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nazhan
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bge-small-en-v1.5-brahmaputra-iter-10-2nd

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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SetFit with BAAI/bge-small-en-v1.5

This is a SetFit model that can be used for Text Classification. This SetFit model uses BAAI/bge-small-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
  • "Show me the products with 'Tablet' in the name and filter by price above 200."
  • 'Can you get me the products with a price above 100?'
  • 'Filter by employees with a salary above 60,000 and show me their first names.'
Aggregation
  • 'What’s the total revenue generated by each employee in 2023?'
  • 'Get me data_asset_001_pcc group by category.'
  • 'Show me max revenue'
Tablejoin
  • 'Show me a merge of key performance metrics and cash flow.'
  • 'How can I integrate the Customers and Orders tables to identify customers with multiple recent orders?'
  • 'Can you integrate data from the Products and Orders tables to determine the revenue generated by each product?'
Viewtables
  • 'How can I view all of the tables stored within the starhub_data_asset database?'
  • 'What are the tables that I can access in the starhub_data_asset database?'
  • 'What are the available tables that are relevant to pricing strategies within starhub_data_asset database?'
Lookup_1
  • 'Display data_asset_kpi_cf.'
  • 'Get me data_asset_001_ta trend history.'
  • 'Show me data_asset_kpi_cf details.'
Rejection
  • "I don't want to apply any filters now."
  • "I don't want to apply any filters to this."
  • "I'd prefer not to apply any filters."
Generalreply
  • "What's your favorite TV show of all time?"
  • "i'll start dinner at 6:00."
  • "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!"

Evaluation

Metrics

Label Accuracy
all 0.9915

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-small-en-v1.5-brahmaputra-iter-10-2nd")
# Run inference
preds = model("Can I have data_asset_kpi_bs details.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 8.8375 62
Label Training Sample Count
Tablejoin 122
Rejection 69
Aggregation 287
Lookup 59
Generalreply 71
Viewtables 79
Lookup_1 156

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 0.2355 -
0.0014 50 0.2202 -
0.0028 100 0.1664 -
0.0042 150 0.216 -
0.0056 200 0.2341 -
0.0070 250 0.2279 -
0.0084 300 0.1786 -
0.0098 350 0.1603 -
0.0112 400 0.0821 -
0.0126 450 0.1498 -
0.0140 500 0.0942 -
0.0155 550 0.0999 -
0.0169 600 0.0895 -
0.0183 650 0.0841 -
0.0197 700 0.1433 -
0.0211 750 0.0808 -
0.0225 800 0.0346 -
0.0239 850 0.0556 -
0.0253 900 0.0755 -
0.0267 950 0.0346 -
0.0281 1000 0.0486 -
0.0295 1050 0.0207 -
0.0309 1100 0.0126 -
0.0323 1150 0.0113 -
0.0337 1200 0.0076 -
0.0351 1250 0.0082 -
0.0365 1300 0.0142 -
0.0379 1350 0.011 -
0.0393 1400 0.0034 -
0.0407 1450 0.0123 -
0.0421 1500 0.0062 -
0.0435 1550 0.0021 -
0.0449 1600 0.005 -
0.0464 1650 0.0124 -
0.0478 1700 0.0026 -
0.0492 1750 0.0029 -
0.0506 1800 0.0023 -
0.0520 1850 0.0017 -
0.0534 1900 0.0027 -
0.0548 1950 0.0017 -
0.0562 2000 0.0043 -
0.0576 2050 0.0018 -
0.0590 2100 0.0032 -
0.0604 2150 0.0022 -
0.0618 2200 0.0052 -
0.0632 2250 0.0025 -
0.0646 2300 0.0018 -
0.0660 2350 0.0016 -
0.0674 2400 0.0016 -
0.0688 2450 0.001 -
0.0702 2500 0.0015 -
0.0716 2550 0.0013 -
0.0730 2600 0.0012 -
0.0744 2650 0.0012 -
0.0759 2700 0.0017 -
0.0773 2750 0.0016 -
0.0787 2800 0.0018 -
0.0801 2850 0.0007 -
0.0815 2900 0.0008 -
0.0829 2950 0.0016 -
0.0843 3000 0.0008 -
0.0857 3050 0.0011 -
0.0871 3100 0.0013 -
0.0885 3150 0.0012 -
0.0899 3200 0.0006 -
0.0913 3250 0.0012 -
0.0927 3300 0.0009 -
0.0941 3350 0.0007 -
0.0955 3400 0.0006 -
0.0969 3450 0.0011 -
0.0983 3500 0.0012 -
0.0997 3550 0.0008 -
0.1011 3600 0.0009 -
0.1025 3650 0.0007 -
0.1039 3700 0.001 -
0.1053 3750 0.0006 -
0.1068 3800 0.0008 -
0.1082 3850 0.0007 -
0.1096 3900 0.0008 -
0.1110 3950 0.0006 -
0.1124 4000 0.0004 -
0.1138 4050 0.001 -
0.1152 4100 0.001 -
0.1166 4150 0.0007 -
0.1180 4200 0.0006 -
0.1194 4250 0.0006 -
0.1208 4300 0.0004 -
0.1222 4350 0.0008 -
0.1236 4400 0.0005 -
0.1250 4450 0.0007 -
0.1264 4500 0.0007 -
0.1278 4550 0.001 -
0.1292 4600 0.0007 -
0.1306 4650 0.0005 -
0.1320 4700 0.0006 -
0.1334 4750 0.0007 -
0.1348 4800 0.0003 -
0.1363 4850 0.0009 -
0.1377 4900 0.0008 -
0.1391 4950 0.0005 -
0.1405 5000 0.0005 -
0.1419 5050 0.0005 -
0.1433 5100 0.0005 -
0.1447 5150 0.0004 -
0.1461 5200 0.0005 -
0.1475 5250 0.0006 -
0.1489 5300 0.0007 -
0.1503 5350 0.0004 -
0.1517 5400 0.0007 -
0.1531 5450 0.0006 -
0.1545 5500 0.0006 -
0.1559 5550 0.0005 -
0.1573 5600 0.0005 -
0.1587 5650 0.0005 -
0.1601 5700 0.0007 -
0.1615 5750 0.0007 -
0.1629 5800 0.0004 -
0.1643 5850 0.0007 -
0.1657 5900 0.0006 -
0.1672 5950 0.0005 -
0.1686 6000 0.0005 -
0.1700 6050 0.0004 -
0.1714 6100 0.0005 -
0.1728 6150 0.0005 -
0.1742 6200 0.0004 -
0.1756 6250 0.0006 -
0.1770 6300 0.0004 -
0.1784 6350 0.0004 -
0.1798 6400 0.0004 -
0.1812 6450 0.0005 -
0.1826 6500 0.0005 -
0.1840 6550 0.0004 -
0.1854 6600 0.0003 -
0.1868 6650 0.0004 -
0.1882 6700 0.0004 -
0.1896 6750 0.0004 -
0.1910 6800 0.0006 -
0.1924 6850 0.0004 -
0.1938 6900 0.0004 -
0.1952 6950 0.0003 -
0.1967 7000 0.0004 -
0.1981 7050 0.0004 -
0.1995 7100 0.0003 -
0.2009 7150 0.0006 -
0.2023 7200 0.0005 -
0.2037 7250 0.0005 -
0.2051 7300 0.0003 -
0.2065 7350 0.0003 -
0.2079 7400 0.0004 -
0.2093 7450 0.0006 -
0.2107 7500 0.0004 -
0.2121 7550 0.0003 -
0.2135 7600 0.0005 -
0.2149 7650 0.0005 -
0.2163 7700 0.0005 -
0.2177 7750 0.0003 -
0.2191 7800 0.0004 -
0.2205 7850 0.0003 -
0.2219 7900 0.0004 -
0.2233 7950 0.0003 -
0.2247 8000 0.0003 -
0.2261 8050 0.0008 -
0.2276 8100 0.0003 -
0.2290 8150 0.0003 -
0.2304 8200 0.0003 -
0.2318 8250 0.0003 -
0.2332 8300 0.0004 -
0.2346 8350 0.0003 -
0.2360 8400 0.0002 -
0.2374 8450 0.0005 -
0.2388 8500 0.0003 -
0.2402 8550 0.0002 -
0.2416 8600 0.0005 -
0.2430 8650 0.0005 -
0.2444 8700 0.0005 -
0.2458 8750 0.0002 -
0.2472 8800 0.0004 -
0.2486 8850 0.0003 -
0.2500 8900 0.0002 -
0.2514 8950 0.0003 -
0.2528 9000 0.0003 -
0.2542 9050 0.0002 -
0.2556 9100 0.0003 -
0.2571 9150 0.0003 -
0.2585 9200 0.0005 -
0.2599 9250 0.0004 -
0.2613 9300 0.0002 -
0.2627 9350 0.0002 -
0.2641 9400 0.0003 -
0.2655 9450 0.0003 -
0.2669 9500 0.0003 -
0.2683 9550 0.0002 -
0.2697 9600 0.0003 -
0.2711 9650 0.0003 -
0.2725 9700 0.0003 -
0.2739 9750 0.0006 -
0.2753 9800 0.0003 -
0.2767 9850 0.0002 -
0.2781 9900 0.0003 -
0.2795 9950 0.0004 -
0.2809 10000 0.0005 -
0.2823 10050 0.0003 -
0.2837 10100 0.0003 -
0.2851 10150 0.0003 -
0.2865 10200 0.0004 -
0.2880 10250 0.0004 -
0.2894 10300 0.0003 -
0.2908 10350 0.0003 -
0.2922 10400 0.0003 -
0.2936 10450 0.0002 -
0.2950 10500 0.0003 -
0.2964 10550 0.0002 -
0.2978 10600 0.0003 -
0.2992 10650 0.0003 -
0.3006 10700 0.0003 -
0.3020 10750 0.0003 -
0.3034 10800 0.0003 -
0.3048 10850 0.0004 -
0.3062 10900 0.0003 -
0.3076 10950 0.0002 -
0.3090 11000 0.0003 -
0.3104 11050 0.0002 -
0.3118 11100 0.0003 -
0.3132 11150 0.0002 -
0.3146 11200 0.0003 -
0.3160 11250 0.0004 -
0.3175 11300 0.0003 -
0.3189 11350 0.0003 -
0.3203 11400 0.0003 -
0.3217 11450 0.0001 -
0.3231 11500 0.0002 -
0.3245 11550 0.0003 -
0.3259 11600 0.0003 -
0.3273 11650 0.0002 -
0.3287 11700 0.0004 -
0.3301 11750 0.0003 -
0.3315 11800 0.0002 -
0.3329 11850 0.0003 -
0.3343 11900 0.0003 -
0.3357 11950 0.0003 -
0.3371 12000 0.0003 -
0.3385 12050 0.0002 -
0.3399 12100 0.0002 -
0.3413 12150 0.0002 -
0.3427 12200 0.0002 -
0.3441 12250 0.0003 -
0.3455 12300 0.0003 -
0.3469 12350 0.0003 -
0.3484 12400 0.0003 -
0.3498 12450 0.0002 -
0.3512 12500 0.0003 -
0.3526 12550 0.0002 -
0.3540 12600 0.0004 -
0.3554 12650 0.0003 -
0.3568 12700 0.0003 -
0.3582 12750 0.0003 -
0.3596 12800 0.0002 -
0.3610 12850 0.0002 -
0.3624 12900 0.0003 -
0.3638 12950 0.0002 -
0.3652 13000 0.0003 -
0.3666 13050 0.0002 -
0.3680 13100 0.0003 -
0.3694 13150 0.0003 -
0.3708 13200 0.0003 -
0.3722 13250 0.0002 -
0.3736 13300 0.0002 -
0.3750 13350 0.0003 -
0.3764 13400 0.0002 -
0.3779 13450 0.0004 -
0.3793 13500 0.0003 -
0.3807 13550 0.0002 -
0.3821 13600 0.0003 -
0.3835 13650 0.0002 -
0.3849 13700 0.0003 -
0.3863 13750 0.0003 -
0.3877 13800 0.0003 -
0.3891 13850 0.0002 -
0.3905 13900 0.0003 -
0.3919 13950 0.0002 -
0.3933 14000 0.0003 -
0.3947 14050 0.0004 -
0.3961 14100 0.0003 -
0.3975 14150 0.0003 -
0.3989 14200 0.0003 -
0.4003 14250 0.0002 -
0.4017 14300 0.0003 -
0.4031 14350 0.0002 -
0.4045 14400 0.0003 -
0.4059 14450 0.0002 -
0.4073 14500 0.0002 -
0.4088 14550 0.0002 -
0.4102 14600 0.0002 -
0.4116 14650 0.0002 -
0.4130 14700 0.0002 -
0.4144 14750 0.0004 -
0.4158 14800 0.0002 -
0.4172 14850 0.0002 -
0.4186 14900 0.0002 -
0.4200 14950 0.0002 -
0.4214 15000 0.0003 -
0.4228 15050 0.0002 -
0.4242 15100 0.0003 -
0.4256 15150 0.0002 -
0.4270 15200 0.0003 -
0.4284 15250 0.0003 -
0.4298 15300 0.0003 -
0.4312 15350 0.0013 -
0.4326 15400 0.0002 -
0.4340 15450 0.0002 -
0.4354 15500 0.0003 -
0.4368 15550 0.0003 -
0.4383 15600 0.0002 -
0.4397 15650 0.0002 -
0.4411 15700 0.0002 -
0.4425 15750 0.0002 -
0.4439 15800 0.0003 -
0.4453 15850 0.0001 -
0.4467 15900 0.0003 -
0.4481 15950 0.0002 -
0.4495 16000 0.0001 -
0.4509 16050 0.0003 -
0.4523 16100 0.0003 -
0.4537 16150 0.0003 -
0.4551 16200 0.0002 -
0.4565 16250 0.0001 -
0.4579 16300 0.0001 -
0.4593 16350 0.0001 -
0.4607 16400 0.0003 -
0.4621 16450 0.0002 -
0.4635 16500 0.0002 -
0.4649 16550 0.0002 -
0.4663 16600 0.0003 -
0.4677 16650 0.0002 -
0.4692 16700 0.0003 -
0.4706 16750 0.0002 -
0.4720 16800 0.0002 -
0.4734 16850 0.0002 -
0.4748 16900 0.0002 -
0.4762 16950 0.0003 -
0.4776 17000 0.0002 -
0.4790 17050 0.0002 -
0.4804 17100 0.0003 -
0.4818 17150 0.0001 -
0.4832 17200 0.0002 -
0.4846 17250 0.0002 -
0.4860 17300 0.0002 -
0.4874 17350 0.0001 -
0.4888 17400 0.0002 -
0.4902 17450 0.0002 -
0.4916 17500 0.0002 -
0.4930 17550 0.0002 -
0.4944 17600 0.0002 -
0.4958 17650 0.0003 -
0.4972 17700 0.0003 -
0.4987 17750 0.0002 -
0.5001 17800 0.0001 -
0.5015 17850 0.0002 -
0.5029 17900 0.0003 -
0.5043 17950 0.0002 -
0.5057 18000 0.0001 -
0.5071 18050 0.0003 -
0.5085 18100 0.0004 -
0.5099 18150 0.0002 -
0.5113 18200 0.0002 -
0.5127 18250 0.0002 -
0.5141 18300 0.0002 -
0.5155 18350 0.0002 -
0.5169 18400 0.0001 -
0.5183 18450 0.0001 -
0.5197 18500 0.0002 -
0.5211 18550 0.0002 -
0.5225 18600 0.0618 -
0.5239 18650 0.0003 -
0.5253 18700 0.0003 -
0.5267 18750 0.0002 -
0.5281 18800 0.0002 -
0.5296 18850 0.0002 -
0.5310 18900 0.0001 -
0.5324 18950 0.0002 -
0.5338 19000 0.0002 -
0.5352 19050 0.0003 -
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0.5380 19150 0.0002 -
0.5394 19200 0.0001 -
0.5408 19250 0.0003 -
0.5422 19300 0.0003 -
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0.8259 29400 0.0001 -
0.8273 29450 0.0002 -
0.8287 29500 0.0001 -
0.8301 29550 0.0002 -
0.8316 29600 0.0001 -
0.8330 29650 0.0001 -
0.8344 29700 0.0001 -
0.8358 29750 0.0001 -
0.8372 29800 0.0001 -
0.8386 29850 0.0001 -
0.8400 29900 0.0001 -
0.8414 29950 0.0002 -
0.8428 30000 0.0002 -
0.8442 30050 0.0001 -
0.8456 30100 0.0001 -
0.8470 30150 0.0001 -
0.8484 30200 0.0001 -
0.8498 30250 0.0001 -
0.8512 30300 0.0001 -
0.8526 30350 0.0001 -
0.8540 30400 0.0001 -
0.8554 30450 0.0002 -
0.8568 30500 0.0001 -
0.8582 30550 0.0001 -
0.8596 30600 0.0 -
0.8611 30650 0.0001 -
0.8625 30700 0.0002 -
0.8639 30750 0.0002 -
0.8653 30800 0.0002 -
0.8667 30850 0.0001 -
0.8681 30900 0.0002 -
0.8695 30950 0.0001 -
0.8709 31000 0.0001 -
0.8723 31050 0.0001 -
0.8737 31100 0.0002 -
0.8751 31150 0.0002 -
0.8765 31200 0.0001 -
0.8779 31250 0.0001 -
0.8793 31300 0.0001 -
0.8807 31350 0.0001 -
0.8821 31400 0.0001 -
0.8835 31450 0.0001 -
0.8849 31500 0.0001 -
0.8863 31550 0.0002 -
0.8877 31600 0.0001 -
0.8891 31650 0.0001 -
0.8905 31700 0.0002 -
0.8920 31750 0.0001 -
0.8934 31800 0.0001 -
0.8948 31850 0.0001 -
0.8962 31900 0.0003 -
0.8976 31950 0.0002 -
0.8990 32000 0.0002 -
0.9004 32050 0.0001 -
0.9018 32100 0.0001 -
0.9032 32150 0.0002 -
0.9046 32200 0.0003 -
0.9060 32250 0.0001 -
0.9074 32300 0.0002 -
0.9088 32350 0.0001 -
0.9102 32400 0.0002 -
0.9116 32450 0.0002 -
0.9130 32500 0.0001 -
0.9144 32550 0.0001 -
0.9158 32600 0.0001 -
0.9172 32650 0.0001 -
0.9186 32700 0.0001 -
0.9200 32750 0.0001 -
0.9215 32800 0.0001 -
0.9229 32850 0.0001 -
0.9243 32900 0.0001 -
0.9257 32950 0.0001 -
0.9271 33000 0.0001 -
0.9285 33050 0.0002 -
0.9299 33100 0.0001 -
0.9313 33150 0.0002 -
0.9327 33200 0.0001 -
0.9341 33250 0.0001 -
0.9355 33300 0.0002 -
0.9369 33350 0.0001 -
0.9383 33400 0.0001 -
0.9397 33450 0.0001 -
0.9411 33500 0.0001 -
0.9425 33550 0.0001 -
0.9439 33600 0.0001 -
0.9453 33650 0.0001 -
0.9467 33700 0.0002 -
0.9481 33750 0.0001 -
0.9495 33800 0.0001 -
0.9509 33850 0.0002 -
0.9524 33900 0.0001 -
0.9538 33950 0.0001 -
0.9552 34000 0.0002 -
0.9566 34050 0.0001 -
0.9580 34100 0.0001 -
0.9594 34150 0.0001 -
0.9608 34200 0.0002 -
0.9622 34250 0.0001 -
0.9636 34300 0.0001 -
0.9650 34350 0.0001 -
0.9664 34400 0.0001 -
0.9678 34450 0.0003 -
0.9692 34500 0.0001 -
0.9706 34550 0.0001 -
0.9720 34600 0.0001 -
0.9734 34650 0.0001 -
0.9748 34700 0.0001 -
0.9762 34750 0.0001 -
0.9776 34800 0.0002 -
0.9790 34850 0.0001 -
0.9804 34900 0.0002 -
0.9819 34950 0.0001 -
0.9833 35000 0.0002 -
0.9847 35050 0.0001 -
0.9861 35100 0.0001 -
0.9875 35150 0.0001 -
0.9889 35200 0.0001 -
0.9903 35250 0.0001 -
0.9917 35300 0.0001 -
0.9931 35350 0.0001 -
0.9945 35400 0.0001 -
0.9959 35450 0.0001 -
0.9973 35500 0.0001 -
0.9987 35550 0.0001 -
1.0 35596 - 0.0121
  • 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 

@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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