \|
	\| 0.5237 \| 18100 \| 0.0001 \| - \|
	\| 0.5252 \| 18150 \| 0.0001 \| - \|
	\| 0.5266 \| 18200 \| 0.0003 \| - \|
	\| 0.5281 \| 18250 \| 0.0001 \| - \|
	\| 0.5295 \| 18300 \| 0.0001 \| - \|
	\| 0.5309 \| 18350 \| 0.0001 \| - \|
	\| 0.5324 \| 18400 \| 0.0001 \| - \|
	\| 0.5338 \| 18450 \| 0.0002 \| - \|
	\| 0.5353 \| 18500 \| 0.0008 \| - \|
	\| 0.5367 \| 18550 \| 0.0002 \| - \|
	\| 0.5382 \| 18600 \| 0.0001 \| - \|
	\| 0.5396 \| 18650 \| 0.0002 \| - \|
	\| 0.5411 \| 18700 \| 0.0002 \| - \|
	\| 0.5425 \| 18750 \| 0.0001 \| - \|
	\| 0.5440 \| 18800 \| 0.0001 \| - \|
	\| 0.5454 \| 18850 \| 0.0001 \| - \|
	\| 0.5469 \| 18900 \| 0.0002 \| - \|
	\| 0.5483 \| 18950 \| 0.0001 \| - \|
	\| 0.5498 \| 19000 \| 0.0001 \| - \|
	\| 0.5512 \| 19050 \| 0.0001 \| - \|
	\| 0.5526 \| 19100 \| 0.0002 \| - \|
	\| 0.5541 \| 19150 \| 0.0001 \| - \|
	\| 0.5555 \| 19200 \| 0.0001 \| - \|
	\| 0.5570 \| 19250 \| 0.0002 \| - \|
	\| 0.5584 \| 19300 \| 0.0001 \| - \|
	\| 0.5599 \| 19350 \| 0.0002 \| - \|
	\| 0.5613 \| 19400 \| 0.0001 \| - \|
	\| 0.5628 \| 19450 \| 0.0002 \| - \|
	\| 0.5642 \| 19500 \| 0.0001 \| - \|
	\| 0.5657 \| 19550 \| 0.0002 \| - \|
	\| 0.5671 \| 19600 \| 0.0002 \| - \|
	\| 0.5686 \| 19650 \| 0.0002 \| - \|
	\| 0.5700 \| 19700 \| 0.0001 \| - \|
	\| 0.5715 \| 19750 \| 0.0001 \| - \|
	\| 0.5729 \| 19800 \| 0.0003 \| - \|
	\| 0.5743 \| 19850 \| 0.0001 \| - \|
	\| 0.5758 \| 19900 \| 0.0001 \| - \|
	\| 0.5772 \| 19950 \| 0.0001 \| - \|
	\| 0.5787 \| 20000 \| 0.0001 \| - \|
	\| 0.5801 \| 20050 \| 0.0001 \| - \|
	\| 0.5816 \| 20100 \| 0.0001 \| - \|
	\| 0.5830 \| 20150 \| 0.0001 \| - \|
	\| 0.5845 \| 20200 \| 0.0001 \| - \|
	\| 0.5859 \| 20250 \| 0.0001 \| - \|
	\| 0.5874 \| 20300 \| 0.0002 \| - \|
	\| 0.5888 \| 20350 \| 0.0002 \| - \|
	\| 0.5903 \| 20400 \| 0.0001 \| - \|
	\| 0.5917 \| 20450 \| 0.0002 \| - \|
	\| 0.5932 \| 20500 \| 0.0001 \| - \|
	\| 0.5946 \| 20550 \| 0.0001 \| - \|
	\| 0.5960 \| 20600 \| 0.0001 \| - \|
	\| 0.5975 \| 20650 \| 0.0002 \| - \|
	\| 0.5989 \| 20700 \| 0.0002 \| - \|
	\| 0.6004 \| 20750 \| 0.0001 \| - \|
	\| 0.6018 \| 20800 \| 0.0001 \| - \|
	\| 0.6033 \| 20850 \| 0.0002 \| - \|
	\| 0.6047 \| 20900 \| 0.0001 \| - \|
	\| 0.6062 \| 20950 \| 0.0002 \| - \|
	\| 0.6076 \| 21000 \| 0.0001 \| - \|
	\| 0.6091 \| 21050 \| 0.0001 \| - \|
	\| 0.6105 \| 21100 \| 0.0001 \| - \|
	\| 0.6120 \| 21150 \| 0.0002 \| - \|
	\| 0.6134 \| 21200 \| 0.0001 \| - \|
	\| 0.6149 \| 21250 \| 0.0001 \| - \|
	\| 0.6163 \| 21300 \| 0.0001 \| - \|
	\| 0.6177 \| 21350 \| 0.0001 \| - \|
	\| 0.6192 \| 21400 \| 0.0002 \| - \|
	\| 0.6206 \| 21450 \| 0.0001 \| - \|
	\| 0.6221 \| 21500 \| 0.0002 \| - \|
	\| 0.6235 \| 21550 \| 0.0003 \| - \|
	\| 0.6250 \| 21600 \| 0.0001 \| - \|
	\| 0.6264 \| 21650 \| 0.0001 \| - \|
	\| 0.6279 \| 21700 \| 0.0001 \| - \|
	\| 0.6293 \| 21750 \| 0.0001 \| - \|
	\| 0.6308 \| 21800 \| 0.0002 \| - \|
	\| 0.6322 \| 21850 \| 0.0001 \| - \|
	\| 0.6337 \| 21900 \| 0.0001 \| - \|
	\| 0.6351 \| 21950 \| 0.0001 \| - \|
	\| 0.6366 \| 22000 \| 0.0002 \| - \|
	\| 0.6380 \| 22050 \| 0.0001 \| - \|
	\| 0.6394 \| 22100 \| 0.0001 \| - \|
	\| 0.6409 \| 22150 \| 0.0002 \| - \|
	\| 0.6423 \| 22200 \| 0.0002 \| - \|
	\| 0.6438 \| 22250 \| 0.0003 \| - \|
	\| 0.6452 \| 22300 \| 0.0001 \| - \|
	\| 0.6467 \| 22350 \| 0.0001 \| - \|
	\| 0.6481 \| 22400 \| 0.0001 \| - \|
	\| 0.6496 \| 22450 \| 0.0002 \| - \|
	\| 0.6510 \| 22500 \| 0.0001 \| - \|
	\| 0.6525 \| 22550 \| 0.0001 \| - \|
	\| 0.6539 \| 22600 \| 0.0001 \| - \|
	\| 0.6554 \| 22650 \| 0.0001 \| - \|
	\| 0.6568 \| 22700 \| 0.0002 \| - \|
	\| 0.6583 \| 22750 \| 0.0001 \| - \|
	\| 0.6597 \| 22800 \| 0.0001 \| - \|
	\| 0.6611 \| 22850 \| 0.0001 \| - \|
	\| 0.6626 \| 22900 \| 0.0001 \| - \|
	\| 0.6640 \| 22950 \| 0.0001 \| - \|
	\| 0.6655 \| 23000 \| 0.0001 \| - \|
	\| 0.6669 \| 23050 \| 0.0002 \| - \|
	\| 0.6684 \| 23100 \| 0.0001 \| - \|
	\| 0.6698 \| 23150 \| 0.0001 \| - \|
	\| 0.6713 \| 23200 \| 0.0001 \| - \|
	\| 0.6727 \| 23250 \| 0.0001 \| - \|
	\| 0.6742 \| 23300 \| 0.0002 \| - \|
	\| 0.6756 \| 23350 \| 0.0002 \| - \|
	\| 0.6771 \| 23400 \| 0.0001 \| - \|
	\| 0.6785 \| 23450 \| 0.0001 \| - \|
	\| 0.6800 \| 23500 \| 0.0001 \| - \|
	\| 0.6814 \| 23550 \| 0.0001 \| - \|
	\| 0.6829 \| 23600 \| 0.0002 \| - \|
	\| 0.6843 \| 23650 \| 0.0001 \| - \|
	\| 0.6857 \| 23700 \| 0.0001 \| - \|
	\| 0.6872 \| 23750 \| 0.0001 \| - \|
	\| 0.6886 \| 23800 \| 0.0001 \| - \|
	\| 0.6901 \| 23850 \| 0.0002 \| - \|
	\| 0.6915 \| 23900 \| 0.0001 \| - \|
	\| 0.6930 \| 23950 \| 0.0001 \| - \|
	\| 0.6944 \| 24000 \| 0.0002 \| - \|
	\| 0.6959 \| 24050 \| 0.0001 \| - \|
	\| 0.6973 \| 24100 \| 0.0001 \| - \|
	\| 0.6988 \| 24150 \| 0.0001 \| - \|
	\| 0.7002 \| 24200 \| 0.0001 \| - \|
	\| 0.7017 \| 24250 \| 0.0001 \| - \|
	\| 0.7031 \| 24300 \| 0.0001 \| - \|
	\| 0.7046 \| 24350 \| 0.0001 \| - \|
	\| 0.7060 \| 24400 \| 0.0001 \| - \|
	\| 0.7074 \| 24450 \| 0.0002 \| - \|
	\| 0.7089 \| 24500 \| 0.0001 \| - \|
	\| 0.7103 \| 24550 \| 0.0002 \| - \|
	\| 0.7118 \| 24600 \| 0.0001 \| - \|
	\| 0.7132 \| 24650 \| 0.0001 \| - \|
	\| 0.7147 \| 24700 \| 0.0001 \| - \|
	\| 0.7161 \| 24750 \| 0.0001 \| - \|
	\| 0.7176 \| 24800 \| 0.0001 \| - \|
	\| 0.7190 \| 24850 \| 0.0001 \| - \|
	\| 0.7205 \| 24900 \| 0.0001 \| - \|
	\| 0.7219 \| 24950 \| 0.0001 \| - \|
	\| 0.7234 \| 25000 \| 0.0001 \| - \|
	\| 0.7248 \| 25050 \| 0.0002 \| - \|
	\| 0.7263 \| 25100 \| 0.0001 \| - \|
	\| 0.7277 \| 25150 \| 0.0001 \| - \|
	\| 0.7291 \| 25200 \| 0.0001 \| - \|
	\| 0.7306 \| 25250 \| 0.0001 \| - \|
	\| 0.7320 \| 25300 \| 0.0001 \| - \|
	\| 0.7335 \| 25350 \| 0.0001 \| - \|
	\| 0.7349 \| 25400 \| 0.0 \| - \|
	\| 0.7364 \| 25450 \| 0.0001 \| - \|
	\| 0.7378 \| 25500 \| 0.0001 \| - \|
	\| 0.7393 \| 25550 \| 0.0001 \| - \|
	\| 0.7407 \| 25600 \| 0.0001 \| - \|
	\| 0.7422 \| 25650 \| 0.0001 \| - \|
	\| 0.7436 \| 25700 \| 0.0001 \| - \|
	\| 0.7451 \| 25750 \| 0.0001 \| - \|
	\| 0.7465 \| 25800 \| 0.0 \| - \|
	\| 0.7480 \| 25850 \| 0.0001 \| - \|
	\| 0.7494 \| 25900 \| 0.0001 \| - \|
	\| 0.7508 \| 25950 \| 0.0001 \| - \|
	\| 0.7523 \| 26000 \| 0.0001 \| - \|
	\| 0.7537 \| 26050 \| 0.0001 \| - \|
	\| 0.7552 \| 26100 \| 0.0001 \| - \|
	\| 0.7566 \| 26150 \| 0.0001 \| - \|
	\| 0.7581 \| 26200 \| 0.0001 \| - \|
	\| 0.7595 \| 26250 \| 0.0001 \| - \|
	\| 0.7610 \| 26300 \| 0.0001 \| - \|
	\| 0.7624 \| 26350 \| 0.0001 \| - \|
	\| 0.7639 \| 26400 \| 0.0002 \| - \|
	\| 0.7653 \| 26450 \| 0.0001 \| - \|
	\| 0.7668 \| 26500 \| 0.0001 \| - \|
	\| 0.7682 \| 26550 \| 0.0001 \| - \|
	\| 0.7697 \| 26600 \| 0.0001 \| - \|
	\| 0.7711 \| 26650 \| 0.0002 \| - \|
	\| 0.7725 \| 26700 \| 0.0001 \| - \|
	\| 0.7740 \| 26750 \| 0.0001 \| - \|
	\| 0.7754 \| 26800 \| 0.0001 \| - \|
	\| 0.7769 \| 26850 \| 0.0001 \| - \|
	\| 0.7783 \| 26900 \| 0.0001 \| - \|
	\| 0.7798 \| 26950 \| 0.0001 \| - \|
	\| 0.7812 \| 27000 \| 0.0001 \| - \|
	\| 0.7827 \| 27050 \| 0.0001 \| - \|
	\| 0.7841 \| 27100 \| 0.0001 \| - \|
	\| 0.7856 \| 27150 \| 0.0001 \| - \|
	\| 0.7870 \| 27200 \| 0.0001 \| - \|
	\| 0.7885 \| 27250 \| 0.0001 \| - \|
	\| 0.7899 \| 27300 \| 0.0001 \| - \|
	\| 0.7914 \| 27350 \| 0.0001 \| - \|
	\| 0.7928 \| 27400 \| 0.0001 \| - \|
	\| 0.7942 \| 27450 \| 0.0001 \| - \|
	\| 0.7957 \| 27500 \| 0.0001 \| - \|
	\| 0.7971 \| 27550 \| 0.0001 \| - \|
	\| 0.7986 \| 27600 \| 0.0001 \| - \|
	\| 0.8000 \| 27650 \| 0.0001 \| - \|
	\| 0.8015 \| 27700 \| 0.0001 \| - \|
	\| 0.8029 \| 27750 \| 0.0001 \| - \|
	\| 0.8044 \| 27800 \| 0.0 \| - \|
	\| 0.8058 \| 27850 \| 0.0001 \| - \|
	\| 0.8073 \| 27900 \| 0.0001 \| - \|
	\| 0.8087 \| 27950 \| 0.0001 \| - \|
	\| 0.8102 \| 28000 \| 0.0001 \| - \|
	\| 0.8116 \| 28050 \| 0.0 \| - \|
	\| 0.8131 \| 28100 \| 0.0 \| - \|
	\| 0.8145 \| 28150 \| 0.0001 \| - \|
	\| 0.8159 \| 28200 \| 0.0001 \| - \|
	\| 0.8174 \| 28250 \| 0.0001 \| - \|
	\| 0.8188 \| 28300 \| 0.0001 \| - \|
	\| 0.8203 \| 28350 \| 0.0001 \| - \|
	\| 0.8217 \| 28400 \| 0.0001 \| - \|
	\| 0.8232 \| 28450 \| 0.0001 \| - \|
	\| 0.8246 \| 28500 \| 0.0001 \| - \|
	\| 0.8261 \| 28550 \| 0.0001 \| - \|
	\| 0.8275 \| 28600 \| 0.0001 \| - \|
	\| 0.8290 \| 28650 \| 0.0001 \| - \|
	\| 0.8304 \| 28700 \| 0.0001 \| - \|
	\| 0.8319 \| 28750 \| 0.0001 \| - \|
	\| 0.8333 \| 28800 \| 0.0001 \| - \|
	\| 0.8348 \| 28850 \| 0.0002 \| - \|
	\| 0.8362 \| 28900 \| 0.0001 \| - \|
	\| 0.8376 \| 28950 \| 0.0001 \| - \|
	\| 0.8391 \| 29000 \| 0.0001 \| - \|
	\| 0.8405 \| 29050 \| 0.0001 \| - \|
	\| 0.8420 \| 29100 \| 0.0001 \| - \|
	\| 0.8434 \| 29150 \| 0.0001 \| - \|
	\| 0.8449 \| 29200 \| 0.0 \| - \|
	\| 0.8463 \| 29250 \| 0.0001 \| - \|
	\| 0.8478 \| 29300 \| 0.0001 \| - \|
	\| 0.8492 \| 29350 \| 0.0001 \| - \|
	\| 0.8507 \| 29400 \| 0.0001 \| - \|
	\| 0.8521 \| 29450 \| 0.0001 \| - \|
	\| 0.8536 \| 29500 \| 0.0001 \| - \|
	\| 0.8550 \| 29550 \| 0.0001 \| - \|
	\| 0.8565 \| 29600 \| 0.0002 \| - \|
	\| 0.8579 \| 29650 \| 0.0 \| - \|
	\| 0.8594 \| 29700 \| 0.0001 \| - \|
	\| 0.8608 \| 29750 \| 0.0001 \| - \|
	\| 0.8622 \| 29800 \| 0.0001 \| - \|
	\| 0.8637 \| 29850 \| 0.0001 \| - \|
	\| 0.8651 \| 29900 \| 0.0 \| - \|
	\| 0.8666 \| 29950 \| 0.0001 \| - \|
	\| 0.8680 \| 30000 \| 0.0001 \| - \|
	\| 0.8695 \| 30050 \| 0.0001 \| - \|
	\| 0.8709 \| 30100 \| 0.0 \| - \|
	\| 0.8724 \| 30150 \| 0.0 \| - \|
	\| 0.8738 \| 30200 \| 0.0001 \| - \|
	\| 0.8753 \| 30250 \| 0.0001 \| - \|
	\| 0.8767 \| 30300 \| 0.0001 \| - \|
	\| 0.8782 \| 30350 \| 0.0001 \| - \|
	\| 0.8796 \| 30400 \| 0.0001 \| - \|
	\| 0.8811 \| 30450 \| 0.0001 \| - \|
	\| 0.8825 \| 30500 \| 0.0001 \| - \|
	\| 0.8839 \| 30550 \| 0.0001 \| - \|
	\| 0.8854 \| 30600 \| 0.0 \| - \|
	\| 0.8868 \| 30650 \| 0.0001 \| - \|
	\| 0.8883 \| 30700 \| 0.0001 \| - \|
	\| 0.8897 \| 30750 \| 0.0001 \| - \|
	\| 0.8912 \| 30800 \| 0.0001 \| - \|
	\| 0.8926 \| 30850 \| 0.0 \| - \|
	\| 0.8941 \| 30900 \| 0.0 \| - \|
	\| 0.8955 \| 30950 \| 0.0001 \| - \|
	\| 0.8970 \| 31000 \| 0.0001 \| - \|
	\| 0.8984 \| 31050 \| 0.0001 \| - \|
	\| 0.8999 \| 31100 \| 0.0001 \| - \|
	\| 0.9013 \| 31150 \| 0.0 \| - \|
	\| 0.9028 \| 31200 \| 0.0001 \| - \|
	\| 0.9042 \| 31250 \| 0.0001 \| - \|
	\| 0.9056 \| 31300 \| 0.0001 \| - \|
	\| 0.9071 \| 31350 \| 0.0001 \| - \|
	\| 0.9085 \| 31400 \| 0.0001 \| - \|
	\| 0.9100 \| 31450 \| 0.0002 \| - \|
	\| 0.9114 \| 31500 \| 0.0001 \| - \|
	\| 0.9129 \| 31550 \| 0.0001 \| - \|
	\| 0.9143 \| 31600 \| 0.0001 \| - \|
	\| 0.9158 \| 31650 \| 0.0001 \| - \|
	\| 0.9172 \| 31700 \| 0.0001 \| - \|
	\| 0.9187 \| 31750 \| 0.0001 \| - \|
	\| 0.9201 \| 31800 \| 0.0001 \| - \|
	\| 0.9216 \| 31850 \| 0.0001 \| - \|
	\| 0.9230 \| 31900 \| 0.0001 \| - \|
	\| 0.9245 \| 31950 \| 0.0001 \| - \|
	\| 0.9259 \| 32000 \| 0.0001 \| - \|
	\| 0.9273 \| 32050 \| 0.0 \| - \|
	\| 0.9288 \| 32100 \| 0.0002 \| - \|
	\| 0.9302 \| 32150 \| 0.0001 \| - \|
	\| 0.9317 \| 32200 \| 0.0001 \| - \|
	\| 0.9331 \| 32250 \| 0.0001 \| - \|
	\| 0.9346 \| 32300 \| 0.0002 \| - \|
	\| 0.9360 \| 32350 \| 0.0 \| - \|
	\| 0.9375 \| 32400 \| 0.0001 \| - \|
	\| 0.9389 \| 32450 \| 0.0001 \| - \|
	\| 0.9404 \| 32500 \| 0.0 \| - \|
	\| 0.9418 \| 32550 \| 0.0001 \| - \|
	\| 0.9433 \| 32600 \| 0.0001 \| - \|
	\| 0.9447 \| 32650 \| 0.0001 \| - \|
	\| 0.9462 \| 32700 \| 0.0001 \| - \|
	\| 0.9476 \| 32750 \| 0.0001 \| - \|
	\| 0.9490 \| 32800 \| 0.0001 \| - \|
	\| 0.9505 \| 32850 \| 0.0001 \| - \|
	\| 0.9519 \| 32900 \| 0.0 \| - \|
	\| 0.9534 \| 32950 \| 0.0001 \| - \|
	\| 0.9548 \| 33000 \| 0.0001 \| - \|
	\| 0.9563 \| 33050 \| 0.0001 \| - \|
	\| 0.9577 \| 33100 \| 0.0001 \| - \|
	\| 0.9592 \| 33150 \| 0.0001 \| - \|
	\| 0.9606 \| 33200 \| 0.0001 \| - \|
	\| 0.9621 \| 33250 \| 0.0001 \| - \|
	\| 0.9635 \| 33300 \| 0.0001 \| - \|
	\| 0.9650 \| 33350 \| 0.0 \| - \|
	\| 0.9664 \| 33400 \| 0.0001 \| - \|
	\| 0.9679 \| 33450 \| 0.0001 \| - \|
	\| 0.9693 \| 33500 \| 0.0 \| - \|
	\| 0.9707 \| 33550 \| 0.0001 \| - \|
	\| 0.9722 \| 33600 \| 0.0 \| - \|
	\| 0.9736 \| 33650 \| 0.0001 \| - \|
	\| 0.9751 \| 33700 \| 0.0001 \| - \|
	\| 0.9765 \| 33750 \| 0.0001 \| - \|
	\| 0.9780 \| 33800 \| 0.0 \| - \|
	\| 0.9794 \| 33850 \| 0.0001 \| - \|
	\| 0.9809 \| 33900 \| 0.0001 \| - \|
	\| 0.9823 \| 33950 \| 0.0001 \| - \|
	\| 0.9838 \| 34000 \| 0.0001 \| - \|
	\| 0.9852 \| 34050 \| 0.0 \| - \|
	\| 0.9867 \| 34100 \| 0.0001 \| - \|
	\| 0.9881 \| 34150 \| 0.0 \| - \|
	\| 0.9896 \| 34200 \| 0.0001 \| - \|
	\| 0.9910 \| 34250 \| 0.0 \| - \|
	\| 0.9924 \| 34300 \| 0.0001 \| - \|
	\| 0.9939 \| 34350 \| 0.0 \| - \|
	\| 0.9953 \| 34400 \| 0.0001 \| - \|
	\| 0.9968 \| 34450 \| 0.0 \| - \|
	\| 0.9982 \| 34500 \| 0.0 \| - \|
	\| 0.9997 \| 34550 \| 0.0001 \| - \|
	\| 1.0 \| 34561 \| - \| 0.0036 \|

	* The bold row denotes the saved checkpoint.
	### Framework Versions
	- Python: 3.11.9
	- SetFit: 1.1.0.dev0
	- Sentence Transformers: 3.0.1
	- Transformers: 4.44.2
	- 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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