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firqaaa
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indo-setfit-bert-base-p3

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
kesedihan
  • 'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'
  • 'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'
  • 'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'
sukacita
  • 'aku mungkin tidak merasa begitu keren'
  • 'saya merasa baik-baik saja'
  • 'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'
cinta
  • 'aku merasa mencintaimu'
  • 'aku akan merasa sangat nostalgia di usia yang begitu muda'
  • 'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'
amarah
  • 'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'
  • 'aku merasa memberontak sedikit kesal gila terkurung'
  • 'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'
takut
  • 'aku merasa seperti diserang oleh landak titanium'
  • 'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'
  • 'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'
kejutan
  • 'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'
  • 'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'
  • 'aku merasa penasaran, bersemangat dan tidak sabar'

Evaluation

Metrics

Label Accuracy
all 0.718

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-p3")
# Run inference
preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 16.7928 56
Label Training Sample Count
kesedihan 300
sukacita 300
cinta 300
amarah 300
takut 300
kejutan 300

Training Hyperparameters

  • batch_size: (128, 128)
  • 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 0.2927 -
0.0024 50 0.2605 -
0.0047 100 0.2591 -
0.0071 150 0.2638 -
0.0095 200 0.245 -
0.0119 250 0.226 -
0.0142 300 0.222 -
0.0166 350 0.1968 -
0.0190 400 0.1703 -
0.0213 450 0.1703 -
0.0237 500 0.1587 -
0.0261 550 0.1087 -
0.0284 600 0.1203 -
0.0308 650 0.0844 -
0.0332 700 0.0696 -
0.0356 750 0.0606 -
0.0379 800 0.0333 -
0.0403 850 0.0453 -
0.0427 900 0.033 -
0.0450 950 0.0142 -
0.0474 1000 0.004 -
0.0498 1050 0.0097 -
0.0521 1100 0.0065 -
0.0545 1150 0.0081 -
0.0569 1200 0.0041 -
0.0593 1250 0.0044 -
0.0616 1300 0.0013 -
0.0640 1350 0.0024 -
0.0664 1400 0.001 -
0.0687 1450 0.0012 -
0.0711 1500 0.0013 -
0.0735 1550 0.0006 -
0.0759 1600 0.0033 -
0.0782 1650 0.0006 -
0.0806 1700 0.0013 -
0.0830 1750 0.0008 -
0.0853 1800 0.0006 -
0.0877 1850 0.0008 -
0.0901 1900 0.0004 -
0.0924 1950 0.0005 -
0.0948 2000 0.0004 -
0.0972 2050 0.0002 -
0.0996 2100 0.0002 -
0.1019 2150 0.0003 -
0.1043 2200 0.0006 -
0.1067 2250 0.0005 -
0.1090 2300 0.0003 -
0.1114 2350 0.0018 -
0.1138 2400 0.0003 -
0.1161 2450 0.0002 -
0.1185 2500 0.0018 -
0.1209 2550 0.0003 -
0.1233 2600 0.0008 -
0.1256 2650 0.0002 -
0.1280 2700 0.0007 -
0.1304 2750 0.006 -
0.1327 2800 0.0002 -
0.1351 2850 0.0001 -
0.1375 2900 0.0001 -
0.1399 2950 0.0001 -
0.1422 3000 0.0001 -
0.1446 3050 0.0001 -
0.1470 3100 0.0001 -
0.1493 3150 0.0001 -
0.1517 3200 0.0002 -
0.1541 3250 0.0003 -
0.1564 3300 0.0004 -
0.1588 3350 0.0001 -
0.1612 3400 0.0001 -
0.1636 3450 0.0014 -
0.1659 3500 0.0005 -
0.1683 3550 0.0003 -
0.1707 3600 0.0001 -
0.1730 3650 0.0001 -
0.1754 3700 0.0001 -
0.1778 3750 0.0001 -
0.1801 3800 0.0001 -
0.1825 3850 0.0001 -
0.1849 3900 0.0001 -
0.1873 3950 0.0001 -
0.1896 4000 0.0001 -
0.1920 4050 0.0001 -
0.1944 4100 0.0003 -
0.1967 4150 0.0006 -
0.1991 4200 0.0001 -
0.2015 4250 0.0 -
0.2038 4300 0.0 -
0.2062 4350 0.0001 -
0.2086 4400 0.0 -
0.2110 4450 0.0 -
0.2133 4500 0.0001 -
0.2157 4550 0.0002 -
0.2181 4600 0.0003 -
0.2204 4650 0.0018 -
0.2228 4700 0.0003 -
0.2252 4750 0.0145 -
0.2276 4800 0.0001 -
0.2299 4850 0.0006 -
0.2323 4900 0.0001 -
0.2347 4950 0.0007 -
0.2370 5000 0.0001 -
0.2394 5050 0.0 -
0.2418 5100 0.0 -
0.2441 5150 0.0001 -
0.2465 5200 0.0003 -
0.2489 5250 0.0 -
0.2513 5300 0.0 -
0.2536 5350 0.0 -
0.2560 5400 0.0 -
0.2584 5450 0.0004 -
0.2607 5500 0.0 -
0.2631 5550 0.0 -
0.2655 5600 0.0 -
0.2678 5650 0.0 -
0.2702 5700 0.0 -
0.2726 5750 0.0002 -
0.2750 5800 0.0 -
0.2773 5850 0.0 -
0.2797 5900 0.0 -
0.2821 5950 0.0 -
0.2844 6000 0.0 -
0.2868 6050 0.0 -
0.2892 6100 0.0 -
0.2916 6150 0.0 -
0.2939 6200 0.0 -
0.2963 6250 0.0 -
0.2987 6300 0.0001 -
0.3010 6350 0.0003 -
0.3034 6400 0.0048 -
0.3058 6450 0.0 -
0.3081 6500 0.0 -
0.3105 6550 0.0 -
0.3129 6600 0.0 -
0.3153 6650 0.0 -
0.3176 6700 0.0 -
0.3200 6750 0.0 -
0.3224 6800 0.0 -
0.3247 6850 0.0 -
0.3271 6900 0.0 -
0.3295 6950 0.0 -
0.3318 7000 0.0 -
0.3342 7050 0.0 -
0.3366 7100 0.0 -
0.3390 7150 0.0011 -
0.3413 7200 0.0002 -
0.3437 7250 0.0 -
0.3461 7300 0.0 -
0.3484 7350 0.0001 -
0.3508 7400 0.0001 -
0.3532 7450 0.0002 -
0.3556 7500 0.0 -
0.3579 7550 0.0 -
0.3603 7600 0.0 -
0.3627 7650 0.0 -
0.3650 7700 0.0 -
0.3674 7750 0.0 -
0.3698 7800 0.0001 -
0.3721 7850 0.0 -
0.3745 7900 0.0 -
0.3769 7950 0.0 -
0.3793 8000 0.0 -
0.3816 8050 0.0 -
0.3840 8100 0.0 -
0.3864 8150 0.0 -
0.3887 8200 0.0 -
0.3911 8250 0.0 -
0.3935 8300 0.0 -
0.3958 8350 0.0 -
0.3982 8400 0.0 -
0.4006 8450 0.0 -
0.4030 8500 0.0 -
0.4053 8550 0.0001 -
0.4077 8600 0.0001 -
0.4101 8650 0.0008 -
0.4124 8700 0.0001 -
0.4148 8750 0.0 -
0.4172 8800 0.0 -
0.4196 8850 0.0001 -
0.4219 8900 0.0 -
0.4243 8950 0.0 -
0.4267 9000 0.0 -
0.4290 9050 0.0 -
0.4314 9100 0.0 -
0.4338 9150 0.0 -
0.4361 9200 0.0 -
0.4385 9250 0.0 -
0.4409 9300 0.0 -
0.4433 9350 0.0 -
0.4456 9400 0.0 -
0.4480 9450 0.0 -
0.4504 9500 0.0 -
0.4527 9550 0.0 -
0.4551 9600 0.0 -
0.4575 9650 0.0 -
0.4598 9700 0.0 -
0.4622 9750 0.0001 -
0.4646 9800 0.0 -
0.4670 9850 0.0 -
0.4693 9900 0.0 -
0.4717 9950 0.0 -
0.4741 10000 0.0 -
0.4764 10050 0.0 -
0.4788 10100 0.0006 -
0.4812 10150 0.0 -
0.4835 10200 0.0 -
0.4859 10250 0.0 -
0.4883 10300 0.0 -
0.4907 10350 0.0 -
0.4930 10400 0.0 -
0.4954 10450 0.0 -
0.4978 10500 0.0 -
0.5001 10550 0.0 -
0.5025 10600 0.0 -
0.5049 10650 0.0 -
0.5073 10700 0.0 -
0.5096 10750 0.0 -
0.5120 10800 0.0 -
0.5144 10850 0.0 -
0.5167 10900 0.0 -
0.5191 10950 0.0 -
0.5215 11000 0.0 -
0.5238 11050 0.0 -
0.5262 11100 0.0 -
0.5286 11150 0.0 -
0.5310 11200 0.0 -
0.5333 11250 0.0 -
0.5357 11300 0.0 -
0.5381 11350 0.0 -
0.5404 11400 0.0 -
0.5428 11450 0.0 -
0.5452 11500 0.0 -
0.5475 11550 0.0 -
0.5499 11600 0.0 -
0.5523 11650 0.0001 -
0.5547 11700 0.0 -
0.5570 11750 0.0043 -
0.5594 11800 0.0 -
0.5618 11850 0.0 -
0.5641 11900 0.0 -
0.5665 11950 0.0 -
0.5689 12000 0.0 -
0.5713 12050 0.0 -
0.5736 12100 0.0 -
0.5760 12150 0.0 -
0.5784 12200 0.0 -
0.5807 12250 0.0029 -
0.5831 12300 0.0 -
0.5855 12350 0.0 -
0.5878 12400 0.0 -
0.5902 12450 0.0 -
0.5926 12500 0.0 -
0.5950 12550 0.0 -
0.5973 12600 0.0 -
0.5997 12650 0.0 -
0.6021 12700 0.0 -
0.6044 12750 0.0 -
0.6068 12800 0.0 -
0.6092 12850 0.0 -
0.6115 12900 0.0 -
0.6139 12950 0.0 -
0.6163 13000 0.0 -
0.6187 13050 0.0 -
0.6210 13100 0.0 -
0.6234 13150 0.0001 -
0.6258 13200 0.0 -
0.6281 13250 0.0 -
0.6305 13300 0.0 -
0.6329 13350 0.0 -
0.6353 13400 0.0001 -
0.6376 13450 0.0 -
0.6400 13500 0.0 -
0.6424 13550 0.0 -
0.6447 13600 0.0 -
0.6471 13650 0.0 -
0.6495 13700 0.0 -
0.6518 13750 0.0 -
0.6542 13800 0.0 -
0.6566 13850 0.0 -
0.6590 13900 0.0 -
0.6613 13950 0.0 -
0.6637 14000 0.0 -
0.6661 14050 0.0 -
0.6684 14100 0.0 -
0.6708 14150 0.0 -
0.6732 14200 0.0 -
0.6755 14250 0.0 -
0.6779 14300 0.0 -
0.6803 14350 0.0 -
0.6827 14400 0.0 -
0.6850 14450 0.0 -
0.6874 14500 0.0 -
0.6898 14550 0.0 -
0.6921 14600 0.0 -
0.6945 14650 0.0 -
0.6969 14700 0.0 -
0.6993 14750 0.0 -
0.7016 14800 0.0 -
0.7040 14850 0.0 -
0.7064 14900 0.0 -
0.7087 14950 0.0 -
0.7111 15000 0.0 -
0.7135 15050 0.0 -
0.7158 15100 0.0 -
0.7182 15150 0.0 -
0.7206 15200 0.0 -
0.7230 15250 0.0 -
0.7253 15300 0.0 -
0.7277 15350 0.0 -
0.7301 15400 0.0 -
0.7324 15450 0.0 -
0.7348 15500 0.0 -
0.7372 15550 0.0 -
0.7395 15600 0.0 -
0.7419 15650 0.0 -
0.7443 15700 0.0 -
0.7467 15750 0.0 -
0.7490 15800 0.0 -
0.7514 15850 0.0 -
0.7538 15900 0.0 -
0.7561 15950 0.0 -
0.7585 16000 0.0 -
0.7609 16050 0.0 -
0.7633 16100 0.0 -
0.7656 16150 0.0 -
0.7680 16200 0.0 -
0.7704 16250 0.0 -
0.7727 16300 0.0 -
0.7751 16350 0.0 -
0.7775 16400 0.0 -
0.7798 16450 0.0 -
0.7822 16500 0.0 -
0.7846 16550 0.0 -
0.7870 16600 0.0 -
0.7893 16650 0.0 -
0.7917 16700 0.0 -
0.7941 16750 0.0 -
0.7964 16800 0.0 -
0.7988 16850 0.0 -
0.8012 16900 0.0 -
0.8035 16950 0.0 -
0.8059 17000 0.0 -
0.8083 17050 0.0 -
0.8107 17100 0.0 -
0.8130 17150 0.0 -
0.8154 17200 0.0 -
0.8178 17250 0.0 -
0.8201 17300 0.0 -
0.8225 17350 0.0 -
0.8249 17400 0.0 -
0.8272 17450 0.0 -
0.8296 17500 0.0 -
0.8320 17550 0.0 -
0.8344 17600 0.0 -
0.8367 17650 0.0 -
0.8391 17700 0.0 -
0.8415 17750 0.0 -
0.8438 17800 0.0 -
0.8462 17850 0.0 -
0.8486 17900 0.0 -
0.8510 17950 0.0 -
0.8533 18000 0.0 -
0.8557 18050 0.0 -
0.8581 18100 0.0 -
0.8604 18150 0.0 -
0.8628 18200 0.0 -
0.8652 18250 0.0 -
0.8675 18300 0.0 -
0.8699 18350 0.0 -
0.8723 18400 0.0 -
0.8747 18450 0.0 -
0.8770 18500 0.0 -
0.8794 18550 0.0 -
0.8818 18600 0.0 -
0.8841 18650 0.0 -
0.8865 18700 0.0 -
0.8889 18750 0.0 -
0.8912 18800 0.0 -
0.8936 18850 0.0 -
0.8960 18900 0.0 -
0.8984 18950 0.0 -
0.9007 19000 0.0 -
0.9031 19050 0.0 -
0.9055 19100 0.0 -
0.9078 19150 0.0 -
0.9102 19200 0.0 -
0.9126 19250 0.0 -
0.9150 19300 0.0 -
0.9173 19350 0.0 -
0.9197 19400 0.0 -
0.9221 19450 0.0 -
0.9244 19500 0.0 -
0.9268 19550 0.0 -
0.9292 19600 0.0 -
0.9315 19650 0.0 -
0.9339 19700 0.0 -
0.9363 19750 0.0 -
0.9387 19800 0.0 -
0.9410 19850 0.0 -
0.9434 19900 0.0 -
0.9458 19950 0.0 -
0.9481 20000 0.0 -
0.9505 20050 0.0 -
0.9529 20100 0.0 -
0.9552 20150 0.0 -
0.9576 20200 0.0 -
0.9600 20250 0.0 -
0.9624 20300 0.0 -
0.9647 20350 0.0 -
0.9671 20400 0.0 -
0.9695 20450 0.0 -
0.9718 20500 0.0 -
0.9742 20550 0.0 -
0.9766 20600 0.0 -
0.9790 20650 0.0 -
0.9813 20700 0.0 -
0.9837 20750 0.0 -
0.9861 20800 0.0 -
0.9884 20850 0.0 -
0.9908 20900 0.0 -
0.9932 20950 0.0 -
0.9955 21000 0.0 -
0.9979 21050 0.0 -
1.0 21094 - 0.2251
  • 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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