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firqaaa
/
indo-setfit-bert-base-p1

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
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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 -
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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 -
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1.3413 21000 0.0 -
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1.3540 21200 0.0 -
1.3572 21250 0.0 -
1.3604 21300 0.0 -
1.3636 21350 0.0 -
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1.3732 21500 0.0 -
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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 -
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1.4530 22750 0.0 -
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1.5456 24200 0.0 -
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1.6127 25250 0.0 -
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1.6989 26600 0.0001 -
1.7021 26650 0.0 -
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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 -
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1.9288 30200 0.0 -
1.9320 30250 0.0 -
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  • 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}
}
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