Hugging Face's logo

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

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

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 -
0.3945 13850 0.0002 -
0.3959 13900 0.0002 -
0.3973 13950 0.0001 -
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 -
0.4686 16450 0.0002 -
0.4700 16500 0.0001 -
0.4714 16550 0.0002 -
0.4728 16600 0.0002 -
0.4743 16650 0.0001 -
0.4757 16700 0.0002 -
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 -
0.4856 17050 0.0001 -
0.4871 17100 0.0001 -
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 -
0.5056 17750 0.0001 -
0.5070 17800 0.0001 -
0.5084 17850 0.0001 -
0.5099 17900 0.0001 -
0.5113 17950 0.0001 -
0.5127 18000 0.0001 -
0.5141 18050 0.0001 -
0.5156 18100 0.0001 -
0.5170 18150 0.0001 -
0.5184 18200 0.0001 -
0.5198 18250 0.0001 -
0.5212 18300 0.0001 -
0.5227 18350 0.0001 -
0.5241 18400 0.0001 -
0.5255 18450 0.0001 -
0.5269 18500 0.0001 -
0.5284 18550 0.0001 -
0.5298 18600 0.0001 -
0.5312 18650 0.0001 -
0.5326 18700 0.0001 -
0.5341 18750 0.0001 -
0.5355 18800 0.0001 -
0.5369 18850 0.0001 -
0.5383 18900 0.0001 -
0.5398 18950 0.0001 -
0.5412 19000 0.0001 -
0.5426 19050 0.0001 -
0.5440 19100 0.0001 -
0.5455 19150 0.0001 -
0.5469 19200 0.0001 -
0.5483 19250 0.0001 -
0.5497 19300 0.0001 -
0.5512 19350 0.0001 -
0.5526 19400 0.0001 -
0.5540 19450 0.0 -
0.5554 19500 0.0001 -
0.5569 19550 0.0001 -
0.5583 19600 0.0001 -
0.5597 19650 0.0001 -
0.5611 19700 0.0001 -
0.5625 19750 0.0001 -
0.5640 19800 0.0001 -
0.5654 19850 0.0001 -
0.5668 19900 0.0001 -
0.5682 19950 0.0001 -
0.5697 20000 0.0001 -
0.5711 20050 0.0001 -
0.5725 20100 0.0001 -
0.5739 20150 0.0001 -
0.5754 20200 0.0 -
0.5768 20250 0.0001 -
0.5782 20300 0.0001 -
0.5796 20350 0.0 -
0.5811 20400 0.0001 -
0.5825 20450 0.0001 -
0.5839 20500 0.0001 -
0.5853 20550 0.0001 -
0.5868 20600 0.0001 -
0.5882 20650 0.0001 -
0.5896 20700 0.0001 -
0.5910 20750 0.0001 -
0.5925 20800 0.0001 -
0.5939 20850 0.0001 -
0.5953 20900 0.0001 -
0.5967 20950 0.0001 -
0.5982 21000 0.0 -
0.5996 21050 0.0001 -
0.6010 21100 0.0001 -
0.6024 21150 0.0001 -
0.6039 21200 0.0001 -
0.6053 21250 0.0002 -
0.6067 21300 0.0001 -
0.6081 21350 0.0001 -
0.6095 21400 0.0001 -
0.6110 21450 0.0001 -
0.6124 21500 0.0001 -
0.6138 21550 0.0001 -
0.6152 21600 0.0001 -
0.6167 21650 0.0001 -
0.6181 21700 0.0001 -
0.6195 21750 0.0001 -
0.6209 21800 0.0001 -
0.6224 21850 0.0 -
0.6238 21900 0.0001 -
0.6252 21950 0.0001 -
0.6266 22000 0.0001 -
0.6281 22050 0.0001 -
0.6295 22100 0.0001 -
0.6309 22150 0.0001 -
0.6323 22200 0.0001 -
0.6338 22250 0.0001 -
0.6352 22300 0.0001 -
0.6366 22350 0.0001 -
0.6380 22400 0.0001 -
0.6395 22450 0.0001 -
0.6409 22500 0.0001 -
0.6423 22550 0.0001 -
0.6437 22600 0.0001 -
0.6452 22650 0.0001 -
0.6466 22700 0.0001 -
0.6480 22750 0.0001 -
0.6494 22800 0.0001 -
0.6508 22850 0.0001 -
0.6523 22900 0.0 -
0.6537 22950 0.0001 -
0.6551 23000 0.0001 -
0.6565 23050 0.0001 -
0.6580 23100 0.0001 -
0.6594 23150 0.0001 -
0.6608 23200 0.0001 -
0.6622 23250 0.0001 -
0.6637 23300 0.0 -
0.6651 23350 0.0001 -
0.6665 23400 0.0001 -
0.6679 23450 0.0001 -
0.6694 23500 0.0 -
0.6708 23550 0.0001 -
0.6722 23600 0.0 -
0.6736 23650 0.0001 -
0.6751 23700 0.0001 -
0.6765 23750 0.0 -
0.6779 23800 0.0001 -
0.6793 23850 0.0001 -
0.6808 23900 0.0001 -
0.6822 23950 0.0001 -
0.6836 24000 0.0 -
0.6850 24050 0.0001 -
0.6865 24100 0.0 -
0.6879 24150 0.0001 -
0.6893 24200 0.0001 -
0.6907 24250 0.0001 -
0.6921 24300 0.0001 -
0.6936 24350 0.0 -
0.6950 24400 0.0001 -
0.6964 24450 0.0001 -
0.6978 24500 0.0001 -
0.6993 24550 0.0001 -
0.7007 24600 0.0 -
0.7021 24650 0.0 -
0.7035 24700 0.0001 -
0.7050 24750 0.0001 -
0.7064 24800 0.0001 -
0.7078 24850 0.0001 -
0.7092 24900 0.0001 -
0.7107 24950 0.0001 -
0.7121 25000 0.0001 -
0.7135 25050 0.0001 -
0.7149 25100 0.0001 -
0.7164 25150 0.0001 -
0.7178 25200 0.0001 -
0.7192 25250 0.0001 -
0.7206 25300 0.0001 -
0.7221 25350 0.0001 -
0.7235 25400 0.0001 -
0.7249 25450 0.0001 -
0.7263 25500 0.0001 -
0.7278 25550 0.0 -
0.7292 25600 0.0 -
0.7306 25650 0.0 -
0.7320 25700 0.0001 -
0.7335 25750 0.0001 -
0.7349 25800 0.0001 -
0.7363 25850 0.0001 -
0.7377 25900 0.0 -
0.7391 25950 0.0 -
0.7406 26000 0.0001 -
0.7420 26050 0.0001 -
0.7434 26100 0.0 -
0.7448 26150 0.0 -
0.7463 26200 0.0001 -
0.7477 26250 0.0 -
0.7491 26300 0.0 -
0.7505 26350 0.0 -
0.7520 26400 0.0001 -
0.7534 26450 0.0 -
0.7548 26500 0.0001 -
0.7562 26550 0.0001 -
0.7577 26600 0.0001 -
0.7591 26650 0.0001 -
0.7605 26700 0.0 -
0.7619 26750 0.0001 -
0.7634 26800 0.0001 -
0.7648 26850 0.0001 -
0.7662 26900 0.0 -
0.7676 26950 0.0001 -
0.7691 27000 0.0 -
0.7705 27050 0.0 -
0.7719 27100 0.0001 -
0.7733 27150 0.0 -
0.7748 27200 0.0 -
0.7762 27250 0.0001 -
0.7776 27300 0.0001 -
0.7790 27350 0.0001 -
0.7804 27400 0.0001 -
0.7819 27450 0.0 -
0.7833 27500 0.0001 -
0.7847 27550 0.0 -
0.7861 27600 0.0 -
0.7876 27650 0.0001 -
0.7890 27700 0.0001 -
0.7904 27750 0.0 -
0.7918 27800 0.0001 -
0.7933 27850 0.0001 -
0.7947 27900 0.0 -
0.7961 27950 0.0 -
0.7975 28000 0.0 -
0.7990 28050 0.0001 -
0.8004 28100 0.0 -
0.8018 28150 0.0001 -
0.8032 28200 0.0001 -
0.8047 28250 0.0 -
0.8061 28300 0.0 -
0.8075 28350 0.0 -
0.8089 28400 0.0001 -
0.8104 28450 0.0 -
0.8118 28500 0.0 -
0.8132 28550 0.0 -
0.8146 28600 0.0 -
0.8161 28650 0.0 -
0.8175 28700 0.0 -
0.8189 28750 0.0001 -
0.8203 28800 0.0 -
0.8218 28850 0.0 -
0.8232 28900 0.0 -
0.8246 28950 0.0001 -
0.8260 29000 0.0 -
0.8274 29050 0.0001 -
0.8289 29100 0.0001 -
0.8303 29150 0.0001 -
0.8317 29200 0.0001 -
0.8331 29250 0.0001 -
0.8346 29300 0.0001 -
0.8360 29350 0.0 -
0.8374 29400 0.0 -
0.8388 29450 0.0001 -
0.8403 29500 0.0001 -
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}
}
Downloads last month
11
Safetensors
Model size
335M params
Tensor type
F32
·
Inference Examples
Text Classification
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for nazhan/bge-large-en-v1.5-brahmaputra-iter-9-2nd-1-epoch

Base model

BAAI/bge-large-en-v1.5
Finetuned
(22)
this model

Evaluation results

View on Papers With Code