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 |
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
model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p1")
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}
}