Hugging Face's logo

firqaaa
/
indo-setfit-bert-base-p1

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

SetFit with firqaaa/indo-sentence-bert-base

This is a SetFit model that can be used for Text Classification. This SetFit model uses firqaaa/indo-sentence-bert-base 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
positif
  • 'secara implisit mengakui dan merayakan kelicikan dan khayalan diri yang luar biasa dari sebagian besar pebisnis Amerika ini, dan oleh karena itu, dokumen ini mungkin merupakan dokumen Hollywood yang paling jujur \u200b\u200bdan aneh dari semuanya.'
  • 'sebuah potret menarik dari para seniman tanpa kompromi yang mencoba menciptakan sesuatu yang orisinal dengan latar belakang industri musik korporat yang tampaknya hanya peduli pada keuntungan.'
  • 'mengerikan dalam potret obyektif Amerika abad kedua puluh satu yang suram dan hilang.'
negatif
  • 'dengan hari-hari anjing di bulan Agustus yang akan datang, anggaplah film anjing ini setara dengan sinematik dengan kelembapan tinggi.'
  • 'itu kelam dan mudah ditebak, dan tidak banyak yang bisa tertawa.'
  • 'pencapaian film yang paling mustahil?'

Evaluation

Metrics

Label Accuracy
all 0.8171

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("firqaaa/indo-setfit-bert-base-p1")
# Run inference
preds = model("holden caulfield melakukannya dengan lebih baik.")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 1 16.073 45
Label Training Sample Count
negatif 500
positif 500

Training Hyperparameters

  • batch_size: (32, 32)
  • num_epochs: (3, 3)
  • 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 0.3943 -
0.0032 50 0.3398 -
0.0064 100 0.2628 -
0.0096 150 0.2842 -
0.0128 200 0.2317 -
0.0160 250 0.2703 -
0.0192 300 0.2272 -
0.0224 350 0.2496 -
0.0255 400 0.2076 -
0.0287 450 0.207 -
0.0319 500 0.232 -
0.0351 550 0.1439 -
0.0383 600 0.1578 -
0.0415 650 0.0821 -
0.0447 700 0.0628 -
0.0479 750 0.0315 -
0.0511 800 0.0089 -
0.0543 850 0.0106 -
0.0575 900 0.0026 -
0.0607 950 0.0025 -
0.0639 1000 0.0028 -
0.0671 1050 0.0093 -
0.0703 1100 0.0008 -
0.0734 1150 0.0008 -
0.0766 1200 0.0003 -
0.0798 1250 0.0006 -
0.0830 1300 0.0005 -
0.0862 1350 0.0005 -
0.0894 1400 0.0002 -
0.0926 1450 0.0003 -
0.0958 1500 0.0003 -
0.0990 1550 0.0003 -
0.1022 1600 0.0002 -
0.1054 1650 0.0002 -
0.1086 1700 0.0001 -
0.1118 1750 0.0002 -
0.1150 1800 0.0001 -
0.1182 1850 0.0001 -
0.1214 1900 0.0001 -
0.1245 1950 0.0001 -
0.1277 2000 0.0001 -
0.1309 2050 0.0001 -
0.1341 2100 0.0001 -
0.1373 2150 0.0001 -
0.1405 2200 0.0001 -
0.1437 2250 0.0001 -
0.1469 2300 0.0001 -
0.1501 2350 0.0001 -
0.1533 2400 0.0001 -
0.1565 2450 0.0002 -
0.1597 2500 0.0001 -
0.1629 2550 0.0001 -
0.1661 2600 0.0134 -
0.1693 2650 0.0001 -
0.1724 2700 0.0001 -
0.1756 2750 0.0016 -
0.1788 2800 0.0001 -
0.1820 2850 0.0001 -
0.1852 2900 0.0002 -
0.1884 2950 0.0001 -
0.1916 3000 0.0066 -
0.1948 3050 0.0001 -
0.1980 3100 0.0001 -
0.2012 3150 0.0005 -
0.2044 3200 0.0001 -
0.2076 3250 0.0001 -
0.2108 3300 0.0001 -
0.2140 3350 0.0001 -
0.2172 3400 0.0001 -
0.2203 3450 0.0 -
0.2235 3500 0.0001 -
0.2267 3550 0.0 -
0.2299 3600 0.0 -
0.2331 3650 0.021 -
0.2363 3700 0.0001 -
0.2395 3750 0.0 -
0.2427 3800 0.0 -
0.2459 3850 0.0 -
0.2491 3900 0.0 -
0.2523 3950 0.0 -
0.2555 4000 0.0 -
0.2587 4050 0.0001 -
0.2619 4100 0.0 -
0.2651 4150 0.0 -
0.2683 4200 0.0016 -
0.2714 4250 0.0 -
0.2746 4300 0.001 -
0.2778 4350 0.0001 -
0.2810 4400 0.0002 -
0.2842 4450 0.0 -
0.2874 4500 0.0001 -
0.2906 4550 0.0001 -
0.2938 4600 0.0002 -
0.2970 4650 0.0 -
0.3002 4700 0.0305 -
0.3034 4750 0.0 -
0.3066 4800 0.0 -
0.3098 4850 0.0 -
0.3130 4900 0.0 -
0.3162 4950 0.0 -
0.3193 5000 0.0 -
0.3225 5050 0.0 -
0.3257 5100 0.0 -
0.3289 5150 0.0 -
0.3321 5200 0.0 -
0.3353 5250 0.0 -
0.3385 5300 0.0 -
0.3417 5350 0.0 -
0.3449 5400 0.0 -
0.3481 5450 0.0 -
0.3513 5500 0.0 -
0.3545 5550 0.0 -
0.3577 5600 0.0 -
0.3609 5650 0.0 -
0.3641 5700 0.0 -
0.3672 5750 0.0 -
0.3704 5800 0.0 -
0.3736 5850 0.0001 -
0.3768 5900 0.0 -
0.3800 5950 0.0 -
0.3832 6000 0.0 -
0.3864 6050 0.0 -
0.3896 6100 0.0 -
0.3928 6150 0.0001 -
0.3960 6200 0.0002 -
0.3992 6250 0.0 -
0.4024 6300 0.0 -
0.4056 6350 0.0 -
0.4088 6400 0.0 -
0.4120 6450 0.0 -
0.4151 6500 0.0 -
0.4183 6550 0.0 -
0.4215 6600 0.0 -
0.4247 6650 0.0 -
0.4279 6700 0.0 -
0.4311 6750 0.0 -
0.4343 6800 0.0 -
0.4375 6850 0.0 -
0.4407 6900 0.0 -
0.4439 6950 0.0 -
0.4471 7000 0.0 -
0.4503 7050 0.0 -
0.4535 7100 0.0 -
0.4567 7150 0.0 -
0.4599 7200 0.0 -
0.4631 7250 0.0 -
0.4662 7300 0.0 -
0.4694 7350 0.0 -
0.4726 7400 0.0 -
0.4758 7450 0.0 -
0.4790 7500 0.0 -
0.4822 7550 0.0 -
0.4854 7600 0.0 -
0.4886 7650 0.0 -
0.4918 7700 0.0 -
0.4950 7750 0.0 -
0.4982 7800 0.0 -
0.5014 7850 0.0 -
0.5046 7900 0.0 -
0.5078 7950 0.0 -
0.5110 8000 0.0 -
0.5141 8050 0.0 -
0.5173 8100 0.0 -
0.5205 8150 0.0 -
0.5237 8200 0.0 -
0.5269 8250 0.0 -
0.5301 8300 0.0 -
0.5333 8350 0.0 -
0.5365 8400 0.0 -
0.5397 8450 0.0 -
0.5429 8500 0.0 -
0.5461 8550 0.0 -
0.5493 8600 0.0 -
0.5525 8650 0.0 -
0.5557 8700 0.0 -
0.5589 8750 0.0 -
0.5620 8800 0.0 -
0.5652 8850 0.0 -
0.5684 8900 0.0 -
0.5716 8950 0.0 -
0.5748 9000 0.0 -
0.5780 9050 0.0 -
0.5812 9100 0.0 -
0.5844 9150 0.0 -
0.5876 9200 0.0 -
0.5908 9250 0.0 -
0.5940 9300 0.0 -
0.5972 9350 0.0 -
0.6004 9400 0.0 -
0.6036 9450 0.0 -
0.6068 9500 0.0 -
0.6100 9550 0.0 -
0.6131 9600 0.0 -
0.6163 9650 0.0 -
0.6195 9700 0.0 -
0.6227 9750 0.0 -
0.6259 9800 0.0 -
0.6291 9850 0.0 -
0.6323 9900 0.0 -
0.6355 9950 0.0 -
0.6387 10000 0.0 -
0.6419 10050 0.0 -
0.6451 10100 0.0 -
0.6483 10150 0.0 -
0.6515 10200 0.0 -
0.6547 10250 0.0 -
0.6579 10300 0.0 -
0.6610 10350 0.0 -
0.6642 10400 0.0 -
0.6674 10450 0.0 -
0.6706 10500 0.0 -
0.6738 10550 0.0 -
0.6770 10600 0.0 -
0.6802 10650 0.0 -
0.6834 10700 0.0 -
0.6866 10750 0.0 -
0.6898 10800 0.0 -
0.6930 10850 0.0 -
0.6962 10900 0.0 -
0.6994 10950 0.0 -
0.7026 11000 0.0 -
0.7058 11050 0.0 -
0.7089 11100 0.0 -
0.7121 11150 0.0 -
0.7153 11200 0.0 -
0.7185 11250 0.0 -
0.7217 11300 0.0 -
0.7249 11350 0.0 -
0.7281 11400 0.0 -
0.7313 11450 0.0 -
0.7345 11500 0.0 -
0.7377 11550 0.0 -
0.7409 11600 0.0 -
0.7441 11650 0.0 -
0.7473 11700 0.0 -
0.7505 11750 0.0 -
0.7537 11800 0.0 -
0.7568 11850 0.0 -
0.7600 11900 0.0 -
0.7632 11950 0.0 -
0.7664 12000 0.0 -
0.7696 12050 0.0 -
0.7728 12100 0.0 -
0.7760 12150 0.0 -
0.7792 12200 0.0 -
0.7824 12250 0.0 -
0.7856 12300 0.0 -
0.7888 12350 0.0 -
0.7920 12400 0.0 -
0.7952 12450 0.0 -
0.7984 12500 0.0 -
0.8016 12550 0.0 -
0.8048 12600 0.0 -
0.8079 12650 0.0 -
0.8111 12700 0.0 -
0.8143 12750 0.0 -
0.8175 12800 0.0 -
0.8207 12850 0.0 -
0.8239 12900 0.0 -
0.8271 12950 0.0 -
0.8303 13000 0.0 -
0.8335 13050 0.0 -
0.8367 13100 0.0 -
0.8399 13150 0.0 -
0.8431 13200 0.0 -
0.8463 13250 0.0 -
0.8495 13300 0.0 -
0.8527 13350 0.0 -
0.8558 13400 0.0 -
0.8590 13450 0.0 -
0.8622 13500 0.0 -
0.8654 13550 0.0 -
0.8686 13600 0.0 -
0.8718 13650 0.0 -
0.8750 13700 0.0 -
0.8782 13750 0.0 -
0.8814 13800 0.0 -
0.8846 13850 0.0 -
0.8878 13900 0.0 -
0.8910 13950 0.0 -
0.8942 14000 0.0 -
0.8974 14050 0.0 -
0.9006 14100 0.0 -
0.9037 14150 0.0 -
0.9069 14200 0.0 -
0.9101 14250 0.0 -
0.9133 14300 0.0 -
0.9165 14350 0.0 -
0.9197 14400 0.0 -
0.9229 14450 0.0 -
0.9261 14500 0.0 -
0.9293 14550 0.0 -
0.9325 14600 0.0 -
0.9357 14650 0.0 -
0.9389 14700 0.0 -
0.9421 14750 0.0 -
0.9453 14800 0.0 -
0.9485 14850 0.0 -
0.9517 14900 0.0 -
0.9548 14950 0.0 -
0.9580 15000 0.0 -
0.9612 15050 0.0 -
0.9644 15100 0.0 -
0.9676 15150 0.0 -
0.9708 15200 0.0 -
0.9740 15250 0.0 -
0.9772 15300 0.0 -
0.9804 15350 0.0 -
0.9836 15400 0.0 -
0.9868 15450 0.0 -
0.9900 15500 0.0 -
0.9932 15550 0.0 -
0.9964 15600 0.0 -
0.9996 15650 0.0 -
1.0 15657 - 0.2641
1.0027 15700 0.0 -
1.0059 15750 0.0 -
1.0091 15800 0.0 -
1.0123 15850 0.0 -
1.0155 15900 0.0 -
1.0187 15950 0.0 -
1.0219 16000 0.0 -
1.0251 16050 0.0 -
1.0283 16100 0.0 -
1.0315 16150 0.0 -
1.0347 16200 0.0 -
1.0379 16250 0.0 -
1.0411 16300 0.0 -
1.0443 16350 0.0 -
1.0475 16400 0.0 -
1.0506 16450 0.0 -
1.0538 16500 0.0 -
1.0570 16550 0.0 -
1.0602 16600 0.0 -
1.0634 16650 0.0 -
1.0666 16700 0.0 -
1.0698 16750 0.0 -
1.0730 16800 0.0 -
1.0762 16850 0.0 -
1.0794 16900 0.0 -
1.0826 16950 0.0 -
1.0858 17000 0.0 -
1.0890 17050 0.0 -
1.0922 17100 0.0 -
1.0954 17150 0.0 -
1.0986 17200 0.0 -
1.1017 17250 0.0 -
1.1049 17300 0.0 -
1.1081 17350 0.0 -
1.1113 17400 0.0 -
1.1145 17450 0.0 -
1.1177 17500 0.0 -
1.1209 17550 0.0 -
1.1241 17600 0.0 -
1.1273 17650 0.0 -
1.1305 17700 0.0 -
1.1337 17750 0.0 -
1.1369 17800 0.0 -
1.1401 17850 0.0 -
1.1433 17900 0.0 -
1.1465 17950 0.0 -
1.1496 18000 0.0 -
1.1528 18050 0.0 -
1.1560 18100 0.0 -
1.1592 18150 0.0 -
1.1624 18200 0.0 -
1.1656 18250 0.0 -
1.1688 18300 0.0 -
1.1720 18350 0.0 -
1.1752 18400 0.0 -
1.1784 18450 0.0 -
1.1816 18500 0.0 -
1.1848 18550 0.0 -
1.1880 18600 0.0 -
1.1912 18650 0.0 -
1.1944 18700 0.0 -
1.1975 18750 0.0 -
1.2007 18800 0.0 -
1.2039 18850 0.0 -
1.2071 18900 0.0 -
1.2103 18950 0.0 -
1.2135 19000 0.0 -
1.2167 19050 0.0 -
1.2199 19100 0.0 -
1.2231 19150 0.0 -
1.2263 19200 0.0 -
1.2295 19250 0.0 -
1.2327 19300 0.0 -
1.2359 19350 0.0 -
1.2391 19400 0.0 -
1.2423 19450 0.0 -
1.2454 19500 0.0 -
1.2486 19550 0.0 -
1.2518 19600 0.0 -
1.2550 19650 0.0 -
1.2582 19700 0.0 -
1.2614 19750 0.0 -
1.2646 19800 0.0 -
1.2678 19850 0.0 -
1.2710 19900 0.0 -
1.2742 19950 0.0 -
1.2774 20000 0.0 -
1.2806 20050 0.0 -
1.2838 20100 0.0 -
1.2870 20150 0.0 -
1.2902 20200 0.0 -
1.2934 20250 0.0 -
1.2965 20300 0.0 -
1.2997 20350 0.0 -
1.3029 20400 0.0 -
1.3061 20450 0.0 -
1.3093 20500 0.0 -
1.3125 20550 0.0 -
1.3157 20600 0.0 -
1.3189 20650 0.0 -
1.3221 20700 0.0 -
1.3253 20750 0.0 -
1.3285 20800 0.0 -
1.3317 20850 0.0 -
1.3349 20900 0.0 -
1.3381 20950 0.0 -
1.3413 21000 0.0 -
1.3444 21050 0.0 -
1.3476 21100 0.0 -
1.3508 21150 0.0 -
1.3540 21200 0.0 -
1.3572 21250 0.0 -
1.3604 21300 0.0 -
1.3636 21350 0.0 -
1.3668 21400 0.0 -
1.3700 21450 0.0 -
1.3732 21500 0.0 -
1.3764 21550 0.0 -
1.3796 21600 0.0 -
1.3828 21650 0.0 -
1.3860 21700 0.0 -
1.3892 21750 0.0 -
1.3923 21800 0.0 -
1.3955 21850 0.0 -
1.3987 21900 0.0 -
1.4019 21950 0.0 -
1.4051 22000 0.0 -
1.4083 22050 0.0 -
1.4115 22100 0.0 -
1.4147 22150 0.0 -
1.4179 22200 0.0 -
1.4211 22250 0.0 -
1.4243 22300 0.0 -
1.4275 22350 0.0 -
1.4307 22400 0.0 -
1.4339 22450 0.0 -
1.4371 22500 0.0 -
1.4403 22550 0.0 -
1.4434 22600 0.0 -
1.4466 22650 0.0 -
1.4498 22700 0.0 -
1.4530 22750 0.0 -
1.4562 22800 0.0 -
1.4594 22850 0.0 -
1.4626 22900 0.0 -
1.4658 22950 0.0 -
1.4690 23000 0.0 -
1.4722 23050 0.0 -
1.4754 23100 0.0 -
1.4786 23150 0.0 -
1.4818 23200 0.0 -
1.4850 23250 0.0 -
1.4882 23300 0.0 -
1.4913 23350 0.0 -
1.4945 23400 0.0 -
1.4977 23450 0.0 -
1.5009 23500 0.0 -
1.5041 23550 0.0 -
1.5073 23600 0.0 -
1.5105 23650 0.0 -
1.5137 23700 0.0 -
1.5169 23750 0.0 -
1.5201 23800 0.0 -
1.5233 23850 0.0 -
1.5265 23900 0.0002 -
1.5297 23950 0.0003 -
1.5329 24000 0.0 -
1.5361 24050 0.0 -
1.5392 24100 0.0 -
1.5424 24150 0.0 -
1.5456 24200 0.0 -
1.5488 24250 0.0 -
1.5520 24300 0.0 -
1.5552 24350 0.0 -
1.5584 24400 0.0 -
1.5616 24450 0.0 -
1.5648 24500 0.0 -
1.5680 24550 0.0 -
1.5712 24600 0.0 -
1.5744 24650 0.0 -
1.5776 24700 0.0 -
1.5808 24750 0.0 -
1.5840 24800 0.0 -
1.5871 24850 0.0 -
1.5903 24900 0.0 -
1.5935 24950 0.0 -
1.5967 25000 0.0 -
1.5999 25050 0.0 -
1.6031 25100 0.0 -
1.6063 25150 0.0 -
1.6095 25200 0.0 -
1.6127 25250 0.0 -
1.6159 25300 0.0 -
1.6191 25350 0.0 -
1.6223 25400 0.0 -
1.6255 25450 0.0 -
1.6287 25500 0.0 -
1.6319 25550 0.0 -
1.6351 25600 0.0 -
1.6382 25650 0.0 -
1.6414 25700 0.0 -
1.6446 25750 0.0 -
1.6478 25800 0.0 -
1.6510 25850 0.0 -
1.6542 25900 0.0 -
1.6574 25950 0.0 -
1.6606 26000 0.0 -
1.6638 26050 0.0 -
1.6670 26100 0.0 -
1.6702 26150 0.0 -
1.6734 26200 0.0 -
1.6766 26250 0.0 -
1.6798 26300 0.0 -
1.6830 26350 0.0 -
1.6861 26400 0.0 -
1.6893 26450 0.0 -
1.6925 26500 0.0 -
1.6957 26550 0.0 -
1.6989 26600 0.0001 -
1.7021 26650 0.0 -
1.7053 26700 0.0 -
1.7085 26750 0.0 -
1.7117 26800 0.0 -
1.7149 26850 0.0 -
1.7181 26900 0.0 -
1.7213 26950 0.0 -
1.7245 27000 0.0 -
1.7277 27050 0.0 -
1.7309 27100 0.0 -
1.7340 27150 0.0 -
1.7372 27200 0.0 -
1.7404 27250 0.0 -
1.7436 27300 0.0 -
1.7468 27350 0.0 -
1.7500 27400 0.0 -
1.7532 27450 0.0 -
1.7564 27500 0.0 -
1.7596 27550 0.0 -
1.7628 27600 0.0 -
1.7660 27650 0.0 -
1.7692 27700 0.0 -
1.7724 27750 0.0 -
1.7756 27800 0.0 -
1.7788 27850 0.0 -
1.7820 27900 0.0 -
1.7851 27950 0.0 -
1.7883 28000 0.0 -
1.7915 28050 0.0 -
1.7947 28100 0.0 -
1.7979 28150 0.0 -
1.8011 28200 0.0 -
1.8043 28250 0.0 -
1.8075 28300 0.0 -
1.8107 28350 0.0 -
1.8139 28400 0.0 -
1.8171 28450 0.0 -
1.8203 28500 0.0 -
1.8235 28550 0.0 -
1.8267 28600 0.0 -
1.8299 28650 0.0 -
1.8330 28700 0.0 -
1.8362 28750 0.0 -
1.8394 28800 0.0 -
1.8426 28850 0.0 -
1.8458 28900 0.0 -
1.8490 28950 0.0 -
1.8522 29000 0.0 -
1.8554 29050 0.0 -
1.8586 29100 0.0 -
1.8618 29150 0.0 -
1.8650 29200 0.0 -
1.8682 29250 0.0 -
1.8714 29300 0.0 -
1.8746 29350 0.0 -
1.8778 29400 0.0 -
1.8809 29450 0.0 -
1.8841 29500 0.0 -
1.8873 29550 0.0 -
1.8905 29600 0.0 -
1.8937 29650 0.0 -
1.8969 29700 0.0 -
1.9001 29750 0.0 -
1.9033 29800 0.0 -
1.9065 29850 0.0 -
1.9097 29900 0.0 -
1.9129 29950 0.0 -
1.9161 30000 0.0 -
1.9193 30050 0.0 -
1.9225 30100 0.0 -
1.9257 30150 0.0 -
1.9288 30200 0.0 -
1.9320 30250 0.0 -
1.9352 30300 0.0 -
1.9384 30350 0.0 -
1.9416 30400 0.0 -
1.9448 30450 0.0 -
1.9480 30500 0.0 -
1.9512 30550 0.0 -
1.9544 30600 0.0 -
1.9576 30650 0.0 -
1.9608 30700 0.0 -
1.9640 30750 0.0 -
1.9672 30800 0.0 -
1.9704 30850 0.0 -
1.9736 30900 0.0 -
1.9768 30950 0.0 -
1.9799 31000 0.0 -
1.9831 31050 0.0 -
1.9863 31100 0.0 -
1.9895 31150 0.0 -
1.9927 31200 0.0 -
1.9959 31250 0.0 -
1.9991 31300 0.0 -
2.0 31314 - 0.2634
2.0023 31350 0.0 -
2.0055 31400 0.0 -
2.0087 31450 0.0 -
2.0119 31500 0.0 -
2.0151 31550 0.0 -
2.0183 31600 0.0 -
2.0215 31650 0.0 -
2.0247 31700 0.0 -
2.0278 31750 0.0 -
2.0310 31800 0.0 -
2.0342 31850 0.0 -
2.0374 31900 0.0 -
2.0406 31950 0.0 -
2.0438 32000 0.0 -
2.0470 32050 0.0 -
2.0502 32100 0.0 -
2.0534 32150 0.0 -
2.0566 32200 0.0 -
2.0598 32250 0.0 -
2.0630 32300 0.0 -
2.0662 32350 0.0 -
2.0694 32400 0.0 -
2.0726 32450 0.0 -
2.0757 32500 0.0 -
2.0789 32550 0.0 -
2.0821 32600 0.0 -
2.0853 32650 0.0 -
2.0885 32700 0.0 -
2.0917 32750 0.0 -
2.0949 32800 0.0 -
2.0981 32850 0.0 -
2.1013 32900 0.0 -
2.1045 32950 0.0 -
2.1077 33000 0.0 -
2.1109 33050 0.0 -
2.1141 33100 0.0 -
2.1173 33150 0.0 -
2.1205 33200 0.0 -
2.1237 33250 0.0 -
2.1268 33300 0.0 -
2.1300 33350 0.0 -
2.1332 33400 0.0 -
2.1364 33450 0.0 -
2.1396 33500 0.0 -
2.1428 33550 0.0 -
2.1460 33600 0.0 -
2.1492 33650 0.0 -
2.1524 33700 0.0 -
2.1556 33750 0.0 -
2.1588 33800 0.0 -
2.1620 33850 0.0 -
2.1652 33900 0.0 -
2.1684 33950 0.0 -
2.1716 34000 0.0 -
2.1747 34050 0.0 -
2.1779 34100 0.0 -
2.1811 34150 0.0 -
2.1843 34200 0.0 -
2.1875 34250 0.0 -
2.1907 34300 0.0 -
2.1939 34350 0.0 -
2.1971 34400 0.0 -
2.2003 34450 0.0 -
2.2035 34500 0.0 -
2.2067 34550 0.0 -
2.2099 34600 0.0 -
2.2131 34650 0.0 -
2.2163 34700 0.0 -
2.2195 34750 0.0 -
2.2226 34800 0.0 -
2.2258 34850 0.0 -
2.2290 34900 0.0 -
2.2322 34950 0.0 -
2.2354 35000 0.0 -
2.2386 35050 0.0 -
2.2418 35100 0.0 -
2.2450 35150 0.0 -
2.2482 35200 0.0 -
2.2514 35250 0.0 -
2.2546 35300 0.0 -
2.2578 35350 0.0 -
2.2610 35400 0.0 -
2.2642 35450 0.0 -
2.2674 35500 0.0 -
2.2705 35550 0.0 -
2.2737 35600 0.0 -
2.2769 35650 0.0 -
2.2801 35700 0.0 -
2.2833 35750 0.0 -
2.2865 35800 0.0 -
2.2897 35850 0.0 -
2.2929 35900 0.0 -
2.2961 35950 0.0 -
2.2993 36000 0.0 -
2.3025 36050 0.0 -
2.3057 36100 0.0 -
2.3089 36150 0.0 -
2.3121 36200 0.0 -
2.3153 36250 0.0 -
2.3185 36300 0.0 -
2.3216 36350 0.0 -
2.3248 36400 0.0 -
2.3280 36450 0.0 -
2.3312 36500 0.0 -
2.3344 36550 0.0 -
2.3376 36600 0.0 -
2.3408 36650 0.0 -
2.3440 36700 0.0 -
2.3472 36750 0.0 -
2.3504 36800 0.0 -
2.3536 36850 0.0 -
2.3568 36900 0.0 -
2.3600 36950 0.0 -
2.3632 37000 0.0 -
2.3664 37050 0.0 -
2.3695 37100 0.0 -
2.3727 37150 0.0 -
2.3759 37200 0.0 -
2.3791 37250 0.0 -
2.3823 37300 0.0 -
2.3855 37350 0.0 -
2.3887 37400 0.0 -
2.3919 37450 0.0 -
2.3951 37500 0.0 -
2.3983 37550 0.0 -
2.4015 37600 0.0 -
2.4047 37650 0.0 -
2.4079 37700 0.0 -
2.4111 37750 0.0 -
2.4143 37800 0.0 -
2.4174 37850 0.0 -
2.4206 37900 0.0 -
2.4238 37950 0.0 -
2.4270 38000 0.0 -
2.4302 38050 0.0 -
2.4334 38100 0.0 -
2.4366 38150 0.0 -
2.4398 38200 0.0 -
2.4430 38250 0.0 -
2.4462 38300 0.0 -
2.4494 38350 0.0 -
2.4526 38400 0.0 -
2.4558 38450 0.0 -
2.4590 38500 0.0 -
2.4622 38550 0.0 -
2.4654 38600 0.0 -
2.4685 38650 0.0 -
2.4717 38700 0.0 -
2.4749 38750 0.0 -
2.4781 38800 0.0 -
2.4813 38850 0.0 -
2.4845 38900 0.0 -
2.4877 38950 0.0 -
2.4909 39000 0.0 -
2.4941 39050 0.0 -
2.4973 39100 0.0 -
2.5005 39150 0.0 -
2.5037 39200 0.0 -
2.5069 39250 0.0 -
2.5101 39300 0.0 -
2.5133 39350 0.0 -
2.5164 39400 0.0 -
2.5196 39450 0.0 -
2.5228 39500 0.0 -
2.5260 39550 0.0 -
2.5292 39600 0.0 -
2.5324 39650 0.0 -
2.5356 39700 0.0 -
2.5388 39750 0.0 -
2.5420 39800 0.0 -
2.5452 39850 0.0 -
2.5484 39900 0.0 -
2.5516 39950 0.0 -
2.5548 40000 0.0 -
2.5580 40050 0.0 -
2.5612 40100 0.0 -
2.5643 40150 0.0 -
2.5675 40200 0.0 -
2.5707 40250 0.0 -
2.5739 40300 0.0 -
2.5771 40350 0.0 -
2.5803 40400 0.0 -
2.5835 40450 0.0 -
2.5867 40500 0.0 -
2.5899 40550 0.0 -
2.5931 40600 0.0 -
2.5963 40650 0.0 -
2.5995 40700 0.0 -
2.6027 40750 0.0 -
2.6059 40800 0.0 -
2.6091 40850 0.0 -
2.6123 40900 0.0 -
2.6154 40950 0.0 -
2.6186 41000 0.0 -
2.6218 41050 0.0 -
2.6250 41100 0.0 -
2.6282 41150 0.0 -
2.6314 41200 0.0 -
2.6346 41250 0.0 -
2.6378 41300 0.0 -
2.6410 41350 0.0 -
2.6442 41400 0.0 -
2.6474 41450 0.0 -
2.6506 41500 0.0 -
2.6538 41550 0.0 -
2.6570 41600 0.0 -
2.6602 41650 0.0 -
2.6633 41700 0.0 -
2.6665 41750 0.0 -
2.6697 41800 0.0 -
2.6729 41850 0.0 -
2.6761 41900 0.0 -
2.6793 41950 0.0 -
2.6825 42000 0.0 -
2.6857 42050 0.0 -
2.6889 42100 0.0 -
2.6921 42150 0.0 -
2.6953 42200 0.0 -
2.6985 42250 0.0 -
2.7017 42300 0.0 -
2.7049 42350 0.0 -
2.7081 42400 0.0 -
2.7112 42450 0.0 -
2.7144 42500 0.0 -
2.7176 42550 0.0 -
2.7208 42600 0.0 -
2.7240 42650 0.0 -
2.7272 42700 0.0 -
2.7304 42750 0.0 -
2.7336 42800 0.0 -
2.7368 42850 0.0 -
2.7400 42900 0.0 -
2.7432 42950 0.0 -
2.7464 43000 0.0 -
2.7496 43050 0.0 -
2.7528 43100 0.0 -
2.7560 43150 0.0 -
2.7591 43200 0.0 -
2.7623 43250 0.0 -
2.7655 43300 0.0 -
2.7687 43350 0.0 -
2.7719 43400 0.0 -
2.7751 43450 0.0 -
2.7783 43500 0.0 -
2.7815 43550 0.0 -
2.7847 43600 0.0 -
2.7879 43650 0.0 -
2.7911 43700 0.0 -
2.7943 43750 0.0 -
2.7975 43800 0.0 -
2.8007 43850 0.0 -
2.8039 43900 0.0 -
2.8071 43950 0.0 -
2.8102 44000 0.0 -
2.8134 44050 0.0 -
2.8166 44100 0.0 -
2.8198 44150 0.0 -
2.8230 44200 0.0 -
2.8262 44250 0.0 -
2.8294 44300 0.0 -
2.8326 44350 0.0 -
2.8358 44400 0.0 -
2.8390 44450 0.0 -
2.8422 44500 0.0 -
2.8454 44550 0.0 -
2.8486 44600 0.0 -
2.8518 44650 0.0 -
2.8550 44700 0.0 -
2.8581 44750 0.0 -
2.8613 44800 0.0 -
2.8645 44850 0.0 -
2.8677 44900 0.0 -
2.8709 44950 0.0 -
2.8741 45000 0.0 -
2.8773 45050 0.0 -
2.8805 45100 0.0 -
2.8837 45150 0.0 -
2.8869 45200 0.0 -
2.8901 45250 0.0 -
2.8933 45300 0.0 -
2.8965 45350 0.0 -
2.8997 45400 0.0 -
2.9029 45450 0.0 -
2.9060 45500 0.0 -
2.9092 45550 0.0 -
2.9124 45600 0.0 -
2.9156 45650 0.0 -
2.9188 45700 0.0 -
2.9220 45750 0.0 -
2.9252 45800 0.0 -
2.9284 45850 0.0 -
2.9316 45900 0.0 -
2.9348 45950 0.0 -
2.9380 46000 0.0 -
2.9412 46050 0.0 -
2.9444 46100 0.0 -
2.9476 46150 0.0 -
2.9508 46200 0.0 -
2.9540 46250 0.0 -
2.9571 46300 0.0 -
2.9603 46350 0.0 -
2.9635 46400 0.0 -
2.9667 46450 0.0 -
2.9699 46500 0.0 -
2.9731 46550 0.0 -
2.9763 46600 0.0 -
2.9795 46650 0.0 -
2.9827 46700 0.0 -
2.9859 46750 0.0 -
2.9891 46800 0.0 -
2.9923 46850 0.0 -
2.9955 46900 0.0 -
2.9987 46950 0.0 -
3.0 46971 - 0.2651
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.13
  • SetFit: 1.0.3
  • Sentence Transformers: 2.2.2
  • Transformers: 4.36.2
  • PyTorch: 2.1.2+cu121
  • Datasets: 2.16.1
  • Tokenizers: 0.15.0

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
10
Safetensors
Model size
124M 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 firqaaa/indo-setfit-bert-base-p1

Base model

firqaaa/indo-sentence-bert-base
Finetuned
(8)
this model

Evaluation results

View on Papers With Code