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nazhan
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bge-large-en-v1.5-brahmaputra-iter-8-2-epoch

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community

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

This is a SetFit model trained on the nazhan/brahmaputra-full-datasets-iter-8 dataset that can be used for Text Classification. This SetFit model uses BAAI/bge-large-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
Lookup_1
  • 'Analyze product category revenue impact.'
  • 'Show me monthly EBIT by product.'
  • 'Visualize M&A deal size distribution.'
Tablejoin
  • 'Could you link the Orders and Employees tables to find out which departments are processing the most orders?'
  • 'Is it possible to combine the Employees and Orders tables to see which employees are assigned to specific order types?'
  • 'Join data_asset_kpi_cf with data_asset_001_kpm tables.'
Lookup
  • "Show me the details of employees with the last name 'Smith'."
  • "Filter by customers with the first name 'Emily' and show me their email addresses."
  • "Show me the products with 'Tablet' in the name and filter by price above 200."
Rejection
  • "Let's not worry about generating additional data."
  • "I'd prefer not to apply any filters."
  • "I don't want to sort or filter right now."
Viewtables
  • 'What is the inventory of tables held in the starhub_data_asset database?'
  • 'What tables are available in the starhub_data_asset database for performing basic data explorations?'
  • 'What is the complete list of all the tables stored in the starhub_data_asset database that require a join operation for data analysis?'
Generalreply
  • "Oh, I enjoy spending my free time doing a few different things! Sometimes I like to read, other times I might go for a walk or watch a movie. It really just depends on what I'm in the mood for. What about you, how do you like to spend your free time?"
  • 'What is your favorite color?'
  • "that's not good."
Aggregation
  • 'What’s the total number of products sold in the Electronics category?'
  • 'Determine the total number of orders placed during promotional periods.'
  • 'What’s the total sales amount recorded in the Orders table?'

Evaluation

Metrics

Label Accuracy
all 1.0

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("nazhan/bge-large-en-v1.5-brahmaputra-iter-8-2-epoch")
# Run inference
preds = model("How's your day going?")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 3 11.0696 62
Label Training Sample Count
Tablejoin 112
Rejection 67
Aggregation 71
Lookup 56
Generalreply 69
Viewtables 73
Lookup_1 69

Training Hyperparameters

  • batch_size: (16, 16)
  • num_epochs: (2, 2)
  • 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
0.0001 1 0.1865 -
0.0035 50 0.1599 -
0.0070 100 0.1933 -
0.0106 150 0.1595 -
0.0141 200 0.0899 -
0.0176 250 0.1334 -
0.0211 300 0.0722 -
0.0246 350 0.0411 -
0.0282 400 0.0171 -
0.0317 450 0.0293 -
0.0352 500 0.0218 -
0.0387 550 0.0057 -
0.0422 600 0.0065 -
0.0458 650 0.0047 -
0.0493 700 0.0045 -
0.0528 750 0.0048 -
0.0563 800 0.0032 -
0.0599 850 0.0038 -
0.0634 900 0.0033 -
0.0669 950 0.0027 -
0.0704 1000 0.0025 -
0.0739 1050 0.0024 -
0.0775 1100 0.0021 -
0.0810 1150 0.0025 -
0.0845 1200 0.0016 -
0.0880 1250 0.0019 -
0.0915 1300 0.0017 -
0.0951 1350 0.0016 -
0.0986 1400 0.0025 -
0.1021 1450 0.0016 -
0.1056 1500 0.0015 -
0.1091 1550 0.0012 -
0.1127 1600 0.001 -
0.1162 1650 0.0012 -
0.1197 1700 0.0012 -
0.1232 1750 0.0013 -
0.1267 1800 0.0012 -
0.1303 1850 0.0009 -
0.1338 1900 0.0011 -
0.1373 1950 0.001 -
0.1408 2000 0.0009 -
0.1443 2050 0.0009 -
0.1479 2100 0.0008 -
0.1514 2150 0.0007 -
0.1549 2200 0.0008 -
0.1584 2250 0.0008 -
0.1619 2300 0.0008 -
0.1655 2350 0.0007 -
0.1690 2400 0.0008 -
0.1725 2450 0.0006 -
0.1760 2500 0.0005 -
0.1796 2550 0.0006 -
0.1831 2600 0.0005 -
0.1866 2650 0.0006 -
0.1901 2700 0.0005 -
0.1936 2750 0.0007 -
0.1972 2800 0.0006 -
0.2007 2850 0.0005 -
0.2042 2900 0.0006 -
0.2077 2950 0.0007 -
0.2112 3000 0.0006 -
0.2148 3050 0.0005 -
0.2183 3100 0.0005 -
0.2218 3150 0.0005 -
0.2253 3200 0.0006 -
0.2288 3250 0.0005 -
0.2324 3300 0.0006 -
0.2359 3350 0.0004 -
0.2394 3400 0.0005 -
0.2429 3450 0.0005 -
0.2464 3500 0.0004 -
0.2500 3550 0.0006 -
0.2535 3600 0.0004 -
0.2570 3650 0.0004 -
0.2605 3700 0.0004 -
0.2640 3750 0.0004 -
0.2676 3800 0.0003 -
0.2711 3850 0.0004 -
0.2746 3900 0.0005 -
0.2781 3950 0.0004 -
0.2817 4000 0.0004 -
0.2852 4050 0.0003 -
0.2887 4100 0.0004 -
0.2922 4150 0.0004 -
0.2957 4200 0.0004 -
0.2993 4250 0.0005 -
0.3028 4300 0.0004 -
0.3063 4350 0.0004 -
0.3098 4400 0.0003 -
0.3133 4450 0.0004 -
0.3169 4500 0.0004 -
0.3204 4550 0.0003 -
0.3239 4600 0.0003 -
0.3274 4650 0.0004 -
0.3309 4700 0.0003 -
0.3345 4750 0.0003 -
0.3380 4800 0.0003 -
0.3415 4850 0.0003 -
0.3450 4900 0.0004 -
0.3485 4950 0.0003 -
0.3521 5000 0.0003 -
0.3556 5050 0.0003 -
0.3591 5100 0.0003 -
0.3626 5150 0.0004 -
0.3661 5200 0.0002 -
0.3697 5250 0.0004 -
0.3732 5300 0.0003 -
0.3767 5350 0.0003 -
0.3802 5400 0.0002 -
0.3837 5450 0.0003 -
0.3873 5500 0.0003 -
0.3908 5550 0.0003 -
0.3943 5600 0.0002 -
0.3978 5650 0.0003 -
0.4014 5700 0.0003 -
0.4049 5750 0.0002 -
0.4084 5800 0.0003 -
0.4119 5850 0.0003 -
0.4154 5900 0.0003 -
0.4190 5950 0.0002 -
0.4225 6000 0.0002 -
0.4260 6050 0.0002 -
0.4295 6100 0.0003 -
0.4330 6150 0.0003 -
0.4366 6200 0.0002 -
0.4401 6250 0.0003 -
0.4436 6300 0.0003 -
0.4471 6350 0.0002 -
0.4506 6400 0.0002 -
0.4542 6450 0.0002 -
0.4577 6500 0.0002 -
0.4612 6550 0.0002 -
0.4647 6600 0.0002 -
0.4682 6650 0.0002 -
0.4718 6700 0.0002 -
0.4753 6750 0.0003 -
0.4788 6800 0.0003 -
0.4823 6850 0.0002 -
0.4858 6900 0.0003 -
0.4894 6950 0.0002 -
0.4929 7000 0.0003 -
0.4964 7050 0.0002 -
0.4999 7100 0.0002 -
0.5035 7150 0.0002 -
0.5070 7200 0.0003 -
0.5105 7250 0.0002 -
0.5140 7300 0.0003 -
0.5175 7350 0.0004 -
0.5211 7400 0.0002 -
0.5246 7450 0.0002 -
0.5281 7500 0.0002 -
0.5316 7550 0.0002 -
0.5351 7600 0.0002 -
0.5387 7650 0.0002 -
0.5422 7700 0.0002 -
0.5457 7750 0.0002 -
0.5492 7800 0.0003 -
0.5527 7850 0.0002 -
0.5563 7900 0.0002 -
0.5598 7950 0.0002 -
0.5633 8000 0.0002 -
0.5668 8050 0.0002 -
0.5703 8100 0.0002 -
0.5739 8150 0.0002 -
0.5774 8200 0.0003 -
0.5809 8250 0.0002 -
0.5844 8300 0.0002 -
0.5879 8350 0.0002 -
0.5915 8400 0.0002 -
0.5950 8450 0.0001 -
0.5985 8500 0.0001 -
0.6020 8550 0.0001 -
0.6055 8600 0.0001 -
0.6091 8650 0.0002 -
0.6126 8700 0.0002 -
0.6161 8750 0.0002 -
0.6196 8800 0.0002 -
0.6232 8850 0.0002 -
0.6267 8900 0.0001 -
0.6302 8950 0.0001 -
0.6337 9000 0.0002 -
0.6372 9050 0.0002 -
0.6408 9100 0.0002 -
0.6443 9150 0.0001 -
0.6478 9200 0.0002 -
0.6513 9250 0.0003 -
0.6548 9300 0.0002 -
0.6584 9350 0.0003 -
0.6619 9400 0.0001 -
0.6654 9450 0.0001 -
0.6689 9500 0.0001 -
0.6724 9550 0.0001 -
0.6760 9600 0.0001 -
0.6795 9650 0.0002 -
0.6830 9700 0.0002 -
0.6865 9750 0.0002 -
0.6900 9800 0.0001 -
0.6936 9850 0.0001 -
0.6971 9900 0.0002 -
0.7006 9950 0.0001 -
0.7041 10000 0.0001 -
0.7076 10050 0.0001 -
0.7112 10100 0.0002 -
0.7147 10150 0.0001 -
0.7182 10200 0.0002 -
0.7217 10250 0.0002 -
0.7252 10300 0.0001 -
0.7288 10350 0.0001 -
0.7323 10400 0.0001 -
0.7358 10450 0.0001 -
0.7393 10500 0.0002 -
0.7429 10550 0.0001 -
0.7464 10600 0.0002 -
0.7499 10650 0.0001 -
0.7534 10700 0.0001 -
0.7569 10750 0.0002 -
0.7605 10800 0.0002 -
0.7640 10850 0.0001 -
0.7675 10900 0.0001 -
0.7710 10950 0.0001 -
0.7745 11000 0.0001 -
0.7781 11050 0.0001 -
0.7816 11100 0.0001 -
0.7851 11150 0.0001 -
0.7886 11200 0.0001 -
0.7921 11250 0.0001 -
0.7957 11300 0.0001 -
0.7992 11350 0.0001 -
0.8027 11400 0.0002 -
0.8062 11450 0.0001 -
0.8097 11500 0.0001 -
0.8133 11550 0.0001 -
0.8168 11600 0.0001 -
0.8203 11650 0.0001 -
0.8238 11700 0.0001 -
0.8273 11750 0.0001 -
0.8309 11800 0.0001 -
0.8344 11850 0.0001 -
0.8379 11900 0.0001 -
0.8414 11950 0.0001 -
0.8450 12000 0.0001 -
0.8485 12050 0.0001 -
0.8520 12100 0.0001 -
0.8555 12150 0.0001 -
0.8590 12200 0.0001 -
0.8626 12250 0.0002 -
0.8661 12300 0.0002 -
0.8696 12350 0.0002 -
0.8731 12400 0.0002 -
0.8766 12450 0.0001 -
0.8802 12500 0.0001 -
0.8837 12550 0.0001 -
0.8872 12600 0.0001 -
0.8907 12650 0.0002 -
0.8942 12700 0.0001 -
0.8978 12750 0.0001 -
0.9013 12800 0.0001 -
0.9048 12850 0.0001 -
0.9083 12900 0.0001 -
0.9118 12950 0.0001 -
0.9154 13000 0.0001 -
0.9189 13050 0.0001 -
0.9224 13100 0.0001 -
0.9259 13150 0.0001 -
0.9294 13200 0.0001 -
0.9330 13250 0.0001 -
0.9365 13300 0.0001 -
0.9400 13350 0.0001 -
0.9435 13400 0.0001 -
0.9470 13450 0.0001 -
0.9506 13500 0.0001 -
0.9541 13550 0.0001 -
0.9576 13600 0.0001 -
0.9611 13650 0.0001 -
0.9647 13700 0.0001 -
0.9682 13750 0.0001 -
0.9717 13800 0.0001 -
0.9752 13850 0.0001 -
0.9787 13900 0.0001 -
0.9823 13950 0.0001 -
0.9858 14000 0.0001 -
0.9893 14050 0.0001 -
0.9928 14100 0.0001 -
0.9963 14150 0.0002 -
0.9999 14200 0.0001 -
1.0 14202 - 0.0082
1.0034 14250 0.0001 -
1.0069 14300 0.0001 -
1.0104 14350 0.0001 -
1.0139 14400 0.0001 -
1.0175 14450 0.0001 -
1.0210 14500 0.0001 -
1.0245 14550 0.0001 -
1.0280 14600 0.0001 -
1.0315 14650 0.0001 -
1.0351 14700 0.0001 -
1.0386 14750 0.0001 -
1.0421 14800 0.0001 -
1.0456 14850 0.0001 -
1.0491 14900 0.0001 -
1.0527 14950 0.0001 -
1.0562 15000 0.0001 -
1.0597 15050 0.0001 -
1.0632 15100 0.0001 -
1.0668 15150 0.0001 -
1.0703 15200 0.0001 -
1.0738 15250 0.0001 -
1.0773 15300 0.0001 -
1.0808 15350 0.0001 -
1.0844 15400 0.0001 -
1.0879 15450 0.0001 -
1.0914 15500 0.0001 -
1.0949 15550 0.0001 -
1.0984 15600 0.0001 -
1.1020 15650 0.0001 -
1.1055 15700 0.0001 -
1.1090 15750 0.0001 -
1.1125 15800 0.0001 -
1.1160 15850 0.0001 -
1.1196 15900 0.0001 -
1.1231 15950 0.0001 -
1.1266 16000 0.0001 -
1.1301 16050 0.0001 -
1.1336 16100 0.0001 -
1.1372 16150 0.0001 -
1.1407 16200 0.0001 -
1.1442 16250 0.0001 -
1.1477 16300 0.0001 -
1.1512 16350 0.0001 -
1.1548 16400 0.0001 -
1.1583 16450 0.0001 -
1.1618 16500 0.0001 -
1.1653 16550 0.0001 -
1.1688 16600 0.0001 -
1.1724 16650 0.0001 -
1.1759 16700 0.0001 -
1.1794 16750 0.0001 -
1.1829 16800 0.0001 -
1.1865 16850 0.0001 -
1.1900 16900 0.0001 -
1.1935 16950 0.0001 -
1.1970 17000 0.0001 -
1.2005 17050 0.0001 -
1.2041 17100 0.0001 -
1.2076 17150 0.0001 -
1.2111 17200 0.0001 -
1.2146 17250 0.0001 -
1.2181 17300 0.0001 -
1.2217 17350 0.0001 -
1.2252 17400 0.0001 -
1.2287 17450 0.0001 -
1.2322 17500 0.0001 -
1.2357 17550 0.0001 -
1.2393 17600 0.0001 -
1.2428 17650 0.0001 -
1.2463 17700 0.0001 -
1.2498 17750 0.0001 -
1.2533 17800 0.0001 -
1.2569 17850 0.0001 -
1.2604 17900 0.0001 -
1.2639 17950 0.0001 -
1.2674 18000 0.0001 -
1.2709 18050 0.0001 -
1.2745 18100 0.0001 -
1.2780 18150 0.0001 -
1.2815 18200 0.0001 -
1.2850 18250 0.0001 -
1.2886 18300 0.0001 -
1.2921 18350 0.0001 -
1.2956 18400 0.0001 -
1.2991 18450 0.0001 -
1.3026 18500 0.0001 -
1.3062 18550 0.0001 -
1.3097 18600 0.0001 -
1.3132 18650 0.0001 -
1.3167 18700 0.0001 -
1.3202 18750 0.0001 -
1.3238 18800 0.0001 -
1.3273 18850 0.0001 -
1.3308 18900 0.0001 -
1.3343 18950 0.0001 -
1.3378 19000 0.0001 -
1.3414 19050 0.0001 -
1.3449 19100 0.0001 -
1.3484 19150 0.0001 -
1.3519 19200 0.0001 -
1.3554 19250 0.0001 -
1.3590 19300 0.0001 -
1.3625 19350 0.0001 -
1.3660 19400 0.0001 -
1.3695 19450 0.0001 -
1.3730 19500 0.0001 -
1.3766 19550 0.0001 -
1.3801 19600 0.0001 -
1.3836 19650 0.0001 -
1.3871 19700 0.0001 -
1.3906 19750 0.0001 -
1.3942 19800 0.0001 -
1.3977 19850 0.0001 -
1.4012 19900 0.0001 -
1.4047 19950 0.0001 -
1.4083 20000 0.0001 -
1.4118 20050 0.0001 -
1.4153 20100 0.0001 -
1.4188 20150 0.0001 -
1.4223 20200 0.0001 -
1.4259 20250 0.0001 -
1.4294 20300 0.0001 -
1.4329 20350 0.0001 -
1.4364 20400 0.0 -
1.4399 20450 0.0001 -
1.4435 20500 0.0001 -
1.4470 20550 0.0001 -
1.4505 20600 0.0001 -
1.4540 20650 0.0001 -
1.4575 20700 0.0001 -
1.4611 20750 0.0001 -
1.4646 20800 0.0001 -
1.4681 20850 0.0 -
1.4716 20900 0.0001 -
1.4751 20950 0.0001 -
1.4787 21000 0.0 -
1.4822 21050 0.0001 -
1.4857 21100 0.0001 -
1.4892 21150 0.0001 -
1.4927 21200 0.0001 -
1.4963 21250 0.0001 -
1.4998 21300 0.0001 -
1.5033 21350 0.0 -
1.5068 21400 0.0001 -
1.5104 21450 0.0001 -
1.5139 21500 0.0001 -
1.5174 21550 0.0 -
1.5209 21600 0.0001 -
1.5244 21650 0.0001 -
1.5280 21700 0.0001 -
1.5315 21750 0.0001 -
1.5350 21800 0.0001 -
1.5385 21850 0.0001 -
1.5420 21900 0.0 -
1.5456 21950 0.0001 -
1.5491 22000 0.0001 -
1.5526 22050 0.0001 -
1.5561 22100 0.0001 -
1.5596 22150 0.0001 -
1.5632 22200 0.0001 -
1.5667 22250 0.0 -
1.5702 22300 0.0 -
1.5737 22350 0.0001 -
1.5772 22400 0.0001 -
1.5808 22450 0.0001 -
1.5843 22500 0.0001 -
1.5878 22550 0.0001 -
1.5913 22600 0.0001 -
1.5948 22650 0.0001 -
1.5984 22700 0.0 -
1.6019 22750 0.0001 -
1.6054 22800 0.0001 -
1.6089 22850 0.0001 -
1.6124 22900 0.0001 -
1.6160 22950 0.0001 -
1.6195 23000 0.0001 -
1.6230 23050 0.0001 -
1.6265 23100 0.0001 -
1.6301 23150 0.0 -
1.6336 23200 0.0001 -
1.6371 23250 0.0001 -
1.6406 23300 0.0 -
1.6441 23350 0.0001 -
1.6477 23400 0.0 -
1.6512 23450 0.0001 -
1.6547 23500 0.0 -
1.6582 23550 0.0001 -
1.6617 23600 0.0001 -
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1.6723 23750 0.0001 -
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1.6793 23850 0.0 -
1.6829 23900 0.0001 -
1.6864 23950 0.0 -
1.6899 24000 0.0 -
1.6934 24050 0.0 -
1.6969 24100 0.0001 -
1.7005 24150 0.0001 -
1.7040 24200 0.0001 -
1.7075 24250 0.0001 -
1.7110 24300 0.0001 -
1.7145 24350 0.0001 -
1.7181 24400 0.0001 -
1.7216 24450 0.0 -
1.7251 24500 0.0001 -
1.7286 24550 0.0 -
1.7322 24600 0.0001 -
1.7357 24650 0.0001 -
1.7392 24700 0.0 -
1.7427 24750 0.0001 -
1.7462 24800 0.0001 -
1.7498 24850 0.0001 -
1.7533 24900 0.0 -
1.7568 24950 0.0 -
1.7603 25000 0.0001 -
1.7638 25050 0.0001 -
1.7674 25100 0.0001 -
1.7709 25150 0.0001 -
1.7744 25200 0.0 -
1.7779 25250 0.0001 -
1.7814 25300 0.0 -
1.7850 25350 0.0 -
1.7885 25400 0.0 -
1.7920 25450 0.0 -
1.7955 25500 0.0 -
1.7990 25550 0.0 -
1.8026 25600 0.0001 -
1.8061 25650 0.0 -
1.8096 25700 0.0001 -
1.8131 25750 0.0001 -
1.8166 25800 0.0 -
1.8202 25850 0.0 -
1.8237 25900 0.0 -
1.8272 25950 0.0 -
1.8307 26000 0.0001 -
1.8342 26050 0.0 -
1.8378 26100 0.0 -
1.8413 26150 0.0 -
1.8448 26200 0.0 -
1.8483 26250 0.0 -
1.8519 26300 0.0 -
1.8554 26350 0.0001 -
1.8589 26400 0.0 -
1.8624 26450 0.0 -
1.8659 26500 0.0 -
1.8695 26550 0.0 -
1.8730 26600 0.0 -
1.8765 26650 0.0 -
1.8800 26700 0.0 -
1.8835 26750 0.0001 -
1.8871 26800 0.0 -
1.8906 26850 0.0 -
1.8941 26900 0.0 -
1.8976 26950 0.0 -
1.9011 27000 0.0001 -
1.9047 27050 0.0 -
1.9082 27100 0.0 -
1.9117 27150 0.0 -
1.9152 27200 0.0001 -
1.9187 27250 0.0 -
1.9223 27300 0.0001 -
1.9258 27350 0.0 -
1.9293 27400 0.0 -
1.9328 27450 0.0 -
1.9363 27500 0.0 -
1.9399 27550 0.0 -
1.9434 27600 0.0 -
1.9469 27650 0.0 -
1.9504 27700 0.0 -
1.9540 27750 0.0001 -
1.9575 27800 0.0 -
1.9610 27850 0.0 -
1.9645 27900 0.0 -
1.9680 27950 0.0001 -
1.9716 28000 0.0 -
1.9751 28050 0.0 -
1.9786 28100 0.0001 -
1.9821 28150 0.0 -
1.9856 28200 0.0 -
1.9892 28250 0.0 -
1.9927 28300 0.0 -
1.9962 28350 0.0 -
1.9997 28400 0.0001 -
2.0 28404 - 0.0076
  • 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 

@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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