SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 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 |
neither |
- 'i asked brand to write it and then let it translate back. so in reality i have no clue what i am sending...'
- "i saw someone summarize brand the other day; it doesn't give answers, it gives answer-shaped responses."
- 'thank you comrade i mean colleague. i will have brand summarize.'
|
peak |
- 'brand!! it helped me finish my resume. i just asked it if it could write my resume based on horribly written descriptions i came up with. and it made it all pretty:)'
- 'been building products for a bit now and your product (audio pen) is simple, useful and just works (like the early magic when product came out). congratulations and keep the flag flying high. not surprised that india is producing apps like yours. high time:-)'
- 'just got access to personalization in brand!! totally unexpected. very happy'
|
pit |
- 'brand recently i came across a very unwell patient in a psychiatric unit who was using product & this was reinforcing his delusional state & detrimentally impacting his mental health. anyone looking into this type of usage of product? what safe guards are being put in place?'
- 'brand product is def better at extracting numbers from images, product failed (pro version) twice...'
- "the stuff brand gives is entirely too scripted and impractical, which is what i'm trying to avoid:/"
|
Evaluation
Metrics
Label |
Accuracy |
F1 |
Precision |
Recall |
all |
0.964 |
[0.8837209302325582, 0.9130434782608696, 0.9781021897810218] |
[1.0, 1.0, 0.9571428571428572] |
[0.7916666666666666, 0.84, 1.0] |
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("jamiehudson/725_model_v5")
preds = model("product the way it shows the sources is so fucking cool, this new ai is amazing")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Word count |
3 |
31.6606 |
98 |
Label |
Training Sample Count |
pit |
277 |
peak |
265 |
neither |
1105 |
Training Hyperparameters
- batch_size: (32, 32)
- 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: False
Training Results
Epoch |
Step |
Training Loss |
Validation Loss |
0.0000 |
1 |
0.3157 |
- |
0.0012 |
50 |
0.2756 |
- |
0.0023 |
100 |
0.2613 |
- |
0.0035 |
150 |
0.278 |
- |
0.0047 |
200 |
0.2617 |
- |
0.0058 |
250 |
0.214 |
- |
0.0070 |
300 |
0.2192 |
- |
0.0082 |
350 |
0.1914 |
- |
0.0093 |
400 |
0.1246 |
- |
0.0105 |
450 |
0.1343 |
- |
0.0117 |
500 |
0.0937 |
- |
0.0129 |
550 |
0.075 |
- |
0.0140 |
600 |
0.0479 |
- |
0.0152 |
650 |
0.0976 |
- |
0.0164 |
700 |
0.0505 |
- |
0.0175 |
750 |
0.0149 |
- |
0.0187 |
800 |
0.0227 |
- |
0.0199 |
850 |
0.0276 |
- |
0.0210 |
900 |
0.0033 |
- |
0.0222 |
950 |
0.0015 |
- |
0.0234 |
1000 |
0.0008 |
- |
0.0245 |
1050 |
0.0005 |
- |
0.0257 |
1100 |
0.001 |
- |
0.0269 |
1150 |
0.0009 |
- |
0.0280 |
1200 |
0.0004 |
- |
0.0292 |
1250 |
0.0007 |
- |
0.0304 |
1300 |
0.001 |
- |
0.0315 |
1350 |
0.0004 |
- |
0.0327 |
1400 |
0.0005 |
- |
0.0339 |
1450 |
0.0003 |
- |
0.0350 |
1500 |
0.0004 |
- |
0.0362 |
1550 |
0.0002 |
- |
0.0374 |
1600 |
0.0004 |
- |
0.0386 |
1650 |
0.0003 |
- |
0.0397 |
1700 |
0.0003 |
- |
0.0409 |
1750 |
0.0005 |
- |
0.0421 |
1800 |
0.0004 |
- |
0.0432 |
1850 |
0.0003 |
- |
0.0444 |
1900 |
0.0002 |
- |
0.0456 |
1950 |
0.0002 |
- |
0.0467 |
2000 |
0.0003 |
- |
0.0479 |
2050 |
0.0002 |
- |
0.0491 |
2100 |
0.0001 |
- |
0.0502 |
2150 |
0.0002 |
- |
0.0514 |
2200 |
0.0256 |
- |
0.0526 |
2250 |
0.0001 |
- |
0.0537 |
2300 |
0.0124 |
- |
0.0549 |
2350 |
0.0004 |
- |
0.0561 |
2400 |
0.0125 |
- |
0.0572 |
2450 |
0.0001 |
- |
0.0584 |
2500 |
0.0002 |
- |
0.0596 |
2550 |
0.0002 |
- |
0.0607 |
2600 |
0.0001 |
- |
0.0619 |
2650 |
0.0002 |
- |
0.0631 |
2700 |
0.0002 |
- |
0.0643 |
2750 |
0.0243 |
- |
0.0654 |
2800 |
0.0001 |
- |
0.0666 |
2850 |
0.0001 |
- |
0.0678 |
2900 |
0.0001 |
- |
0.0689 |
2950 |
0.0002 |
- |
0.0701 |
3000 |
0.006 |
- |
0.0713 |
3050 |
0.0021 |
- |
0.0724 |
3100 |
0.0003 |
- |
0.0736 |
3150 |
0.0003 |
- |
0.0748 |
3200 |
0.0001 |
- |
0.0759 |
3250 |
0.0 |
- |
0.0771 |
3300 |
0.0002 |
- |
0.0783 |
3350 |
0.0001 |
- |
0.0794 |
3400 |
0.0 |
- |
0.0806 |
3450 |
0.0124 |
- |
0.0818 |
3500 |
0.0001 |
- |
0.0829 |
3550 |
0.0001 |
- |
0.0841 |
3600 |
0.0001 |
- |
0.0853 |
3650 |
0.0 |
- |
0.0864 |
3700 |
0.0042 |
- |
0.0876 |
3750 |
0.0001 |
- |
0.0888 |
3800 |
0.0004 |
- |
0.0900 |
3850 |
0.0001 |
- |
0.0911 |
3900 |
0.0 |
- |
0.0923 |
3950 |
0.004 |
- |
0.0935 |
4000 |
0.0002 |
- |
0.0946 |
4050 |
0.0001 |
- |
0.0958 |
4100 |
0.0001 |
- |
0.0970 |
4150 |
0.0 |
- |
0.0981 |
4200 |
0.0 |
- |
0.0993 |
4250 |
0.0008 |
- |
0.1005 |
4300 |
0.0 |
- |
0.1016 |
4350 |
0.0 |
- |
0.1028 |
4400 |
0.0 |
- |
0.1040 |
4450 |
0.0 |
- |
0.1051 |
4500 |
0.0 |
- |
0.1063 |
4550 |
0.0 |
- |
0.1075 |
4600 |
0.0 |
- |
0.1086 |
4650 |
0.0 |
- |
0.1098 |
4700 |
0.0 |
- |
0.1110 |
4750 |
0.0 |
- |
0.1121 |
4800 |
0.0 |
- |
0.1133 |
4850 |
0.0 |
- |
0.1145 |
4900 |
0.0 |
- |
0.1157 |
4950 |
0.0 |
- |
0.1168 |
5000 |
0.0 |
- |
0.1180 |
5050 |
0.0 |
- |
0.1192 |
5100 |
0.0 |
- |
0.1203 |
5150 |
0.0008 |
- |
0.1215 |
5200 |
0.001 |
- |
0.1227 |
5250 |
0.0 |
- |
0.1238 |
5300 |
0.0 |
- |
0.1250 |
5350 |
0.0057 |
- |
0.1262 |
5400 |
0.0014 |
- |
0.1273 |
5450 |
0.0001 |
- |
0.1285 |
5500 |
0.0001 |
- |
0.1297 |
5550 |
0.0001 |
- |
0.1308 |
5600 |
0.0001 |
- |
0.1320 |
5650 |
0.0001 |
- |
0.1332 |
5700 |
0.0 |
- |
0.1343 |
5750 |
0.0 |
- |
0.1355 |
5800 |
0.0004 |
- |
0.1367 |
5850 |
0.0 |
- |
0.1378 |
5900 |
0.0001 |
- |
0.1390 |
5950 |
0.0 |
- |
0.1402 |
6000 |
0.0 |
- |
0.1414 |
6050 |
0.0 |
- |
0.1425 |
6100 |
0.0 |
- |
0.1437 |
6150 |
0.0 |
- |
0.1449 |
6200 |
0.0 |
- |
0.1460 |
6250 |
0.0 |
- |
0.1472 |
6300 |
0.0 |
- |
0.1484 |
6350 |
0.0 |
- |
0.1495 |
6400 |
0.0 |
- |
0.1507 |
6450 |
0.0 |
- |
0.1519 |
6500 |
0.0 |
- |
0.1530 |
6550 |
0.0 |
- |
0.1542 |
6600 |
0.0 |
- |
0.1554 |
6650 |
0.0 |
- |
0.1565 |
6700 |
0.0 |
- |
0.1577 |
6750 |
0.0 |
- |
0.1589 |
6800 |
0.0 |
- |
0.1600 |
6850 |
0.0 |
- |
0.1612 |
6900 |
0.0 |
- |
0.1624 |
6950 |
0.0 |
- |
0.1635 |
7000 |
0.0 |
- |
0.1647 |
7050 |
0.0 |
- |
0.1659 |
7100 |
0.0 |
- |
0.1671 |
7150 |
0.0 |
- |
0.1682 |
7200 |
0.0 |
- |
0.1694 |
7250 |
0.0 |
- |
0.1706 |
7300 |
0.0 |
- |
0.1717 |
7350 |
0.0 |
- |
0.1729 |
7400 |
0.0 |
- |
0.1741 |
7450 |
0.0 |
- |
0.1752 |
7500 |
0.0 |
- |
0.1764 |
7550 |
0.0 |
- |
0.1776 |
7600 |
0.0 |
- |
0.1787 |
7650 |
0.0 |
- |
0.1799 |
7700 |
0.0 |
- |
0.1811 |
7750 |
0.0 |
- |
0.1822 |
7800 |
0.0 |
- |
0.1834 |
7850 |
0.0 |
- |
0.1846 |
7900 |
0.0 |
- |
0.1857 |
7950 |
0.0 |
- |
0.1869 |
8000 |
0.0 |
- |
0.1881 |
8050 |
0.0 |
- |
0.1892 |
8100 |
0.0 |
- |
0.1904 |
8150 |
0.0 |
- |
0.1916 |
8200 |
0.0 |
- |
0.1928 |
8250 |
0.0 |
- |
0.1939 |
8300 |
0.0 |
- |
0.1951 |
8350 |
0.0 |
- |
0.1963 |
8400 |
0.0127 |
- |
0.1974 |
8450 |
0.0001 |
- |
0.1986 |
8500 |
0.0 |
- |
0.1998 |
8550 |
0.0 |
- |
0.2009 |
8600 |
0.0249 |
- |
0.2021 |
8650 |
0.0003 |
- |
0.2033 |
8700 |
0.0 |
- |
0.2044 |
8750 |
0.0003 |
- |
0.2056 |
8800 |
0.0003 |
- |
0.2068 |
8850 |
0.0002 |
- |
0.2079 |
8900 |
0.0 |
- |
0.2091 |
8950 |
0.0 |
- |
0.2103 |
9000 |
0.0001 |
- |
0.2114 |
9050 |
0.0 |
- |
0.2126 |
9100 |
0.0 |
- |
0.2138 |
9150 |
0.0 |
- |
0.2149 |
9200 |
0.0 |
- |
0.2161 |
9250 |
0.0 |
- |
0.2173 |
9300 |
0.0 |
- |
0.2185 |
9350 |
0.0 |
- |
0.2196 |
9400 |
0.0 |
- |
0.2208 |
9450 |
0.0 |
- |
0.2220 |
9500 |
0.0 |
- |
0.2231 |
9550 |
0.0 |
- |
0.2243 |
9600 |
0.0 |
- |
0.2255 |
9650 |
0.0 |
- |
0.2266 |
9700 |
0.0 |
- |
0.2278 |
9750 |
0.0 |
- |
0.2290 |
9800 |
0.0 |
- |
0.2301 |
9850 |
0.0 |
- |
0.2313 |
9900 |
0.0 |
- |
0.2325 |
9950 |
0.0 |
- |
0.2336 |
10000 |
0.0 |
- |
0.2348 |
10050 |
0.0 |
- |
0.2360 |
10100 |
0.0 |
- |
0.2371 |
10150 |
0.0 |
- |
0.2383 |
10200 |
0.0 |
- |
0.2395 |
10250 |
0.0 |
- |
0.2406 |
10300 |
0.0 |
- |
0.2418 |
10350 |
0.0 |
- |
0.2430 |
10400 |
0.0 |
- |
0.2442 |
10450 |
0.0 |
- |
0.2453 |
10500 |
0.0 |
- |
0.2465 |
10550 |
0.0 |
- |
0.2477 |
10600 |
0.0 |
- |
0.2488 |
10650 |
0.0 |
- |
0.2500 |
10700 |
0.0 |
- |
0.2512 |
10750 |
0.0 |
- |
0.2523 |
10800 |
0.0 |
- |
0.2535 |
10850 |
0.0 |
- |
0.2547 |
10900 |
0.0 |
- |
0.2558 |
10950 |
0.0 |
- |
0.2570 |
11000 |
0.0 |
- |
0.2582 |
11050 |
0.0 |
- |
0.2593 |
11100 |
0.0 |
- |
0.2605 |
11150 |
0.0 |
- |
0.2617 |
11200 |
0.0 |
- |
0.2628 |
11250 |
0.0 |
- |
0.2640 |
11300 |
0.0 |
- |
0.2652 |
11350 |
0.0 |
- |
0.2663 |
11400 |
0.0 |
- |
0.2675 |
11450 |
0.0 |
- |
0.2687 |
11500 |
0.0 |
- |
0.2699 |
11550 |
0.0 |
- |
0.2710 |
11600 |
0.0 |
- |
0.2722 |
11650 |
0.0 |
- |
0.2734 |
11700 |
0.0 |
- |
0.2745 |
11750 |
0.0 |
- |
0.2757 |
11800 |
0.0 |
- |
0.2769 |
11850 |
0.0 |
- |
0.2780 |
11900 |
0.0 |
- |
0.2792 |
11950 |
0.0 |
- |
0.2804 |
12000 |
0.0 |
- |
0.2815 |
12050 |
0.0 |
- |
0.2827 |
12100 |
0.0 |
- |
0.2839 |
12150 |
0.0 |
- |
0.2850 |
12200 |
0.0 |
- |
0.2862 |
12250 |
0.0 |
- |
0.2874 |
12300 |
0.0 |
- |
0.2885 |
12350 |
0.0 |
- |
0.2897 |
12400 |
0.0 |
- |
0.2909 |
12450 |
0.0 |
- |
0.2920 |
12500 |
0.0 |
- |
0.2932 |
12550 |
0.0 |
- |
0.2944 |
12600 |
0.0 |
- |
0.2956 |
12650 |
0.0 |
- |
0.2967 |
12700 |
0.0 |
- |
0.2979 |
12750 |
0.0 |
- |
0.2991 |
12800 |
0.0 |
- |
0.3002 |
12850 |
0.0 |
- |
0.3014 |
12900 |
0.0 |
- |
0.3026 |
12950 |
0.0 |
- |
0.3037 |
13000 |
0.0 |
- |
0.3049 |
13050 |
0.0 |
- |
0.3061 |
13100 |
0.0 |
- |
0.3072 |
13150 |
0.0 |
- |
0.3084 |
13200 |
0.0 |
- |
0.3096 |
13250 |
0.0 |
- |
0.3107 |
13300 |
0.0 |
- |
0.3119 |
13350 |
0.0 |
- |
0.3131 |
13400 |
0.0 |
- |
0.3142 |
13450 |
0.0 |
- |
0.3154 |
13500 |
0.0 |
- |
0.3166 |
13550 |
0.0 |
- |
0.3177 |
13600 |
0.0 |
- |
0.3189 |
13650 |
0.0 |
- |
0.3201 |
13700 |
0.0 |
- |
0.3213 |
13750 |
0.0 |
- |
0.3224 |
13800 |
0.0 |
- |
0.3236 |
13850 |
0.0 |
- |
0.3248 |
13900 |
0.0 |
- |
0.3259 |
13950 |
0.0 |
- |
0.3271 |
14000 |
0.0 |
- |
0.3283 |
14050 |
0.0 |
- |
0.3294 |
14100 |
0.0 |
- |
0.3306 |
14150 |
0.0 |
- |
0.3318 |
14200 |
0.0 |
- |
0.3329 |
14250 |
0.0 |
- |
0.3341 |
14300 |
0.0 |
- |
0.3353 |
14350 |
0.0 |
- |
0.3364 |
14400 |
0.0 |
- |
0.3376 |
14450 |
0.0 |
- |
0.3388 |
14500 |
0.0 |
- |
0.3399 |
14550 |
0.0 |
- |
0.3411 |
14600 |
0.0 |
- |
0.3423 |
14650 |
0.0 |
- |
0.3434 |
14700 |
0.0 |
- |
0.3446 |
14750 |
0.0 |
- |
0.3458 |
14800 |
0.0 |
- |
0.3470 |
14850 |
0.0 |
- |
0.3481 |
14900 |
0.0 |
- |
0.3493 |
14950 |
0.0 |
- |
0.3505 |
15000 |
0.0 |
- |
0.3516 |
15050 |
0.0 |
- |
0.3528 |
15100 |
0.0 |
- |
0.3540 |
15150 |
0.0 |
- |
0.3551 |
15200 |
0.0 |
- |
0.3563 |
15250 |
0.0 |
- |
0.3575 |
15300 |
0.0 |
- |
0.3586 |
15350 |
0.0 |
- |
0.3598 |
15400 |
0.0 |
- |
0.3610 |
15450 |
0.0 |
- |
0.3621 |
15500 |
0.0 |
- |
0.3633 |
15550 |
0.0 |
- |
0.3645 |
15600 |
0.0 |
- |
0.3656 |
15650 |
0.0 |
- |
0.3668 |
15700 |
0.0 |
- |
0.3680 |
15750 |
0.0 |
- |
0.3692 |
15800 |
0.0 |
- |
0.3703 |
15850 |
0.0 |
- |
0.3715 |
15900 |
0.0 |
- |
0.3727 |
15950 |
0.0 |
- |
0.3738 |
16000 |
0.0 |
- |
0.3750 |
16050 |
0.0 |
- |
0.3762 |
16100 |
0.0 |
- |
0.3773 |
16150 |
0.0 |
- |
0.3785 |
16200 |
0.0 |
- |
0.3797 |
16250 |
0.0 |
- |
0.3808 |
16300 |
0.0 |
- |
0.3820 |
16350 |
0.0 |
- |
0.3832 |
16400 |
0.0 |
- |
0.3843 |
16450 |
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0.3867 |
16550 |
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0.3878 |
16600 |
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0.3890 |
16650 |
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16700 |
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17100 |
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17300 |
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17350 |
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0.4065 |
17400 |
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0.4077 |
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0.031 |
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0.4089 |
17500 |
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0.4100 |
17550 |
0.0569 |
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0.4112 |
17600 |
0.0006 |
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0.4124 |
17650 |
0.0003 |
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0.4135 |
17700 |
0.0007 |
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0.4147 |
17750 |
0.0002 |
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0.4159 |
17800 |
0.025 |
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0.4170 |
17850 |
0.0032 |
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0.4182 |
17900 |
0.0 |
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0.4194 |
17950 |
0.0 |
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0.4206 |
18000 |
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18050 |
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18100 |
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18150 |
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18200 |
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0.4264 |
18250 |
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0.4276 |
18300 |
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18350 |
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18400 |
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0.4311 |
18450 |
0.0002 |
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0.4322 |
18500 |
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18550 |
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0.4346 |
18600 |
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18650 |
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18700 |
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18800 |
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18850 |
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0.4416 |
18900 |
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0.4427 |
18950 |
0.0001 |
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0.4439 |
19000 |
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0.4451 |
19050 |
0.0 |
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0.4463 |
19100 |
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0.4474 |
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0.4486 |
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0.4498 |
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19450 |
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19550 |
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0.4579 |
19600 |
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0.4591 |
19650 |
0.0001 |
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0.4603 |
19700 |
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19750 |
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0.4626 |
19800 |
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0.4638 |
19850 |
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19900 |
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19950 |
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0.4673 |
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20050 |
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20100 |
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20150 |
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0.4720 |
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0.4731 |
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0.4743 |
20300 |
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0.4790 |
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20550 |
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0.4813 |
20600 |
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0.4825 |
20650 |
0.0 |
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0.4836 |
20700 |
0.0317 |
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20750 |
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20800 |
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20850 |
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0.4883 |
20900 |
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21050 |
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0.4930 |
21100 |
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21150 |
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21200 |
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21250 |
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21300 |
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21350 |
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0.5000 |
21400 |
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21450 |
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21500 |
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21550 |
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21600 |
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0.5058 |
21650 |
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21700 |
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21750 |
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21800 |
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21850 |
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22000 |
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22700 |
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22850 |
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0.5432 |
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0.6519 |
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0.6530 |
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0.6542 |
28000 |
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0.6565 |
28100 |
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0.6577 |
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0.6600 |
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0.6612 |
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0.6635 |
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0.6670 |
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0.6682 |
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0.6705 |
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0.6892 |
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0.6927 |
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0.6939 |
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0.6951 |
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0.6974 |
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0.6986 |
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29950 |
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0.7009 |
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0.7021 |
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0.7033 |
30100 |
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0.7044 |
30150 |
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30700 |
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30850 |
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0.7219 |
30900 |
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31000 |
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31050 |
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31100 |
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31150 |
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31200 |
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31250 |
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0.7325 |
31350 |
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31400 |
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31450 |
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0.7360 |
31500 |
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31650 |
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31700 |
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0.7418 |
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0.7430 |
31800 |
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31850 |
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0.7453 |
31900 |
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0.7465 |
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0.7476 |
32000 |
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0.7488 |
32050 |
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0.7500 |
32100 |
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0.7512 |
32150 |
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0.7523 |
32200 |
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0.7535 |
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0.7547 |
32300 |
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0.7558 |
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0.7570 |
32400 |
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0.7582 |
32450 |
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0.7593 |
32500 |
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32550 |
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0.7617 |
32600 |
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0.7628 |
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0.7640 |
32700 |
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0.7663 |
32800 |
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0.7675 |
32850 |
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0.7687 |
32900 |
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0.7698 |
32950 |
0.0 |
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0.7710 |
33000 |
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0.7722 |
33050 |
0.0 |
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0.7733 |
33100 |
0.0 |
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0.7745 |
33150 |
0.0 |
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0.7757 |
33200 |
0.0 |
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0.7769 |
33250 |
0.0 |
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0.7780 |
33300 |
0.0 |
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0.7792 |
33350 |
0.0 |
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0.7804 |
33400 |
0.0 |
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0.7815 |
33450 |
0.0 |
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0.7827 |
33500 |
0.0 |
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0.7839 |
33550 |
0.0 |
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0.7850 |
33600 |
0.0 |
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0.7862 |
33650 |
0.0 |
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0.7874 |
33700 |
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0.7885 |
33750 |
0.0 |
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0.7897 |
33800 |
0.0 |
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0.7909 |
33850 |
0.0 |
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0.7920 |
33900 |
0.0 |
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0.7932 |
33950 |
0.0 |
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0.7944 |
34000 |
0.0 |
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0.7955 |
34050 |
0.0 |
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0.7967 |
34100 |
0.0 |
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0.7979 |
34150 |
0.0 |
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0.7990 |
34200 |
0.0 |
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0.8002 |
34250 |
0.0 |
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0.8014 |
34300 |
0.0 |
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0.8026 |
34350 |
0.0 |
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0.8037 |
34400 |
0.0 |
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0.8049 |
34450 |
0.0 |
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0.8061 |
34500 |
0.0 |
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0.8072 |
34550 |
0.0 |
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0.8084 |
34600 |
0.0 |
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0.8096 |
34650 |
0.0 |
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0.8107 |
34700 |
0.0 |
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0.8119 |
34750 |
0.0 |
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0.8131 |
34800 |
0.0 |
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0.8142 |
34850 |
0.0 |
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0.8154 |
34900 |
0.0 |
- |
0.8166 |
34950 |
0.0 |
- |
0.8177 |
35000 |
0.0 |
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0.8189 |
35050 |
0.0 |
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0.8201 |
35100 |
0.0 |
- |
0.8212 |
35150 |
0.0 |
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0.8224 |
35200 |
0.0 |
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0.8236 |
35250 |
0.0 |
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0.8247 |
35300 |
0.0009 |
- |
0.8259 |
35350 |
0.0 |
- |
0.8271 |
35400 |
0.0 |
- |
0.8283 |
35450 |
0.0 |
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0.8294 |
35500 |
0.0 |
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0.8306 |
35550 |
0.0 |
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0.8318 |
35600 |
0.0 |
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0.8329 |
35650 |
0.0 |
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0.8341 |
35700 |
0.0 |
- |
0.8353 |
35750 |
0.0001 |
- |
0.8364 |
35800 |
0.0 |
- |
0.8376 |
35850 |
0.0 |
- |
0.8388 |
35900 |
0.0 |
- |
0.8399 |
35950 |
0.0 |
- |
0.8411 |
36000 |
0.0 |
- |
0.8423 |
36050 |
0.0 |
- |
0.8434 |
36100 |
0.0 |
- |
0.8446 |
36150 |
0.0 |
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0.8458 |
36200 |
0.0 |
- |
0.8469 |
36250 |
0.0 |
- |
0.8481 |
36300 |
0.0 |
- |
0.8493 |
36350 |
0.0 |
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0.8504 |
36400 |
0.0 |
- |
0.8516 |
36450 |
0.0 |
- |
0.8528 |
36500 |
0.0 |
- |
0.8540 |
36550 |
0.0 |
- |
0.8551 |
36600 |
0.0 |
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0.8563 |
36650 |
0.0 |
- |
0.8575 |
36700 |
0.0 |
- |
0.8586 |
36750 |
0.0 |
- |
0.8598 |
36800 |
0.0 |
- |
0.8610 |
36850 |
0.0 |
- |
0.8621 |
36900 |
0.0 |
- |
0.8633 |
36950 |
0.0 |
- |
0.8645 |
37000 |
0.0 |
- |
0.8656 |
37050 |
0.0 |
- |
0.8668 |
37100 |
0.0 |
- |
0.8680 |
37150 |
0.0 |
- |
0.8691 |
37200 |
0.0 |
- |
0.8703 |
37250 |
0.0 |
- |
0.8715 |
37300 |
0.0 |
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0.8726 |
37350 |
0.0 |
- |
0.8738 |
37400 |
0.0 |
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0.8750 |
37450 |
0.0 |
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0.8761 |
37500 |
0.0 |
- |
0.8773 |
37550 |
0.0 |
- |
0.8785 |
37600 |
0.0 |
- |
0.8797 |
37650 |
0.0 |
- |
0.8808 |
37700 |
0.0 |
- |
0.8820 |
37750 |
0.0 |
- |
0.8832 |
37800 |
0.0 |
- |
0.8843 |
37850 |
0.0 |
- |
0.8855 |
37900 |
0.0 |
- |
0.8867 |
37950 |
0.0 |
- |
0.8878 |
38000 |
0.0 |
- |
0.8890 |
38050 |
0.0 |
- |
0.8902 |
38100 |
0.0 |
- |
0.8913 |
38150 |
0.0 |
- |
0.8925 |
38200 |
0.0 |
- |
0.8937 |
38250 |
0.0 |
- |
0.8948 |
38300 |
0.0 |
- |
0.8960 |
38350 |
0.0 |
- |
0.8972 |
38400 |
0.0 |
- |
0.8983 |
38450 |
0.0 |
- |
0.8995 |
38500 |
0.0 |
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0.9007 |
38550 |
0.0 |
- |
0.9018 |
38600 |
0.0 |
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0.9030 |
38650 |
0.0 |
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0.9042 |
38700 |
0.0 |
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0.9054 |
38750 |
0.0 |
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0.9065 |
38800 |
0.0 |
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0.9077 |
38850 |
0.0 |
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0.9089 |
38900 |
0.0 |
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0.9100 |
38950 |
0.0 |
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0.9112 |
39000 |
0.0 |
- |
0.9124 |
39050 |
0.0 |
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0.9135 |
39100 |
0.0 |
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0.9147 |
39150 |
0.0 |
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0.9159 |
39200 |
0.0 |
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0.9170 |
39250 |
0.0 |
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0.9182 |
39300 |
0.0 |
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0.9194 |
39350 |
0.0 |
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0.9205 |
39400 |
0.0 |
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0.9217 |
39450 |
0.0 |
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0.9229 |
39500 |
0.0 |
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0.9240 |
39550 |
0.0 |
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0.9252 |
39600 |
0.0 |
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0.9264 |
39650 |
0.0 |
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0.9275 |
39700 |
0.0 |
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0.9287 |
39750 |
0.0 |
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0.9299 |
39800 |
0.0 |
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0.9311 |
39850 |
0.0 |
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0.9322 |
39900 |
0.0 |
- |
0.9334 |
39950 |
0.0 |
- |
0.9346 |
40000 |
0.0 |
- |
0.9357 |
40050 |
0.0 |
- |
0.9369 |
40100 |
0.0 |
- |
0.9381 |
40150 |
0.0 |
- |
0.9392 |
40200 |
0.0 |
- |
0.9404 |
40250 |
0.0 |
- |
0.9416 |
40300 |
0.0 |
- |
0.9427 |
40350 |
0.0 |
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0.9439 |
40400 |
0.0 |
- |
0.9451 |
40450 |
0.0 |
- |
0.9462 |
40500 |
0.0 |
- |
0.9474 |
40550 |
0.0 |
- |
0.9486 |
40600 |
0.0 |
- |
0.9497 |
40650 |
0.0 |
- |
0.9509 |
40700 |
0.0 |
- |
0.9521 |
40750 |
0.0 |
- |
0.9532 |
40800 |
0.0 |
- |
0.9544 |
40850 |
0.0 |
- |
0.9556 |
40900 |
0.0 |
- |
0.9568 |
40950 |
0.0 |
- |
0.9579 |
41000 |
0.0 |
- |
0.9591 |
41050 |
0.0 |
- |
0.9603 |
41100 |
0.0 |
- |
0.9614 |
41150 |
0.0 |
- |
0.9626 |
41200 |
0.0 |
- |
0.9638 |
41250 |
0.0 |
- |
0.9649 |
41300 |
0.0 |
- |
0.9661 |
41350 |
0.0 |
- |
0.9673 |
41400 |
0.0 |
- |
0.9684 |
41450 |
0.0 |
- |
0.9696 |
41500 |
0.0 |
- |
0.9708 |
41550 |
0.0 |
- |
0.9719 |
41600 |
0.0 |
- |
0.9731 |
41650 |
0.0 |
- |
0.9743 |
41700 |
0.0 |
- |
0.9754 |
41750 |
0.0 |
- |
0.9766 |
41800 |
0.0 |
- |
0.9778 |
41850 |
0.0 |
- |
0.9789 |
41900 |
0.0 |
- |
0.9801 |
41950 |
0.0 |
- |
0.9813 |
42000 |
0.0 |
- |
0.9825 |
42050 |
0.0 |
- |
0.9836 |
42100 |
0.0 |
- |
0.9848 |
42150 |
0.0 |
- |
0.9860 |
42200 |
0.0 |
- |
0.9871 |
42250 |
0.0 |
- |
0.9883 |
42300 |
0.0 |
- |
0.9895 |
42350 |
0.0 |
- |
0.9906 |
42400 |
0.0 |
- |
0.9918 |
42450 |
0.0 |
- |
0.9930 |
42500 |
0.0 |
- |
0.9941 |
42550 |
0.0 |
- |
0.9953 |
42600 |
0.0 |
- |
0.9965 |
42650 |
0.0 |
- |
0.9976 |
42700 |
0.0 |
- |
0.9988 |
42750 |
0.0 |
- |
1.0000 |
42800 |
0.0 |
- |
Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.5.1
- Transformers: 4.38.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
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}
}