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diwank
/
hn-upvote-classifier

Text Classification
setfit
Safetensors
sentence-transformers
new
generated_from_setfit_trainer
custom_code
Model card Files
xet
Community
diwank commited on
Commit
24d61b8
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verified ·
1 Parent(s): fa37eec

Push model using huggingface_hub.

Browse files
Files changed (5) hide show
  1. README.md +164 -606
  2. config.json +3 -3
  3. config_setfit.json +2 -2
  4. model.safetensors +1 -1
  5. model_head.pkl +1 -1
README.md CHANGED
@@ -12,21 +12,11 @@ tags:
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  - text-classification
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  - generated_from_setfit_trainer
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  widget:
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- - text: 'Title: Pixar’s Rules of Storytelling
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-
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- Source: b''aerogrammestudio.com'''
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- - text: 'Title: What I''ve learned about Open Source community over 30 years
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-
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- Source: b'''''
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- - text: 'Title: My Python code is a neural network
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-
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- Source: b'''''
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- - text: 'Title: The telltale words that could identify generative AI text
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-
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- Source: b'''''
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- - text: 'Title: What I''ve learned about Open Source community over 30 years
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-
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- Source: b'''''
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  inference: true
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  ---
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@@ -58,10 +48,10 @@ The model has been trained using an efficient few-shot learning technique that i
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  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
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  ### Model Labels
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- | Label | Examples |
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- |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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- | 0 | <ul><li>"Title: The telltale words that could identify generative AI text\nSource: b''"</li><li>"Title: What I've learned about Open Source community over 30 years\nSource: b''"</li><li>"Title: My Python code is a neural network\nSource: b''"</li></ul> |
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- | 1 | <ul><li>"Title: Rat Park Experiment: A New Theory of Addiction\nSource: b'sub.garrytan.com'"</li><li>"Title: Thinking the unthinkable\nSource: b'anarchistsoccermom.blogspot.com'"</li><li>"Title: Realtime Analysis of the Oroville Dam Disaster\nSource: b'github.com'"</li></ul> |
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  ## Uses
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@@ -81,8 +71,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("diwank/hn-upvote-classifier")
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  # Run inference
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- preds = model("Title: My Python code is a neural network
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- Source: b''")
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  ```
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  <!--
@@ -112,17 +101,17 @@ Source: b''")
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  ## Training Details
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  ### Training Set Metrics
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- | Training set | Min | Median | Max |
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- |:-------------|:----|:--------|:----|
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- | Word count | 3 | 10.2389 | 20 |
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  | Label | Training Sample Count |
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  |:------|:----------------------|
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- | 0 | 3302 |
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- | 1 | 1114 |
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  ### Training Hyperparameters
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- - batch_size: (256, 16)
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  - num_epochs: (1, 16)
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  - max_steps: -1
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  - sampling_strategy: undersampling
@@ -140,585 +129,154 @@ Source: b''")
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  - load_best_model_at_end: True
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  ### Training Results
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- | Epoch | Step | Training Loss | Validation Loss |
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- |:----------:|:---------:|:-------------:|:---------------:|
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- | 0.0000 | 1 | 0.1861 | - |
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- | 0.0017 | 50 | 0.1334 | - |
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- | 0.0035 | 100 | 0.0344 | - |
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- | 0.0052 | 150 | 0.0048 | - |
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- | 0.0070 | 200 | 0.0027 | - |
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- | 0.0087 | 250 | 0.002 | - |
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- | 0.0104 | 300 | 0.0016 | - |
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- | 0.0122 | 350 | 0.0011 | - |
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- | 0.0139 | 400 | 0.001 | - |
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- | 0.0157 | 450 | 0.0009 | - |
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- | 0.0191 | 550 | 0.0006 | - |
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- | 0.0226 | 650 | 0.0006 | - |
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- | 0.0244 | 700 | 0.0005 | - |
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- | 0.0261 | 750 | 0.0005 | - |
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- | 0.0278 | 800 | 0.0004 | - |
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- | 0.0296 | 850 | 0.0004 | - |
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- | 0.0313 | 900 | 0.0004 | - |
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- | 0.0331 | 950 | 0.0003 | - |
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- | 0.0348 | 1000 | 0.0003 | - |
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- | 0.5759 | 16550 | 0.0 | - |
477
- | 0.5776 | 16600 | 0.0 | - |
478
- | 0.5794 | 16650 | 0.0 | - |
479
- | 0.5811 | 16700 | 0.0 | - |
480
- | 0.5829 | 16750 | 0.0 | - |
481
- | 0.5846 | 16800 | 0.0 | - |
482
- | 0.5863 | 16850 | 0.0 | - |
483
- | 0.5881 | 16900 | 0.0 | - |
484
- | 0.5898 | 16950 | 0.0 | - |
485
- | 0.5916 | 17000 | 0.0 | - |
486
- | 0.5933 | 17050 | 0.0 | - |
487
- | 0.5950 | 17100 | 0.0 | - |
488
- | 0.5968 | 17150 | 0.0 | - |
489
- | 0.5985 | 17200 | 0.0 | - |
490
- | 0.6003 | 17250 | 0.0 | - |
491
- | 0.6020 | 17300 | 0.0 | - |
492
- | 0.6037 | 17350 | 0.0 | - |
493
- | 0.6055 | 17400 | 0.0 | - |
494
- | 0.6072 | 17450 | 0.0 | - |
495
- | 0.6089 | 17500 | 0.0 | - |
496
- | 0.6107 | 17550 | 0.0 | - |
497
- | 0.6124 | 17600 | 0.0 | - |
498
- | 0.6142 | 17650 | 0.0 | - |
499
- | 0.6159 | 17700 | 0.0 | - |
500
- | 0.6176 | 17750 | 0.0 | - |
501
- | 0.6194 | 17800 | 0.0 | - |
502
- | 0.6211 | 17850 | 0.0 | - |
503
- | 0.6229 | 17900 | 0.0 | - |
504
- | 0.6246 | 17950 | 0.0 | - |
505
- | 0.6263 | 18000 | 0.0 | 0.0 |
506
- | 0.6281 | 18050 | 0.0 | - |
507
- | 0.6298 | 18100 | 0.0 | - |
508
- | 0.6316 | 18150 | 0.0 | - |
509
- | 0.6333 | 18200 | 0.0 | - |
510
- | 0.6350 | 18250 | 0.0 | - |
511
- | 0.6368 | 18300 | 0.0 | - |
512
- | 0.6385 | 18350 | 0.0 | - |
513
- | 0.6403 | 18400 | 0.0 | - |
514
- | 0.6420 | 18450 | 0.0 | - |
515
- | 0.6437 | 18500 | 0.0 | - |
516
- | 0.6455 | 18550 | 0.0 | - |
517
- | 0.6472 | 18600 | 0.0 | - |
518
- | 0.6490 | 18650 | 0.0 | - |
519
- | 0.6507 | 18700 | 0.0 | - |
520
- | 0.6524 | 18750 | 0.0 | - |
521
- | 0.6542 | 18800 | 0.0 | - |
522
- | 0.6559 | 18850 | 0.0 | - |
523
- | 0.6577 | 18900 | 0.0 | - |
524
- | 0.6594 | 18950 | 0.0 | - |
525
- | 0.6611 | 19000 | 0.0 | - |
526
- | 0.6629 | 19050 | 0.0 | - |
527
- | 0.6646 | 19100 | 0.0 | - |
528
- | 0.6664 | 19150 | 0.0 | - |
529
- | 0.6681 | 19200 | 0.0 | - |
530
- | 0.6698 | 19250 | 0.0 | - |
531
- | 0.6716 | 19300 | 0.0 | - |
532
- | 0.6733 | 19350 | 0.0 | - |
533
- | 0.6751 | 19400 | 0.0 | - |
534
- | 0.6768 | 19450 | 0.0 | - |
535
- | 0.6785 | 19500 | 0.0 | - |
536
- | 0.6803 | 19550 | 0.0 | - |
537
- | 0.6820 | 19600 | 0.0 | - |
538
- | 0.6838 | 19650 | 0.0 | - |
539
- | 0.6855 | 19700 | 0.0 | - |
540
- | 0.6872 | 19750 | 0.0 | - |
541
- | 0.6890 | 19800 | 0.0 | - |
542
- | 0.6907 | 19850 | 0.0 | - |
543
- | 0.6925 | 19900 | 0.0 | - |
544
- | 0.6942 | 19950 | 0.0 | - |
545
- | 0.6959 | 20000 | 0.0 | - |
546
- | 0.6977 | 20050 | 0.0 | - |
547
- | 0.6994 | 20100 | 0.0 | - |
548
- | 0.7012 | 20150 | 0.0 | - |
549
- | 0.7029 | 20200 | 0.0 | - |
550
- | 0.7046 | 20250 | 0.0 | - |
551
- | 0.7064 | 20300 | 0.0 | - |
552
- | 0.7081 | 20350 | 0.0 | - |
553
- | 0.7099 | 20400 | 0.0 | - |
554
- | 0.7116 | 20450 | 0.0 | - |
555
- | 0.7133 | 20500 | 0.0 | - |
556
- | 0.7151 | 20550 | 0.0 | - |
557
- | 0.7168 | 20600 | 0.0 | - |
558
- | 0.7186 | 20650 | 0.0 | - |
559
- | 0.7203 | 20700 | 0.0 | - |
560
- | 0.7220 | 20750 | 0.0 | - |
561
- | 0.7238 | 20800 | 0.0 | - |
562
- | 0.7255 | 20850 | 0.0 | - |
563
- | 0.7273 | 20900 | 0.0 | - |
564
- | 0.7290 | 20950 | 0.0 | - |
565
- | **0.7307** | **21000** | **0.0** | **0.0** |
566
- | 0.7325 | 21050 | 0.0 | - |
567
- | 0.7342 | 21100 | 0.0 | - |
568
- | 0.7360 | 21150 | 0.0 | - |
569
- | 0.7377 | 21200 | 0.0 | - |
570
- | 0.7394 | 21250 | 0.0 | - |
571
- | 0.7412 | 21300 | 0.0 | - |
572
- | 0.7429 | 21350 | 0.0 | - |
573
- | 0.7447 | 21400 | 0.0 | - |
574
- | 0.7464 | 21450 | 0.0 | - |
575
- | 0.7481 | 21500 | 0.0 | - |
576
- | 0.7499 | 21550 | 0.0 | - |
577
- | 0.7516 | 21600 | 0.0 | - |
578
- | 0.7534 | 21650 | 0.0 | - |
579
- | 0.7551 | 21700 | 0.0 | - |
580
- | 0.7568 | 21750 | 0.0 | - |
581
- | 0.7586 | 21800 | 0.0 | - |
582
- | 0.7603 | 21850 | 0.0 | - |
583
- | 0.7621 | 21900 | 0.0 | - |
584
- | 0.7638 | 21950 | 0.0 | - |
585
- | 0.7655 | 22000 | 0.0 | - |
586
- | 0.7673 | 22050 | 0.0 | - |
587
- | 0.7690 | 22100 | 0.0 | - |
588
- | 0.7708 | 22150 | 0.0 | - |
589
- | 0.7725 | 22200 | 0.0 | - |
590
- | 0.7742 | 22250 | 0.0 | - |
591
- | 0.7760 | 22300 | 0.0 | - |
592
- | 0.7777 | 22350 | 0.0 | - |
593
- | 0.7795 | 22400 | 0.0 | - |
594
- | 0.7812 | 22450 | 0.0 | - |
595
- | 0.7829 | 22500 | 0.0 | - |
596
- | 0.7847 | 22550 | 0.0 | - |
597
- | 0.7864 | 22600 | 0.0 | - |
598
- | 0.7882 | 22650 | 0.0 | - |
599
- | 0.7899 | 22700 | 0.0 | - |
600
- | 0.7916 | 22750 | 0.0 | - |
601
- | 0.7934 | 22800 | 0.0 | - |
602
- | 0.7951 | 22850 | 0.0 | - |
603
- | 0.7969 | 22900 | 0.0 | - |
604
- | 0.7986 | 22950 | 0.0 | - |
605
- | 0.8003 | 23000 | 0.0 | - |
606
- | 0.8021 | 23050 | 0.0 | - |
607
- | 0.8038 | 23100 | 0.0 | - |
608
- | 0.8056 | 23150 | 0.0 | - |
609
- | 0.8073 | 23200 | 0.0 | - |
610
- | 0.8090 | 23250 | 0.0 | - |
611
- | 0.8108 | 23300 | 0.0 | - |
612
- | 0.8125 | 23350 | 0.0 | - |
613
- | 0.8143 | 23400 | 0.0 | - |
614
- | 0.8160 | 23450 | 0.0 | - |
615
- | 0.8177 | 23500 | 0.0 | - |
616
- | 0.8195 | 23550 | 0.0 | - |
617
- | 0.8212 | 23600 | 0.0 | - |
618
- | 0.8230 | 23650 | 0.0 | - |
619
- | 0.8247 | 23700 | 0.0 | - |
620
- | 0.8264 | 23750 | 0.0 | - |
621
- | 0.8282 | 23800 | 0.0 | - |
622
- | 0.8299 | 23850 | 0.0 | - |
623
- | 0.8317 | 23900 | 0.0 | - |
624
- | 0.8334 | 23950 | 0.0 | - |
625
- | 0.8351 | 24000 | 0.0 | 0.0 |
626
- | 0.8369 | 24050 | 0.0 | - |
627
- | 0.8386 | 24100 | 0.0 | - |
628
- | 0.8404 | 24150 | 0.0 | - |
629
- | 0.8421 | 24200 | 0.0 | - |
630
- | 0.8438 | 24250 | 0.0 | - |
631
- | 0.8456 | 24300 | 0.0 | - |
632
- | 0.8473 | 24350 | 0.0 | - |
633
- | 0.8491 | 24400 | 0.0 | - |
634
- | 0.8508 | 24450 | 0.0 | - |
635
- | 0.8525 | 24500 | 0.0 | - |
636
- | 0.8543 | 24550 | 0.0 | - |
637
- | 0.8560 | 24600 | 0.0 | - |
638
- | 0.8577 | 24650 | 0.0 | - |
639
- | 0.8595 | 24700 | 0.0 | - |
640
- | 0.8612 | 24750 | 0.0 | - |
641
- | 0.8630 | 24800 | 0.0 | - |
642
- | 0.8647 | 24850 | 0.0 | - |
643
- | 0.8664 | 24900 | 0.0 | - |
644
- | 0.8682 | 24950 | 0.0 | - |
645
- | 0.8699 | 25000 | 0.0 | - |
646
- | 0.8717 | 25050 | 0.0 | - |
647
- | 0.8734 | 25100 | 0.0 | - |
648
- | 0.8751 | 25150 | 0.0 | - |
649
- | 0.8769 | 25200 | 0.0 | - |
650
- | 0.8786 | 25250 | 0.0 | - |
651
- | 0.8804 | 25300 | 0.0 | - |
652
- | 0.8821 | 25350 | 0.0 | - |
653
- | 0.8838 | 25400 | 0.0 | - |
654
- | 0.8856 | 25450 | 0.0 | - |
655
- | 0.8873 | 25500 | 0.0 | - |
656
- | 0.8891 | 25550 | 0.0 | - |
657
- | 0.8908 | 25600 | 0.0 | - |
658
- | 0.8925 | 25650 | 0.0 | - |
659
- | 0.8943 | 25700 | 0.0 | - |
660
- | 0.8960 | 25750 | 0.0 | - |
661
- | 0.8978 | 25800 | 0.0 | - |
662
- | 0.8995 | 25850 | 0.0 | - |
663
- | 0.9012 | 25900 | 0.0 | - |
664
- | 0.9030 | 25950 | 0.0 | - |
665
- | 0.9047 | 26000 | 0.0 | - |
666
- | 0.9065 | 26050 | 0.0 | - |
667
- | 0.9082 | 26100 | 0.0 | - |
668
- | 0.9099 | 26150 | 0.0 | - |
669
- | 0.9117 | 26200 | 0.0 | - |
670
- | 0.9134 | 26250 | 0.0 | - |
671
- | 0.9152 | 26300 | 0.0 | - |
672
- | 0.9169 | 26350 | 0.0 | - |
673
- | 0.9186 | 26400 | 0.0 | - |
674
- | 0.9204 | 26450 | 0.0 | - |
675
- | 0.9221 | 26500 | 0.0 | - |
676
- | 0.9239 | 26550 | 0.0 | - |
677
- | 0.9256 | 26600 | 0.0 | - |
678
- | 0.9273 | 26650 | 0.0 | - |
679
- | 0.9291 | 26700 | 0.0 | - |
680
- | 0.9308 | 26750 | 0.0 | - |
681
- | 0.9326 | 26800 | 0.0 | - |
682
- | 0.9343 | 26850 | 0.0 | - |
683
- | 0.9360 | 26900 | 0.0 | - |
684
- | 0.9378 | 26950 | 0.0 | - |
685
- | 0.9395 | 27000 | 0.0 | 0.0 |
686
- | 0.9413 | 27050 | 0.0 | - |
687
- | 0.9430 | 27100 | 0.0 | - |
688
- | 0.9447 | 27150 | 0.0 | - |
689
- | 0.9465 | 27200 | 0.0 | - |
690
- | 0.9482 | 27250 | 0.0 | - |
691
- | 0.9500 | 27300 | 0.0 | - |
692
- | 0.9517 | 27350 | 0.0 | - |
693
- | 0.9534 | 27400 | 0.0 | - |
694
- | 0.9552 | 27450 | 0.0 | - |
695
- | 0.9569 | 27500 | 0.0 | - |
696
- | 0.9587 | 27550 | 0.0 | - |
697
- | 0.9604 | 27600 | 0.0 | - |
698
- | 0.9621 | 27650 | 0.0 | - |
699
- | 0.9639 | 27700 | 0.0 | - |
700
- | 0.9656 | 27750 | 0.0 | - |
701
- | 0.9674 | 27800 | 0.0 | - |
702
- | 0.9691 | 27850 | 0.0 | - |
703
- | 0.9708 | 27900 | 0.0 | - |
704
- | 0.9726 | 27950 | 0.0 | - |
705
- | 0.9743 | 28000 | 0.0 | - |
706
- | 0.9761 | 28050 | 0.0 | - |
707
- | 0.9778 | 28100 | 0.0 | - |
708
- | 0.9795 | 28150 | 0.0 | - |
709
- | 0.9813 | 28200 | 0.0 | - |
710
- | 0.9830 | 28250 | 0.0 | - |
711
- | 0.9848 | 28300 | 0.0 | - |
712
- | 0.9865 | 28350 | 0.0 | - |
713
- | 0.9882 | 28400 | 0.0 | - |
714
- | 0.9900 | 28450 | 0.0 | - |
715
- | 0.9917 | 28500 | 0.0 | - |
716
- | 0.9935 | 28550 | 0.0 | - |
717
- | 0.9952 | 28600 | 0.0 | - |
718
- | 0.9969 | 28650 | 0.0 | - |
719
- | 0.9987 | 28700 | 0.0 | - |
720
-
721
- * The bold row denotes the saved checkpoint.
722
  ### Framework Versions
723
  - Python: 3.10.14
724
  - SetFit: 1.0.3
 
12
  - text-classification
13
  - generated_from_setfit_trainer
14
  widget:
15
+ - text: My Python code is a neural network
16
+ - text: The telltale words that could identify generative AI text
17
+ - text: My Python code is a neural network
18
+ - text: My Python code is a neural network
19
+ - text: The telltale words that could identify generative AI text
 
 
 
 
 
 
 
 
 
 
20
  inference: true
21
  ---
22
 
 
48
  - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
49
 
50
  ### Model Labels
51
+ | Label | Examples |
52
+ |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
53
+ | 0 | <ul><li>'The telltale words that could identify generative AI text'</li><li>'The telltale words that could identify generative AI text'</li><li>'The telltale words that could identify generative AI text'</li></ul> |
54
+ | 1 | <ul><li>'Dangerous Feelings\nSource: www.collaborativefund.com'</li><li>'The Modos Paper Monitor\nSource: www.modos.tech'</li><li>'What did Mary know? A thought experiment about consciousness (2013)\nSource: philosophynow.org'</li></ul> |
55
 
56
  ## Uses
57
 
 
71
  # Download from the 🤗 Hub
72
  model = SetFitModel.from_pretrained("diwank/hn-upvote-classifier")
73
  # Run inference
74
+ preds = model("My Python code is a neural network")
 
75
  ```
76
 
77
  <!--
 
101
  ## Training Details
102
 
103
  ### Training Set Metrics
104
+ | Training set | Min | Median | Max |
105
+ |:-------------|:----|:-------|:----|
106
+ | Word count | 3 | 8.6577 | 18 |
107
 
108
  | Label | Training Sample Count |
109
  |:------|:----------------------|
110
+ | 0 | 4577 |
111
+ | 1 | 252 |
112
 
113
  ### Training Hyperparameters
114
+ - batch_size: (320, 32)
115
  - num_epochs: (1, 16)
116
  - max_steps: -1
117
  - sampling_strategy: undersampling
 
129
  - load_best_model_at_end: True
130
 
131
  ### Training Results
132
+ | Epoch | Step | Training Loss | Validation Loss |
133
+ |:------:|:----:|:-------------:|:---------------:|
134
+ | 0.0001 | 1 | 0.208 | - |
135
+ | 0.0069 | 50 | 0.0121 | - |
136
+ | 0.0139 | 100 | 0.002 | - |
137
+ | 0.0208 | 150 | 0.0032 | - |
138
+ | 0.0277 | 200 | 0.001 | - |
139
+ | 0.0347 | 250 | 0.0006 | - |
140
+ | 0.0416 | 300 | 0.0005 | - |
141
+ | 0.0486 | 350 | 0.0004 | - |
142
+ | 0.0555 | 400 | 0.0003 | - |
143
+ | 0.0624 | 450 | 0.0002 | - |
144
+ | 0.0694 | 500 | 0.0002 | - |
145
+ | 0.0763 | 550 | 0.0002 | - |
146
+ | 0.0832 | 600 | 0.0002 | - |
147
+ | 0.0902 | 650 | 0.0001 | - |
148
+ | 0.0971 | 700 | 0.0001 | - |
149
+ | 0.1040 | 750 | 0.0001 | - |
150
+ | 0.1110 | 800 | 0.0001 | - |
151
+ | 0.1179 | 850 | 0.0001 | - |
152
+ | 0.1248 | 900 | 0.0001 | - |
153
+ | 0.1318 | 950 | 0.0001 | - |
154
+ | 0.1387 | 1000 | 0.0001 | - |
155
+ | 0.1457 | 1050 | 0.0001 | - |
156
+ | 0.1526 | 1100 | 0.0001 | - |
157
+ | 0.1595 | 1150 | 0.0001 | - |
158
+ | 0.1665 | 1200 | 0.0001 | - |
159
+ | 0.1734 | 1250 | 0.0001 | - |
160
+ | 0.1803 | 1300 | 0.0001 | - |
161
+ | 0.1873 | 1350 | 0.0001 | - |
162
+ | 0.1942 | 1400 | 0.0001 | - |
163
+ | 0.2011 | 1450 | 0.0001 | - |
164
+ | 0.2081 | 1500 | 0.0001 | - |
165
+ | 0.2150 | 1550 | 0.0001 | - |
166
+ | 0.2219 | 1600 | 0.0 | - |
167
+ | 0.2289 | 1650 | 0.0 | - |
168
+ | 0.2358 | 1700 | 0.0 | - |
169
+ | 0.2428 | 1750 | 0.0 | - |
170
+ | 0.2497 | 1800 | 0.0001 | - |
171
+ | 0.2566 | 1850 | 0.0 | - |
172
+ | 0.2636 | 1900 | 0.0 | - |
173
+ | 0.2705 | 1950 | 0.0 | - |
174
+ | 0.2774 | 2000 | 0.0 | - |
175
+ | 0.2844 | 2050 | 0.0 | - |
176
+ | 0.2913 | 2100 | 0.0 | - |
177
+ | 0.2982 | 2150 | 0.0 | - |
178
+ | 0.3052 | 2200 | 0.0 | - |
179
+ | 0.3121 | 2250 | 0.0 | - |
180
+ | 0.3190 | 2300 | 0.0 | - |
181
+ | 0.3260 | 2350 | 0.0 | - |
182
+ | 0.3329 | 2400 | 0.0 | - |
183
+ | 0.3399 | 2450 | 0.0 | - |
184
+ | 0.3468 | 2500 | 0.0 | - |
185
+ | 0.3537 | 2550 | 0.0 | - |
186
+ | 0.3607 | 2600 | 0.0 | - |
187
+ | 0.3676 | 2650 | 0.0 | - |
188
+ | 0.3745 | 2700 | 0.0 | - |
189
+ | 0.3815 | 2750 | 0.0 | - |
190
+ | 0.3884 | 2800 | 0.0 | - |
191
+ | 0.3953 | 2850 | 0.0 | - |
192
+ | 0.4023 | 2900 | 0.0 | - |
193
+ | 0.4092 | 2950 | 0.0 | - |
194
+ | 0.4161 | 3000 | 0.0 | - |
195
+ | 0.4231 | 3050 | 0.0 | - |
196
+ | 0.4300 | 3100 | 0.0 | - |
197
+ | 0.4370 | 3150 | 0.0 | - |
198
+ | 0.4439 | 3200 | 0.0 | - |
199
+ | 0.4508 | 3250 | 0.0 | - |
200
+ | 0.4578 | 3300 | 0.0 | - |
201
+ | 0.4647 | 3350 | 0.0 | - |
202
+ | 0.4716 | 3400 | 0.0 | - |
203
+ | 0.4786 | 3450 | 0.0 | - |
204
+ | 0.4855 | 3500 | 0.0 | - |
205
+ | 0.4924 | 3550 | 0.0 | - |
206
+ | 0.4994 | 3600 | 0.0 | - |
207
+ | 0.5063 | 3650 | 0.0 | - |
208
+ | 0.5132 | 3700 | 0.0 | - |
209
+ | 0.5202 | 3750 | 0.0 | - |
210
+ | 0.5271 | 3800 | 0.0 | - |
211
+ | 0.5341 | 3850 | 0.0 | - |
212
+ | 0.5410 | 3900 | 0.0 | - |
213
+ | 0.5479 | 3950 | 0.0 | - |
214
+ | 0.5549 | 4000 | 0.0 | - |
215
+ | 0.5618 | 4050 | 0.0 | - |
216
+ | 0.5687 | 4100 | 0.0 | - |
217
+ | 0.5757 | 4150 | 0.0 | - |
218
+ | 0.5826 | 4200 | 0.0 | - |
219
+ | 0.5895 | 4250 | 0.0 | - |
220
+ | 0.5965 | 4300 | 0.0 | - |
221
+ | 0.6034 | 4350 | 0.0 | - |
222
+ | 0.6103 | 4400 | 0.0 | - |
223
+ | 0.6173 | 4450 | 0.0 | - |
224
+ | 0.6242 | 4500 | 0.0 | - |
225
+ | 0.6312 | 4550 | 0.0 | - |
226
+ | 0.6381 | 4600 | 0.0 | - |
227
+ | 0.6450 | 4650 | 0.0 | - |
228
+ | 0.6520 | 4700 | 0.0 | - |
229
+ | 0.6589 | 4750 | 0.0 | - |
230
+ | 0.6658 | 4800 | 0.0 | - |
231
+ | 0.6728 | 4850 | 0.0 | - |
232
+ | 0.6797 | 4900 | 0.0 | - |
233
+ | 0.6866 | 4950 | 0.0 | - |
234
+ | 0.6936 | 5000 | 0.0 | - |
235
+ | 0.7005 | 5050 | 0.0 | - |
236
+ | 0.7074 | 5100 | 0.0 | - |
237
+ | 0.7144 | 5150 | 0.0 | - |
238
+ | 0.7213 | 5200 | 0.0 | - |
239
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