Hugging Face's logo

nazhan
/
bge-large-en-v1.5-brahmaputra-iter-8-2nd-1-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-8-2nd-fixed 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
Aggregation
  • 'How many unique customers made purchases last year?'
  • 'Determine the minimum order amount for each customer.'
  • 'Get me sum of total_revenue.'
Tablejoin
  • 'Show me a join of cash flow and variance.'
  • 'Join data_asset_001_forecast with data_asset_kpi_bs tables.'
  • 'Join data_asset_kpi_ma_product with data_asset_001_variance.'
Lookup_1
  • 'Show me asset impairment by year.'
  • 'Get me data_asset_001_pcc group by category.'
  • 'Show me data_asset_001_variance group by category.'
Viewtables
  • 'What are the table names within the starhub_data_asset database that enable data analysis of customer feedback?'
  • 'How can I access the table directory for starhub_data_asset database to view all the available tables?'
  • 'Please show me the tables that contain data related to customer transactions present in the starhub_data_asset database.'
Generalreply
  • "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?"
  • "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?"
  • 'What is your favorite color?'
Lookup
  • "Get me all the customers who haven't placed any orders."
  • 'Get me the list of customers who have a phone number listed.'
  • 'Can you filter by customers who registered without an email address?'
Rejection
  • "I'm not keen on producing any new data sets."
  • "Please don't generate any new data."
  • "I don't want to create any new data outputs."

Evaluation

Metrics

Label Accuracy
all 0.9739

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-8-2nd-1-epoch")
# Run inference
preds = model("Get forecast by service type.")

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 -
0.1259 4350 0.0004 -
0.1273 4400 0.0003 -
0.1288 4450 0.0003 -
0.1302 4500 0.0003 -
0.1317 4550 0.0002 -
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 -
0.3443 11900 0.0004 -
0.3458 11950 0.0002 -
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 -
0.3747 12950 0.0002 -
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 -
0.3892 13450 0.0001 -
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 -
0.3978 13750 0.0002 -
0.3993 13800 0.0002 -
0.4007 13850 0.0003 -
0.4022 13900 0.0002 -
0.4036 13950 0.0003 -
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 -
0.4528 15650 0.0001 -
0.4543 15700 0.0001 -
0.4557 15750 0.0002 -
0.4572 15800 0.0001 -
0.4586 15850 0.0002 -
0.4601 15900 0.0003 -
0.4615 15950 0.0002 -
0.4629 16000 0.0002 -
0.4644 16050 0.0002 -
0.4658 16100 0.0001 -
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 

@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
23
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-8-2nd-1-epoch

Base model

BAAI/bge-large-en-v1.5
Finetuned
(24)
this model

Dataset used to train nazhan/bge-large-en-v1.5-brahmaputra-iter-8-2nd-1-epoch

Evaluation results

View on Papers With Code