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:
- Fine-tuning a Sentence Transformer with contrastive learning.
- 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
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
model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p3")
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}
}