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nazhan
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bge-large-en-v1.5-brahmaputra-iter-9-2nd-1-epoch

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

This is a SetFit model 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.'
  • 'Analyze Product-wise Financial Performance Metrics.'
  • 'Get M&A deal size by company.'
Aggregation
  • 'Group the products by color and find the average price for each color.'
  • 'Get me count Product.'
  • 'Show me forecast accuracy and group by version.'
Lookup
  • 'What are the products with a price below 20?'
  • 'Can you get me the products that are out of stock?'
  • 'Get me the list of employees who joined the company after January 2023.'
Viewtables
  • 'What are the different types of tables that can be found within the starhub_data_asset database?'
  • 'What is the complete list of tables in the starhub_data_asset database that can be accessed without needing to perform any table joining operations?'
  • 'What is the list of tables that a new user should familiarize themselves with when accessing the starhub_data_asset database?'
Tablejoin
  • 'Can you join the Products and Orders tables to track revenue by product category?'
  • 'Could you combine table data from Orders and Products to identify which products were ordered most frequently?'
  • 'Show me a join of key performance metrics and cash flow tables.'
Generalreply
  • "Oh, I'm a big fan of indie rock. What about you? What's your favorite type of music?"
  • 'It was pretty good! How about yours?'
  • "Oh, that's a tough question! I have a few favorites, but if I had to pick just one, it would be The Shawshank Redemption. What about you, what's your favorite movie?"
Rejection
  • "I don't need to filter this data set."
  • "Let's not generate more data entries."
  • "Please don't filter the list."

Evaluation

Metrics

Label Accuracy
all 0.9829

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-9-2nd-1-epoch")
# Run inference
preds = model("you're very lucky.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 8.8397 53
Label Training Sample Count
Tablejoin 129
Rejection 69
Aggregation 282
Lookup 64
Generalreply 69
Viewtables 76
Lookup_1 147

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 0.23 -
0.0014 50 0.196 -
0.0028 100 0.1679 -
0.0043 150 0.156 -
0.0057 200 0.2 -
0.0071 250 0.0765 -
0.0085 300 0.167 -
0.0100 350 0.1154 -
0.0114 400 0.0625 -
0.0128 450 0.0666 -
0.0142 500 0.0515 -
0.0157 550 0.0178 -
0.0171 600 0.0068 -
0.0185 650 0.0174 -
0.0199 700 0.0136 -
0.0214 750 0.0066 -
0.0228 800 0.0052 -
0.0242 850 0.0045 -
0.0256 900 0.003 -
0.0271 950 0.0031 -
0.0285 1000 0.0035 -
0.0299 1050 0.0032 -
0.0313 1100 0.0031 -
0.0328 1150 0.0029 -
0.0342 1200 0.0023 -
0.0356 1250 0.0012 -
0.0370 1300 0.0025 -
0.0385 1350 0.0019 -
0.0399 1400 0.0023 -
0.0413 1450 0.0016 -
0.0427 1500 0.0018 -
0.0441 1550 0.0019 -
0.0456 1600 0.0012 -
0.0470 1650 0.0012 -
0.0484 1700 0.0013 -
0.0498 1750 0.0011 -
0.0513 1800 0.001 -
0.0527 1850 0.0013 -
0.0541 1900 0.0014 -
0.0555 1950 0.0008 -
0.0570 2000 0.0009 -
0.0584 2050 0.0009 -
0.0598 2100 0.0009 -
0.0612 2150 0.0012 -
0.0627 2200 0.0008 -
0.0641 2250 0.0011 -
0.0655 2300 0.0006 -
0.0669 2350 0.0011 -
0.0684 2400 0.0007 -
0.0698 2450 0.0009 -
0.0712 2500 0.0007 -
0.0726 2550 0.0005 -
0.0741 2600 0.0006 -
0.0755 2650 0.0007 -
0.0769 2700 0.0008 -
0.0783 2750 0.0007 -
0.0798 2800 0.0007 -
0.0812 2850 0.0007 -
0.0826 2900 0.0008 -
0.0840 2950 0.0006 -
0.0855 3000 0.0006 -
0.0869 3050 0.0006 -
0.0883 3100 0.0005 -
0.0897 3150 0.0007 -
0.0911 3200 0.0005 -
0.0926 3250 0.0007 -
0.0940 3300 0.0007 -
0.0954 3350 0.0006 -
0.0968 3400 0.0007 -
0.0983 3450 0.0005 -
0.0997 3500 0.0005 -
0.1011 3550 0.0005 -
0.1025 3600 0.0004 -
0.1040 3650 0.0003 -
0.1054 3700 0.0005 -
0.1068 3750 0.0004 -
0.1082 3800 0.0005 -
0.1097 3850 0.0004 -
0.1111 3900 0.0004 -
0.1125 3950 0.0003 -
0.1139 4000 0.0004 -
0.1154 4050 0.0003 -
0.1168 4100 0.1163 -
0.1182 4150 0.0054 -
0.1196 4200 0.0317 -
0.1211 4250 0.0009 -
0.1225 4300 0.0005 -
0.1239 4350 0.0008 -
0.1253 4400 0.0007 -
0.1268 4450 0.0004 -
0.1282 4500 0.0006 -
0.1296 4550 0.0004 -
0.1310 4600 0.0003 -
0.1324 4650 0.0004 -
0.1339 4700 0.0005 -
0.1353 4750 0.0003 -
0.1367 4800 0.0004 -
0.1381 4850 0.0004 -
0.1396 4900 0.0002 -
0.1410 4950 0.0005 -
0.1424 5000 0.0003 -
0.1438 5050 0.0004 -
0.1453 5100 0.0004 -
0.1467 5150 0.0003 -
0.1481 5200 0.0003 -
0.1495 5250 0.0003 -
0.1510 5300 0.0005 -
0.1524 5350 0.0004 -
0.1538 5400 0.0002 -
0.1552 5450 0.0003 -
0.1567 5500 0.0003 -
0.1581 5550 0.0002 -
0.1595 5600 0.0002 -
0.1609 5650 0.0003 -
0.1624 5700 0.0003 -
0.1638 5750 0.0003 -
0.1652 5800 0.0002 -
0.1666 5850 0.0003 -
0.1681 5900 0.0003 -
0.1695 5950 0.0003 -
0.1709 6000 0.0002 -
0.1723 6050 0.0002 -
0.1737 6100 0.0002 -
0.1752 6150 0.0002 -
0.1766 6200 0.0003 -
0.1780 6250 0.0002 -
0.1794 6300 0.0003 -
0.1809 6350 0.0002 -
0.1823 6400 0.0003 -
0.1837 6450 0.0003 -
0.1851 6500 0.0002 -
0.1866 6550 0.0002 -
0.1880 6600 0.0004 -
0.1894 6650 0.0002 -
0.1908 6700 0.0002 -
0.1923 6750 0.0002 -
0.1937 6800 0.0002 -
0.1951 6850 0.0002 -
0.1965 6900 0.0002 -
0.1980 6950 0.0002 -
0.1994 7000 0.0002 -
0.2008 7050 0.0002 -
0.2022 7100 0.0002 -
0.2037 7150 0.0003 -
0.2051 7200 0.0002 -
0.2065 7250 0.0002 -
0.2079 7300 0.0002 -
0.2094 7350 0.0002 -
0.2108 7400 0.0002 -
0.2122 7450 0.0002 -
0.2136 7500 0.0002 -
0.2151 7550 0.0002 -
0.2165 7600 0.0002 -
0.2179 7650 0.0002 -
0.2193 7700 0.0002 -
0.2207 7750 0.0002 -
0.2222 7800 0.0001 -
0.2236 7850 0.0002 -
0.2250 7900 0.0002 -
0.2264 7950 0.0002 -
0.2279 8000 0.0002 -
0.2293 8050 0.0002 -
0.2307 8100 0.0002 -
0.2321 8150 0.0002 -
0.2336 8200 0.0002 -
0.2350 8250 0.0004 -
0.2364 8300 0.0001 -
0.2378 8350 0.0002 -
0.2393 8400 0.0001 -
0.2407 8450 0.0002 -
0.2421 8500 0.0001 -
0.2435 8550 0.0002 -
0.2450 8600 0.0002 -
0.2464 8650 0.0002 -
0.2478 8700 0.0001 -
0.2492 8750 0.0001 -
0.2507 8800 0.0001 -
0.2521 8850 0.0002 -
0.2535 8900 0.0002 -
0.2549 8950 0.0002 -
0.2564 9000 0.0002 -
0.2578 9050 0.0001 -
0.2592 9100 0.0001 -
0.2606 9150 0.0003 -
0.2620 9200 0.0001 -
0.2635 9250 0.0001 -
0.2649 9300 0.0002 -
0.2663 9350 0.0001 -
0.2677 9400 0.0001 -
0.2692 9450 0.0001 -
0.2706 9500 0.0002 -
0.2720 9550 0.0002 -
0.2734 9600 0.0002 -
0.2749 9650 0.0001 -
0.2763 9700 0.0002 -
0.2777 9750 0.0001 -
0.2791 9800 0.0001 -
0.2806 9850 0.0001 -
0.2820 9900 0.0002 -
0.2834 9950 0.0002 -
0.2848 10000 0.0001 -
0.2863 10050 0.0001 -
0.2877 10100 0.0001 -
0.2891 10150 0.0002 -
0.2905 10200 0.0001 -
0.2920 10250 0.0002 -
0.2934 10300 0.0001 -
0.2948 10350 0.0002 -
0.2962 10400 0.0001 -
0.2977 10450 0.0001 -
0.2991 10500 0.0001 -
0.3005 10550 0.0001 -
0.3019 10600 0.0001 -
0.3033 10650 0.0001 -
0.3048 10700 0.0001 -
0.3062 10750 0.0001 -
0.3076 10800 0.0001 -
0.3090 10850 0.0001 -
0.3105 10900 0.0001 -
0.3119 10950 0.0001 -
0.3133 11000 0.0001 -
0.3147 11050 0.0001 -
0.3162 11100 0.0001 -
0.3176 11150 0.0001 -
0.3190 11200 0.0001 -
0.3204 11250 0.0001 -
0.3219 11300 0.0001 -
0.3233 11350 0.0001 -
0.3247 11400 0.0002 -
0.3261 11450 0.0001 -
0.3276 11500 0.0001 -
0.3290 11550 0.0001 -
0.3304 11600 0.0001 -
0.3318 11650 0.0001 -
0.3333 11700 0.0002 -
0.3347 11750 0.0001 -
0.3361 11800 0.0001 -
0.3375 11850 0.0001 -
0.3390 11900 0.0002 -
0.3404 11950 0.0001 -
0.3418 12000 0.0001 -
0.3432 12050 0.0002 -
0.3447 12100 0.0001 -
0.3461 12150 0.0001 -
0.3475 12200 0.0001 -
0.3489 12250 0.0003 -
0.3503 12300 0.0003 -
0.3518 12350 0.0003 -
0.3532 12400 0.0269 -
0.3546 12450 0.0475 -
0.3560 12500 0.0004 -
0.3575 12550 0.0003 -
0.3589 12600 0.0005 -
0.3603 12650 0.0003 -
0.3617 12700 0.0001 -
0.3632 12750 0.0002 -
0.3646 12800 0.0003 -
0.3660 12850 0.0002 -
0.3674 12900 0.0001 -
0.3689 12950 0.0004 -
0.3703 13000 0.0002 -
0.3717 13050 0.0002 -
0.3731 13100 0.0003 -
0.3746 13150 0.0002 -
0.3760 13200 0.0003 -
0.3774 13250 0.0003 -
0.3788 13300 0.0001 -
0.3803 13350 0.0002 -
0.3817 13400 0.0002 -
0.3831 13450 0.0002 -
0.3845 13500 0.0002 -
0.3860 13550 0.0002 -
0.3874 13600 0.0002 -
0.3888 13650 0.0001 -
0.3902 13700 0.0001 -
0.3916 13750 0.0002 -
0.3931 13800 0.0003 -
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0.3988 14000 0.0001 -
0.4002 14050 0.0001 -
0.4016 14100 0.0002 -
0.4030 14150 0.0002 -
0.4045 14200 0.0001 -
0.4059 14250 0.0001 -
0.4073 14300 0.0001 -
0.4087 14350 0.0001 -
0.4102 14400 0.0003 -
0.4116 14450 0.0002 -
0.4130 14500 0.0001 -
0.4144 14550 0.0002 -
0.4159 14600 0.0002 -
0.4173 14650 0.0001 -
0.4187 14700 0.0001 -
0.4201 14750 0.0001 -
0.4216 14800 0.0001 -
0.4230 14850 0.0001 -
0.4244 14900 0.0001 -
0.4258 14950 0.0002 -
0.4273 15000 0.0001 -
0.4287 15050 0.0001 -
0.4301 15100 0.0001 -
0.4315 15150 0.0001 -
0.4329 15200 0.0001 -
0.4344 15250 0.0001 -
0.4358 15300 0.0001 -
0.4372 15350 0.0001 -
0.4386 15400 0.0001 -
0.4401 15450 0.0001 -
0.4415 15500 0.0001 -
0.4429 15550 0.0001 -
0.4443 15600 0.0001 -
0.4458 15650 0.0001 -
0.4472 15700 0.0001 -
0.4486 15750 0.0001 -
0.4500 15800 0.0001 -
0.4515 15850 0.0017 -
0.4529 15900 0.0007 -
0.4543 15950 0.0009 -
0.4557 16000 0.0004 -
0.4572 16050 0.0006 -
0.4586 16100 0.0003 -
0.4600 16150 0.0003 -
0.4614 16200 0.0003 -
0.4629 16250 0.0003 -
0.4643 16300 0.0002 -
0.4657 16350 0.0002 -
0.4671 16400 0.0002 -
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0.4700 16500 0.0001 -
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0.4728 16600 0.0002 -
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0.4771 16750 0.0001 -
0.4785 16800 0.0001 -
0.4799 16850 0.0001 -
0.4814 16900 0.0004 -
0.4828 16950 0.0001 -
0.4842 17000 0.0002 -
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0.4885 17150 0.0002 -
0.4899 17200 0.0001 -
0.4913 17250 0.0001 -
0.4928 17300 0.0001 -
0.4942 17350 0.0001 -
0.4956 17400 0.0001 -
0.4970 17450 0.0001 -
0.4985 17500 0.0001 -
0.4999 17550 0.0001 -
0.5013 17600 0.0002 -
0.5027 17650 0.0001 -
0.5042 17700 0.0001 -
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0.5070 17800 0.0001 -
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0.8417 29550 0.0001 -
0.8431 29600 0.0001 -
0.8445 29650 0.0001 -
0.8460 29700 0.0 -
0.8474 29750 0.0 -
0.8488 29800 0.0001 -
0.8502 29850 0.0001 -
0.8517 29900 0.0 -
0.8531 29950 0.0001 -
0.8545 30000 0.0001 -
0.8559 30050 0.0001 -
0.8574 30100 0.0001 -
0.8588 30150 0.0 -
0.8602 30200 0.0 -
0.8616 30250 0.0001 -
0.8631 30300 0.0001 -
0.8645 30350 0.0 -
0.8659 30400 0.0 -
0.8673 30450 0.0001 -
0.8687 30500 0.0 -
0.8702 30550 0.0 -
0.8716 30600 0.0 -
0.8730 30650 0.0001 -
0.8744 30700 0.0 -
0.8759 30750 0.0 -
0.8773 30800 0.0001 -
0.8787 30850 0.0001 -
0.8801 30900 0.0 -
0.8816 30950 0.0 -
0.8830 31000 0.0 -
0.8844 31050 0.0001 -
0.8858 31100 0.0001 -
0.8873 31150 0.0001 -
0.8887 31200 0.0 -
0.8901 31250 0.0 -
0.8915 31300 0.0 -
0.8930 31350 0.0001 -
0.8944 31400 0.0 -
0.8958 31450 0.0 -
0.8972 31500 0.0 -
0.8987 31550 0.0001 -
0.9001 31600 0.0 -
0.9015 31650 0.0 -
0.9029 31700 0.0001 -
0.9044 31750 0.0 -
0.9058 31800 0.0 -
0.9072 31850 0.0 -
0.9086 31900 0.0 -
0.9100 31950 0.0001 -
0.9115 32000 0.0001 -
0.9129 32050 0.0 -
0.9143 32100 0.0 -
0.9157 32150 0.0 -
0.9172 32200 0.0 -
0.9186 32250 0.0 -
0.9200 32300 0.0 -
0.9214 32350 0.0 -
0.9229 32400 0.0 -
0.9243 32450 0.0 -
0.9257 32500 0.0 -
0.9271 32550 0.0 -
0.9286 32600 0.0001 -
0.9300 32650 0.0001 -
0.9314 32700 0.0 -
0.9328 32750 0.0001 -
0.9343 32800 0.0 -
0.9357 32850 0.0 -
0.9371 32900 0.0 -
0.9385 32950 0.0 -
0.9400 33000 0.0 -
0.9414 33050 0.0 -
0.9428 33100 0.0 -
0.9442 33150 0.0001 -
0.9457 33200 0.0001 -
0.9471 33250 0.0 -
0.9485 33300 0.0 -
0.9499 33350 0.0 -
0.9514 33400 0.0 -
0.9528 33450 0.0 -
0.9542 33500 0.0001 -
0.9556 33550 0.0 -
0.9570 33600 0.0 -
0.9585 33650 0.0 -
0.9599 33700 0.0 -
0.9613 33750 0.0001 -
0.9627 33800 0.0 -
0.9642 33850 0.0001 -
0.9656 33900 0.0001 -
0.9670 33950 0.0 -
0.9684 34000 0.0 -
0.9699 34050 0.0 -
0.9713 34100 0.0001 -
0.9727 34150 0.0001 -
0.9741 34200 0.0 -
0.9756 34250 0.0 -
0.9770 34300 0.0 -
0.9784 34350 0.0 -
0.9798 34400 0.0 -
0.9813 34450 0.0 -
0.9827 34500 0.0 -
0.9841 34550 0.0 -
0.9855 34600 0.0 -
0.9870 34650 0.0001 -
0.9884 34700 0.0 -
0.9898 34750 0.0 -
0.9912 34800 0.0 -
0.9927 34850 0.0001 -
0.9941 34900 0.0 -
0.9955 34950 0.0 -
0.9969 35000 0.0001 -
0.9983 35050 0.0 -
0.9998 35100 0.0 -
1.0 35108 - 0.03
  • 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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