End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: answerdotai/ModernBERT-large
+tags:
+- generated_from_trainer
+model-index:
+- name: answerdotai-ModernBERT-large_20241230-093521
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# answerdotai-ModernBERT-large_20241230-093521
+
+This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1709
+- Precision@0.01: 0.9292
+- Recall@0.01: 0.9890
+- F1@0.01: 0.9581
+- Accuracy@0.01: 0.9627
+- Precision@0.02: 0.9339
+- Recall@0.02: 0.9873
+- F1@0.02: 0.9599
+- Accuracy@0.02: 0.9644
+- Precision@0.03: 0.9363
+- Recall@0.03: 0.9867
+- F1@0.03: 0.9608
+- Accuracy@0.03: 0.9653
+- Precision@0.04: 0.9374
+- Recall@0.04: 0.9866
+- F1@0.04: 0.9614
+- Accuracy@0.04: 0.9658
+- Precision@0.05: 0.9379
+- Recall@0.05: 0.9865
+- F1@0.05: 0.9616
+- Accuracy@0.05: 0.9660
+- Precision@0.06: 0.9382
+- Recall@0.06: 0.9862
+- F1@0.06: 0.9616
+- Accuracy@0.06: 0.9660
+- Precision@0.07: 0.9388
+- Recall@0.07: 0.9859
+- F1@0.07: 0.9617
+- Accuracy@0.07: 0.9662
+- Precision@0.08: 0.9393
+- Recall@0.08: 0.9859
+- F1@0.08: 0.9620
+- Accuracy@0.08: 0.9664
+- Precision@0.09: 0.9395
+- Recall@0.09: 0.9858
+- F1@0.09: 0.9621
+- Accuracy@0.09: 0.9665
+- Precision@0.1: 0.9397
+- Recall@0.1: 0.9857
+- F1@0.1: 0.9622
+- Accuracy@0.1: 0.9666
+- Precision@0.11: 0.9400
+- Recall@0.11: 0.9856
+- F1@0.11: 0.9623
+- Accuracy@0.11: 0.9667
+- Precision@0.12: 0.9403
+- Recall@0.12: 0.9856
+- F1@0.12: 0.9624
+- Accuracy@0.12: 0.9668
+- Precision@0.13: 0.9406
+- Recall@0.13: 0.9855
+- F1@0.13: 0.9625
+- Accuracy@0.13: 0.9669
+- Precision@0.14: 0.9407
+- Recall@0.14: 0.9855
+- F1@0.14: 0.9626
+- Accuracy@0.14: 0.9670
+- Precision@0.15: 0.9408
+- Recall@0.15: 0.9855
+- F1@0.15: 0.9626
+- Accuracy@0.15: 0.9670
+- Precision@0.16: 0.9411
+- Recall@0.16: 0.9855
+- F1@0.16: 0.9628
+- Accuracy@0.16: 0.9671
+- Precision@0.17: 0.9414
+- Recall@0.17: 0.9853
+- F1@0.17: 0.9628
+- Accuracy@0.17: 0.9672
+- Precision@0.18: 0.9415
+- Recall@0.18: 0.9852
+- F1@0.18: 0.9628
+- Accuracy@0.18: 0.9672
+- Precision@0.19: 0.9416
+- Recall@0.19: 0.9850
+- F1@0.19: 0.9628
+- Accuracy@0.19: 0.9672
+- Precision@0.2: 0.9416
+- Recall@0.2: 0.9850
+- F1@0.2: 0.9628
+- Accuracy@0.2: 0.9672
+- Precision@0.21: 0.9416
+- Recall@0.21: 0.9849
+- F1@0.21: 0.9628
+- Accuracy@0.21: 0.9671
+- Precision@0.22: 0.9419
+- Recall@0.22: 0.9849
+- F1@0.22: 0.9629
+- Accuracy@0.22: 0.9673
+- Precision@0.23: 0.9421
+- Recall@0.23: 0.9849
+- F1@0.23: 0.9630
+- Accuracy@0.23: 0.9674
+- Precision@0.24: 0.9421
+- Recall@0.24: 0.9849
+- F1@0.24: 0.9630
+- Accuracy@0.24: 0.9674
+- Precision@0.25: 0.9422
+- Recall@0.25: 0.9846
+- F1@0.25: 0.9629
+- Accuracy@0.25: 0.9673
+- Precision@0.26: 0.9422
+- Recall@0.26: 0.9846
+- F1@0.26: 0.9629
+- Accuracy@0.26: 0.9673
+- Precision@0.27: 0.9423
+- Recall@0.27: 0.9846
+- F1@0.27: 0.9629
+- Accuracy@0.27: 0.9673
+- Precision@0.28: 0.9423
+- Recall@0.28: 0.9845
+- F1@0.28: 0.9629
+- Accuracy@0.28: 0.9673
+- Precision@0.29: 0.9424
+- Recall@0.29: 0.9844
+- F1@0.29: 0.9629
+- Accuracy@0.29: 0.9673
+- Precision@0.3: 0.9425
+- Recall@0.3: 0.9843
+- F1@0.3: 0.9629
+- Accuracy@0.3: 0.9673
+- Precision@0.31: 0.9425
+- Recall@0.31: 0.9843
+- F1@0.31: 0.9629
+- Accuracy@0.31: 0.9673
+- Precision@0.32: 0.9428
+- Recall@0.32: 0.9841
+- F1@0.32: 0.9630
+- Accuracy@0.32: 0.9674
+- Precision@0.33: 0.9428
+- Recall@0.33: 0.9840
+- F1@0.33: 0.9630
+- Accuracy@0.33: 0.9674
+- Precision@0.34: 0.9429
+- Recall@0.34: 0.9839
+- F1@0.34: 0.9630
+- Accuracy@0.34: 0.9674
+- Precision@0.35: 0.9431
+- Recall@0.35: 0.9838
+- F1@0.35: 0.9630
+- Accuracy@0.35: 0.9674
+- Precision@0.36: 0.9431
+- Recall@0.36: 0.9838
+- F1@0.36: 0.9631
+- Accuracy@0.36: 0.9674
+- Precision@0.37: 0.9432
+- Recall@0.37: 0.9837
+- F1@0.37: 0.9631
+- Accuracy@0.37: 0.9674
+- Precision@0.38: 0.9432
+- Recall@0.38: 0.9837
+- F1@0.38: 0.9631
+- Accuracy@0.38: 0.9674
+- Precision@0.39: 0.9432
+- Recall@0.39: 0.9837
+- F1@0.39: 0.9631
+- Accuracy@0.39: 0.9674
+- Precision@0.4: 0.9433
+- Recall@0.4: 0.9836
+- F1@0.4: 0.9631
+- Accuracy@0.4: 0.9674
+- Precision@0.41: 0.9433
+- Recall@0.41: 0.9835
+- F1@0.41: 0.9630
+- Accuracy@0.41: 0.9674
+- Precision@0.42: 0.9433
+- Recall@0.42: 0.9835
+- F1@0.42: 0.9630
+- Accuracy@0.42: 0.9674
+- Precision@0.43: 0.9434
+- Recall@0.43: 0.9834
+- F1@0.43: 0.9630
+- Accuracy@0.43: 0.9674
+- Precision@0.44: 0.9435
+- Recall@0.44: 0.9834
+- F1@0.44: 0.9630
+- Accuracy@0.44: 0.9674
+- Precision@0.45: 0.9436
+- Recall@0.45: 0.9832
+- F1@0.45: 0.9630
+- Accuracy@0.45: 0.9674
+- Precision@0.46: 0.9438
+- Recall@0.46: 0.9832
+- F1@0.46: 0.9631
+- Accuracy@0.46: 0.9675
+- Precision@0.47: 0.9438
+- Recall@0.47: 0.9831
+- F1@0.47: 0.9631
+- Accuracy@0.47: 0.9675
+- Precision@0.48: 0.9440
+- Recall@0.48: 0.9831
+- F1@0.48: 0.9632
+- Accuracy@0.48: 0.9676
+- Precision@0.49: 0.9442
+- Recall@0.49: 0.9831
+- F1@0.49: 0.9633
+- Accuracy@0.49: 0.9677
+- Precision@0.5: 0.9445
+- Recall@0.5: 0.9830
+- F1@0.5: 0.9634
+- Accuracy@0.5: 0.9678
+- Precision@0.51: 0.9446
+- Recall@0.51: 0.9829
+- F1@0.51: 0.9634
+- Accuracy@0.51: 0.9678
+- Precision@0.52: 0.9447
+- Recall@0.52: 0.9829
+- F1@0.52: 0.9634
+- Accuracy@0.52: 0.9678
+- Precision@0.53: 0.9448
+- Recall@0.53: 0.9828
+- F1@0.53: 0.9635
+- Accuracy@0.53: 0.9678
+- Precision@0.54: 0.9449
+- Recall@0.54: 0.9827
+- F1@0.54: 0.9635
+- Accuracy@0.54: 0.9678
+- Precision@0.55: 0.9449
+- Recall@0.55: 0.9827
+- F1@0.55: 0.9635
+- Accuracy@0.55: 0.9678
+- Precision@0.56: 0.9450
+- Recall@0.56: 0.9826
+- F1@0.56: 0.9635
+- Accuracy@0.56: 0.9678
+- Precision@0.57: 0.9451
+- Recall@0.57: 0.9826
+- F1@0.57: 0.9635
+- Accuracy@0.57: 0.9679
+- Precision@0.58: 0.9453
+- Recall@0.58: 0.9826
+- F1@0.58: 0.9636
+- Accuracy@0.58: 0.9680
+- Precision@0.59: 0.9456
+- Recall@0.59: 0.9826
+- F1@0.59: 0.9637
+- Accuracy@0.59: 0.9681
+- Precision@0.6: 0.9457
+- Recall@0.6: 0.9825
+- F1@0.6: 0.9637
+- Accuracy@0.6: 0.9681
+- Precision@0.61: 0.9457
+- Recall@0.61: 0.9825
+- F1@0.61: 0.9637
+- Accuracy@0.61: 0.9681
+- Precision@0.62: 0.9459
+- Recall@0.62: 0.9824
+- F1@0.62: 0.9638
+- Accuracy@0.62: 0.9682
+- Precision@0.63: 0.9459
+- Recall@0.63: 0.9824
+- F1@0.63: 0.9638
+- Accuracy@0.63: 0.9682
+- Precision@0.64: 0.9460
+- Recall@0.64: 0.9824
+- F1@0.64: 0.9639
+- Accuracy@0.64: 0.9682
+- Precision@0.65: 0.9461
+- Recall@0.65: 0.9824
+- F1@0.65: 0.9639
+- Accuracy@0.65: 0.9683
+- Precision@0.66: 0.9462
+- Recall@0.66: 0.9822
+- F1@0.66: 0.9639
+- Accuracy@0.66: 0.9682
+- Precision@0.67: 0.9463
+- Recall@0.67: 0.9822
+- F1@0.67: 0.9639
+- Accuracy@0.67: 0.9683
+- Precision@0.68: 0.9464
+- Recall@0.68: 0.9820
+- F1@0.68: 0.9639
+- Accuracy@0.68: 0.9682
+- Precision@0.69: 0.9464
+- Recall@0.69: 0.9817
+- F1@0.69: 0.9637
+- Accuracy@0.69: 0.9681
+- Precision@0.7: 0.9465
+- Recall@0.7: 0.9817
+- F1@0.7: 0.9638
+- Accuracy@0.7: 0.9682
+- Precision@0.71: 0.9466
+- Recall@0.71: 0.9817
+- F1@0.71: 0.9638
+- Accuracy@0.71: 0.9682
+- Precision@0.72: 0.9467
+- Recall@0.72: 0.9816
+- F1@0.72: 0.9639
+- Accuracy@0.72: 0.9682
+- Precision@0.73: 0.9467
+- Recall@0.73: 0.9816
+- F1@0.73: 0.9639
+- Accuracy@0.73: 0.9682
+- Precision@0.74: 0.9468
+- Recall@0.74: 0.9815
+- F1@0.74: 0.9638
+- Accuracy@0.74: 0.9682
+- Precision@0.75: 0.9471
+- Recall@0.75: 0.9815
+- F1@0.75: 0.9640
+- Accuracy@0.75: 0.9684
+- Precision@0.76: 0.9475
+- Recall@0.76: 0.9815
+- F1@0.76: 0.9642
+- Accuracy@0.76: 0.9686
+- Precision@0.77: 0.9476
+- Recall@0.77: 0.9815
+- F1@0.77: 0.9642
+- Accuracy@0.77: 0.9686
+- Precision@0.78: 0.9478
+- Recall@0.78: 0.9813
+- F1@0.78: 0.9643
+- Accuracy@0.78: 0.9686
+- Precision@0.79: 0.9479
+- Recall@0.79: 0.9812
+- F1@0.79: 0.9643
+- Accuracy@0.79: 0.9686
+- Precision@0.8: 0.9479
+- Recall@0.8: 0.9812
+- F1@0.8: 0.9643
+- Accuracy@0.8: 0.9686
+- Precision@0.81: 0.9480
+- Recall@0.81: 0.9811
+- F1@0.81: 0.9643
+- Accuracy@0.81: 0.9686
+- Precision@0.82: 0.9481
+- Recall@0.82: 0.9809
+- F1@0.82: 0.9642
+- Accuracy@0.82: 0.9686
+- Precision@0.83: 0.9484
+- Recall@0.83: 0.9809
+- F1@0.83: 0.9644
+- Accuracy@0.83: 0.9687
+- Precision@0.84: 0.9487
+- Recall@0.84: 0.9807
+- F1@0.84: 0.9644
+- Accuracy@0.84: 0.9688
+- Precision@0.85: 0.9489
+- Recall@0.85: 0.9806
+- F1@0.85: 0.9645
+- Accuracy@0.85: 0.9689
+- Precision@0.86: 0.9492
+- Recall@0.86: 0.9805
+- F1@0.86: 0.9646
+- Accuracy@0.86: 0.9689
+- Precision@0.87: 0.9497
+- Recall@0.87: 0.9805
+- F1@0.87: 0.9648
+- Accuracy@0.87: 0.9692
+- Precision@0.88: 0.9500
+- Recall@0.88: 0.9801
+- F1@0.88: 0.9648
+- Accuracy@0.88: 0.9692
+- Precision@0.89: 0.9502
+- Recall@0.89: 0.9798
+- F1@0.89: 0.9648
+- Accuracy@0.89: 0.9691
+- Precision@0.9: 0.9505
+- Recall@0.9: 0.9797
+- F1@0.9: 0.9649
+- Accuracy@0.9: 0.9692
+- Precision@0.91: 0.9506
+- Recall@0.91: 0.9794
+- F1@0.91: 0.9648
+- Accuracy@0.91: 0.9692
+- Precision@0.92: 0.9509
+- Recall@0.92: 0.9792
+- F1@0.92: 0.9649
+- Accuracy@0.92: 0.9693
+- Precision@0.93: 0.9519
+- Recall@0.93: 0.9790
+- F1@0.93: 0.9653
+- Accuracy@0.93: 0.9696
+- Precision@0.94: 0.9525
+- Recall@0.94: 0.9782
+- F1@0.94: 0.9652
+- Accuracy@0.94: 0.9696
+- Precision@0.95: 0.9536
+- Recall@0.95: 0.9774
+- F1@0.95: 0.9654
+- Accuracy@0.95: 0.9697
+- Precision@0.96: 0.9547
+- Recall@0.96: 0.9761
+- F1@0.96: 0.9653
+- Accuracy@0.96: 0.9697
+- Precision@0.97: 0.9565
+- Recall@0.97: 0.9745
+- F1@0.97: 0.9655
+- Accuracy@0.97: 0.9699
+- Precision@0.98: 0.9615
+- Recall@0.98: 0.9707
+- F1@0.98: 0.9661
+- Accuracy@0.98: 0.9706
+- Precision@0.99: 0.9671
+- Recall@0.99: 0.9612
+- F1@0.99: 0.9642
+- Accuracy@0.99: 0.9692
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 64
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Precision@0.01 | Recall@0.01 | F1@0.01 | Accuracy@0.01 | Precision@0.02 | Recall@0.02 | F1@0.02 | Accuracy@0.02 | Precision@0.03 | Recall@0.03 | F1@0.03 | Accuracy@0.03 | Precision@0.04 | Recall@0.04 | F1@0.04 | Accuracy@0.04 | Precision@0.05 | Recall@0.05 | F1@0.05 | Accuracy@0.05 | Precision@0.06 | Recall@0.06 | F1@0.06 | Accuracy@0.06 | Precision@0.07 | Recall@0.07 | F1@0.07 | Accuracy@0.07 | Precision@0.08 | Recall@0.08 | F1@0.08 | Accuracy@0.08 | Precision@0.09 | Recall@0.09 | F1@0.09 | Accuracy@0.09 | Precision@0.1 | Recall@0.1 | F1@0.1 | Accuracy@0.1 | Precision@0.11 | Recall@0.11 | F1@0.11 | Accuracy@0.11 | Precision@0.12 | Recall@0.12 | F1@0.12 | Accuracy@0.12 | Precision@0.13 | Recall@0.13 | F1@0.13 | Accuracy@0.13 | Precision@0.14 | Recall@0.14 | F1@0.14 | Accuracy@0.14 | Precision@0.15 | Recall@0.15 | F1@0.15 | Accuracy@0.15 | Precision@0.16 | Recall@0.16 | F1@0.16 | Accuracy@0.16 | Precision@0.17 | Recall@0.17 | F1@0.17 | Accuracy@0.17 | Precision@0.18 | Recall@0.18 | F1@0.18 | Accuracy@0.18 | Precision@0.19 | Recall@0.19 | F1@0.19 | Accuracy@0.19 | Precision@0.2 | Recall@0.2 | F1@0.2 | Accuracy@0.2 | Precision@0.21 | Recall@0.21 | F1@0.21 | Accuracy@0.21 | Precision@0.22 | Recall@0.22 | F1@0.22 | Accuracy@0.22 | Precision@0.23 | Recall@0.23 | F1@0.23 | Accuracy@0.23 | Precision@0.24 | Recall@0.24 | F1@0.24 | Accuracy@0.24 | Precision@0.25 | Recall@0.25 | F1@0.25 | Accuracy@0.25 | Precision@0.26 | Recall@0.26 | F1@0.26 | Accuracy@0.26 | Precision@0.27 | Recall@0.27 | F1@0.27 | Accuracy@0.27 | Precision@0.28 | Recall@0.28 | F1@0.28 | Accuracy@0.28 | Precision@0.29 | Recall@0.29 | F1@0.29 | Accuracy@0.29 | Precision@0.3 | Recall@0.3 | F1@0.3 | Accuracy@0.3 | Precision@0.31 | Recall@0.31 | F1@0.31 | Accuracy@0.31 | Precision@0.32 | Recall@0.32 | F1@0.32 | Accuracy@0.32 | Precision@0.33 | Recall@0.33 | F1@0.33 | Accuracy@0.33 | Precision@0.34 | Recall@0.34 | F1@0.34 | Accuracy@0.34 | Precision@0.35 | Recall@0.35 | F1@0.35 | Accuracy@0.35 | Precision@0.36 | Recall@0.36 | F1@0.36 | Accuracy@0.36 | Precision@0.37 | Recall@0.37 | F1@0.37 | Accuracy@0.37 | Precision@0.38 | Recall@0.38 | F1@0.38 | Accuracy@0.38 | Precision@0.39 | Recall@0.39 | F1@0.39 | Accuracy@0.39 | Precision@0.4 | Recall@0.4 | F1@0.4 | Accuracy@0.4 | Precision@0.41 | Recall@0.41 | F1@0.41 | Accuracy@0.41 | Precision@0.42 | Recall@0.42 | F1@0.42 | Accuracy@0.42 | Precision@0.43 | Recall@0.43 | F1@0.43 | Accuracy@0.43 | Precision@0.44 | Recall@0.44 | F1@0.44 | Accuracy@0.44 | Precision@0.45 | Recall@0.45 | F1@0.45 | Accuracy@0.45 | Precision@0.46 | Recall@0.46 | F1@0.46 | Accuracy@0.46 | Precision@0.47 | Recall@0.47 | F1@0.47 | Accuracy@0.47 | Precision@0.48 | Recall@0.48 | F1@0.48 | Accuracy@0.48 | Precision@0.49 | Recall@0.49 | F1@0.49 | Accuracy@0.49 | Precision@0.5 | Recall@0.5 | F1@0.5 | Accuracy@0.5 | Precision@0.51 | Recall@0.51 | F1@0.51 | Accuracy@0.51 | Precision@0.52 | Recall@0.52 | F1@0.52 | Accuracy@0.52 | Precision@0.53 | Recall@0.53 | F1@0.53 | Accuracy@0.53 | Precision@0.54 | Recall@0.54 | F1@0.54 | Accuracy@0.54 | Precision@0.55 | Recall@0.55 | F1@0.55 | Accuracy@0.55 | Precision@0.56 | Recall@0.56 | F1@0.56 | Accuracy@0.56 | Precision@0.57 | Recall@0.57 | F1@0.57 | Accuracy@0.57 | Precision@0.58 | Recall@0.58 | F1@0.58 | Accuracy@0.58 | Precision@0.59 | Recall@0.59 | F1@0.59 | Accuracy@0.59 | Precision@0.6 | Recall@0.6 | F1@0.6 | Accuracy@0.6 | Precision@0.61 | Recall@0.61 | F1@0.61 | Accuracy@0.61 | Precision@0.62 | Recall@0.62 | F1@0.62 | Accuracy@0.62 | Precision@0.63 | Recall@0.63 | F1@0.63 | Accuracy@0.63 | Precision@0.64 | Recall@0.64 | F1@0.64 | Accuracy@0.64 | Precision@0.65 | Recall@0.65 | F1@0.65 | Accuracy@0.65 | Precision@0.66 | Recall@0.66 | F1@0.66 | Accuracy@0.66 | Precision@0.67 | Recall@0.67 | F1@0.67 | Accuracy@0.67 | Precision@0.68 | Recall@0.68 | F1@0.68 | Accuracy@0.68 | Precision@0.69 | Recall@0.69 | F1@0.69 | Accuracy@0.69 | Precision@0.7 | Recall@0.7 | F1@0.7 | Accuracy@0.7 | Precision@0.71 | Recall@0.71 | F1@0.71 | Accuracy@0.71 | Precision@0.72 | Recall@0.72 | F1@0.72 | Accuracy@0.72 | Precision@0.73 | Recall@0.73 | F1@0.73 | Accuracy@0.73 | Precision@0.74 | Recall@0.74 | F1@0.74 | Accuracy@0.74 | Precision@0.75 | Recall@0.75 | F1@0.75 | Accuracy@0.75 | Precision@0.76 | Recall@0.76 | F1@0.76 | Accuracy@0.76 | Precision@0.77 | Recall@0.77 | F1@0.77 | Accuracy@0.77 | Precision@0.78 | Recall@0.78 | F1@0.78 | Accuracy@0.78 | Precision@0.79 | Recall@0.79 | F1@0.79 | Accuracy@0.79 | Precision@0.8 | Recall@0.8 | F1@0.8 | Accuracy@0.8 | Precision@0.81 | Recall@0.81 | F1@0.81 | Accuracy@0.81 | Precision@0.82 | Recall@0.82 | F1@0.82 | Accuracy@0.82 | Precision@0.83 | Recall@0.83 | F1@0.83 | Accuracy@0.83 | Precision@0.84 | Recall@0.84 | F1@0.84 | Accuracy@0.84 | Precision@0.85 | Recall@0.85 | F1@0.85 | Accuracy@0.85 | Precision@0.86 | Recall@0.86 | F1@0.86 | Accuracy@0.86 | Precision@0.87 | Recall@0.87 | F1@0.87 | Accuracy@0.87 | Precision@0.88 | Recall@0.88 | F1@0.88 | Accuracy@0.88 | Precision@0.89 | Recall@0.89 | F1@0.89 | Accuracy@0.89 | Precision@0.9 | Recall@0.9 | F1@0.9 | Accuracy@0.9 | Precision@0.91 | Recall@0.91 | F1@0.91 | Accuracy@0.91 | Precision@0.92 | Recall@0.92 | F1@0.92 | Accuracy@0.92 | Precision@0.93 | Recall@0.93 | F1@0.93 | Accuracy@0.93 | Precision@0.94 | Recall@0.94 | F1@0.94 | Accuracy@0.94 | Precision@0.95 | Recall@0.95 | F1@0.95 | Accuracy@0.95 | Precision@0.96 | Recall@0.96 | F1@0.96 | Accuracy@0.96 | Precision@0.97 | Recall@0.97 | F1@0.97 | Accuracy@0.97 | Precision@0.98 | Recall@0.98 | F1@0.98 | Accuracy@0.98 | Precision@0.99 | Recall@0.99 | F1@0.99 | Accuracy@0.99 |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:-------------:|:----------:|:------:|:------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|:--------------:|:-----------:|:-------:|:-------------:|
+| 1.1673        | 1.0    | 2436 | 0.1657          | 0.8037         | 0.9994      | 0.8909  | 0.8945        | 0.8203         | 0.9993      | 0.9010  | 0.9053        | 0.8287         | 0.9991      | 0.9060  | 0.9106        | 0.8359         | 0.9989      | 0.9101  | 0.9149        | 0.8403         | 0.9987      | 0.9127  | 0.9176        | 0.8450         | 0.9985      | 0.9153  | 0.9203        | 0.8501         | 0.9983      | 0.9183  | 0.9233        | 0.8540         | 0.9981      | 0.9204  | 0.9255        | 0.8574         | 0.9980      | 0.9224  | 0.9275        | 0.8616        | 0.9979     | 0.9247 | 0.9299       | 0.8640         | 0.9979      | 0.9261  | 0.9313        | 0.8666         | 0.9979      | 0.9276  | 0.9328        | 0.8694         | 0.9978      | 0.9291  | 0.9344        | 0.8711         | 0.9976      | 0.9301  | 0.9353        | 0.8732         | 0.9974      | 0.9312  | 0.9364        | 0.8752         | 0.9974      | 0.9323  | 0.9375        | 0.8772         | 0.9974      | 0.9335  | 0.9387        | 0.8786         | 0.9973      | 0.9342  | 0.9394        | 0.8801         | 0.9973      | 0.9350  | 0.9402        | 0.8812        | 0.9971     | 0.9356 | 0.9408       | 0.8824         | 0.9970      | 0.9362  | 0.9414        | 0.8844         | 0.9969      | 0.9373  | 0.9425        | 0.8854         | 0.9967      | 0.9378  | 0.9429        | 0.8861         | 0.9966      | 0.9381  | 0.9433        | 0.8865         | 0.9966      | 0.9383  | 0.9435        | 0.8871         | 0.9966      | 0.9387  | 0.9439        | 0.8883         | 0.9965      | 0.9393  | 0.9445        | 0.8895         | 0.9965      | 0.9400  | 0.9451        | 0.8898         | 0.9965      | 0.9401  | 0.9453        | 0.8913        | 0.9964     | 0.9409 | 0.9461       | 0.8919         | 0.9963      | 0.9412  | 0.9463        | 0.8930         | 0.9961      | 0.9418  | 0.9469        | 0.8940         | 0.9961      | 0.9423  | 0.9474        | 0.8949         | 0.9960      | 0.9427  | 0.9478        | 0.8953         | 0.9959      | 0.9429  | 0.9480        | 0.8961         | 0.9959      | 0.9434  | 0.9484        | 0.8969         | 0.9959      | 0.9438  | 0.9489        | 0.8977         | 0.9958      | 0.9442  | 0.9492        | 0.8981         | 0.9956      | 0.9443  | 0.9494        | 0.8988        | 0.9955     | 0.9447 | 0.9497       | 0.8997         | 0.9954      | 0.9451  | 0.9502        | 0.9006         | 0.9954      | 0.9456  | 0.9506        | 0.9011         | 0.9954      | 0.9459  | 0.9509        | 0.9014         | 0.9954      | 0.9461  | 0.9510        | 0.9023         | 0.9954      | 0.9466  | 0.9515        | 0.9026         | 0.9954      | 0.9467  | 0.9517        | 0.9032         | 0.9954      | 0.9471  | 0.9520        | 0.9036         | 0.9954      | 0.9473  | 0.9522        | 0.9045         | 0.9952      | 0.9477  | 0.9526        | 0.9047        | 0.9950     | 0.9477 | 0.9526       | 0.9053         | 0.9950      | 0.9480  | 0.9529        | 0.9063         | 0.9950      | 0.9486  | 0.9535        | 0.9068         | 0.9950      | 0.9489  | 0.9537        | 0.9076         | 0.9950      | 0.9493  | 0.9541        | 0.9081         | 0.9950      | 0.9495  | 0.9544        | 0.9088         | 0.9950      | 0.9499  | 0.9547        | 0.9089         | 0.9950      | 0.9500  | 0.9548        | 0.9093         | 0.9949      | 0.9502  | 0.9550        | 0.9098         | 0.9949      | 0.9504  | 0.9552        | 0.9103        | 0.9949     | 0.9507 | 0.9555       | 0.9106         | 0.9948      | 0.9508  | 0.9556        | 0.9116         | 0.9948      | 0.9514  | 0.9562        | 0.9120         | 0.9945      | 0.9515  | 0.9562        | 0.9126         | 0.9944      | 0.9517  | 0.9565        | 0.9129         | 0.9941      | 0.9518  | 0.9566        | 0.9135         | 0.9940      | 0.9521  | 0.9568        | 0.9144         | 0.9939      | 0.9525  | 0.9572        | 0.9149         | 0.9939      | 0.9527  | 0.9575        | 0.9152         | 0.9939      | 0.9529  | 0.9577        | 0.9158        | 0.9938     | 0.9532 | 0.9579       | 0.9159         | 0.9936      | 0.9532  | 0.9579        | 0.9169         | 0.9934      | 0.9536  | 0.9583        | 0.9175         | 0.9931      | 0.9538  | 0.9585        | 0.9180         | 0.9931      | 0.9541  | 0.9588        | 0.9188         | 0.9929      | 0.9544  | 0.9591        | 0.9198         | 0.9927      | 0.9549  | 0.9595        | 0.9209         | 0.9926      | 0.9554  | 0.9600        | 0.9216         | 0.9925      | 0.9557  | 0.9603        | 0.9223         | 0.9923      | 0.9560  | 0.9606        | 0.9239        | 0.9923     | 0.9569 | 0.9615       | 0.9247         | 0.9922      | 0.9573  | 0.9618        | 0.9251         | 0.9921      | 0.9575  | 0.9620        | 0.9264         | 0.9919      | 0.9580  | 0.9625        | 0.9272         | 0.9915      | 0.9583  | 0.9628        | 0.9285         | 0.9914      | 0.9589  | 0.9633        | 0.9294         | 0.9911      | 0.9593  | 0.9637        | 0.9306         | 0.9907      | 0.9597  | 0.9641        | 0.9316         | 0.9901      | 0.9599  | 0.9644        | 0.9324         | 0.9894      | 0.9600  | 0.9644        | 0.9338        | 0.9888     | 0.9605 | 0.9649       | 0.9359         | 0.9880      | 0.9612  | 0.9656        | 0.9380         | 0.9871      | 0.9619  | 0.9663        | 0.9407         | 0.9860      | 0.9628  | 0.9671        | 0.9436         | 0.9854      | 0.9640  | 0.9683        | 0.9471         | 0.9839      | 0.9652  | 0.9694        | 0.9521         | 0.9818      | 0.9667  | 0.9708        | 0.9577         | 0.9781      | 0.9678  | 0.9719        | 0.9653         | 0.9709      | 0.9681  | 0.9724        | 0.9751         | 0.9542      | 0.9645  | 0.9697        |
+| 0.7107        | 2.0    | 4872 | 0.1609          | 0.9044         | 0.9943      | 0.9472  | 0.9522        | 0.9147         | 0.9927      | 0.9521  | 0.9570        | 0.9185         | 0.9916      | 0.9536  | 0.9584        | 0.9221         | 0.9911      | 0.9553  | 0.9600        | 0.9244         | 0.9905      | 0.9563  | 0.9610        | 0.9268         | 0.9898      | 0.9572  | 0.9618        | 0.9278         | 0.9895      | 0.9576  | 0.9622        | 0.9293         | 0.9894      | 0.9584  | 0.9629        | 0.9299         | 0.9891      | 0.9586  | 0.9631        | 0.9309        | 0.9889     | 0.9590 | 0.9635       | 0.9314         | 0.9885      | 0.9591  | 0.9637        | 0.9322         | 0.9884      | 0.9595  | 0.9640        | 0.9325         | 0.9884      | 0.9596  | 0.9641        | 0.9328         | 0.9883      | 0.9598  | 0.9643        | 0.9334         | 0.9882      | 0.9600  | 0.9645        | 0.9336         | 0.9882      | 0.9602  | 0.9646        | 0.9341         | 0.9881      | 0.9603  | 0.9648        | 0.9346         | 0.9880      | 0.9606  | 0.9650        | 0.9351         | 0.9880      | 0.9608  | 0.9652        | 0.9354        | 0.9875     | 0.9608 | 0.9652       | 0.9357         | 0.9874      | 0.9608  | 0.9653        | 0.9357         | 0.9873      | 0.9608  | 0.9653        | 0.9359         | 0.9873      | 0.9609  | 0.9654        | 0.9362         | 0.9872      | 0.9610  | 0.9655        | 0.9368         | 0.9871      | 0.9613  | 0.9657        | 0.9370         | 0.9871      | 0.9614  | 0.9658        | 0.9370         | 0.9870      | 0.9614  | 0.9658        | 0.9375         | 0.9870      | 0.9616  | 0.9660        | 0.9378         | 0.9870      | 0.9618  | 0.9662        | 0.9381        | 0.9869     | 0.9619 | 0.9663       | 0.9385         | 0.9869      | 0.9621  | 0.9665        | 0.9388         | 0.9868      | 0.9622  | 0.9666        | 0.9390         | 0.9867      | 0.9623  | 0.9666        | 0.9392         | 0.9866      | 0.9623  | 0.9667        | 0.9393         | 0.9865      | 0.9623  | 0.9667        | 0.9394         | 0.9865      | 0.9624  | 0.9667        | 0.9397         | 0.9865      | 0.9625  | 0.9669        | 0.9397         | 0.9864      | 0.9625  | 0.9668        | 0.9399         | 0.9862      | 0.9625  | 0.9668        | 0.9400        | 0.9862     | 0.9625 | 0.9669       | 0.9400         | 0.9861      | 0.9625  | 0.9669        | 0.9402         | 0.9861      | 0.9626  | 0.9670        | 0.9404         | 0.9860      | 0.9627  | 0.9670        | 0.9407         | 0.9858      | 0.9627  | 0.9671        | 0.9410         | 0.9857      | 0.9628  | 0.9672        | 0.9412         | 0.9856      | 0.9629  | 0.9672        | 0.9415         | 0.9856      | 0.9630  | 0.9674        | 0.9416         | 0.9855      | 0.9630  | 0.9674        | 0.9423         | 0.9854      | 0.9634  | 0.9677        | 0.9424        | 0.9853     | 0.9634 | 0.9677       | 0.9427         | 0.9852      | 0.9634  | 0.9678        | 0.9428         | 0.9850      | 0.9634  | 0.9678        | 0.9430         | 0.9849      | 0.9635  | 0.9678        | 0.9434         | 0.9848      | 0.9636  | 0.9679        | 0.9436         | 0.9847      | 0.9637  | 0.9680        | 0.9436         | 0.9847      | 0.9637  | 0.9680        | 0.9438         | 0.9847      | 0.9638  | 0.9681        | 0.9438         | 0.9846      | 0.9638  | 0.9681        | 0.9442         | 0.9844      | 0.9639  | 0.9682        | 0.9445        | 0.9844     | 0.9640 | 0.9683       | 0.9447         | 0.9843      | 0.9641  | 0.9684        | 0.9449         | 0.9843      | 0.9642  | 0.9685        | 0.9450         | 0.9840      | 0.9641  | 0.9684        | 0.9452         | 0.9838      | 0.9641  | 0.9684        | 0.9452         | 0.9838      | 0.9641  | 0.9684        | 0.9452         | 0.9837      | 0.9641  | 0.9684        | 0.9453         | 0.9834      | 0.9640  | 0.9683        | 0.9453         | 0.9833      | 0.9640  | 0.9683        | 0.9456         | 0.9832      | 0.9641  | 0.9684        | 0.9457        | 0.9832     | 0.9641 | 0.9684       | 0.9458         | 0.9831      | 0.9641  | 0.9684        | 0.9462         | 0.9830      | 0.9642  | 0.9686        | 0.9464         | 0.9829      | 0.9643  | 0.9686        | 0.9467         | 0.9825      | 0.9643  | 0.9686        | 0.9470         | 0.9824      | 0.9644  | 0.9687        | 0.9474         | 0.9822      | 0.9645  | 0.9688        | 0.9477         | 0.9820      | 0.9645  | 0.9689        | 0.9480         | 0.9816      | 0.9645  | 0.9688        | 0.9482         | 0.9815      | 0.9646  | 0.9689        | 0.9484        | 0.9814     | 0.9646 | 0.9689       | 0.9489         | 0.9812      | 0.9648  | 0.9691        | 0.9494         | 0.9807      | 0.9648  | 0.9691        | 0.9496         | 0.9805      | 0.9648  | 0.9691        | 0.9500         | 0.9803      | 0.9649  | 0.9692        | 0.9505         | 0.9800      | 0.9650  | 0.9693        | 0.9511         | 0.9794      | 0.9651  | 0.9694        | 0.9515         | 0.9790      | 0.9651  | 0.9694        | 0.9519         | 0.9787      | 0.9651  | 0.9695        | 0.9525         | 0.9782      | 0.9652  | 0.9696        | 0.9532        | 0.9776     | 0.9653 | 0.9697       | 0.9537         | 0.9772      | 0.9653  | 0.9697        | 0.9545         | 0.9772      | 0.9657  | 0.9701        | 0.9547         | 0.9769      | 0.9657  | 0.9701        | 0.9558         | 0.9763      | 0.9660  | 0.9703        | 0.9568         | 0.9755      | 0.9660  | 0.9704        | 0.9588         | 0.9736      | 0.9662  | 0.9706        | 0.9610         | 0.9712      | 0.9661  | 0.9706        | 0.9640         | 0.9680      | 0.9660  | 0.9706        | 0.9694         | 0.9621      | 0.9657  | 0.9705        |
+| 0.2941        | 2.9991 | 7305 | 0.1709          | 0.9292         | 0.9890      | 0.9581  | 0.9627        | 0.9339         | 0.9873      | 0.9599  | 0.9644        | 0.9363         | 0.9867      | 0.9608  | 0.9653        | 0.9374         | 0.9866      | 0.9614  | 0.9658        | 0.9379         | 0.9865      | 0.9616  | 0.9660        | 0.9382         | 0.9862      | 0.9616  | 0.9660        | 0.9388         | 0.9859      | 0.9617  | 0.9662        | 0.9393         | 0.9859      | 0.9620  | 0.9664        | 0.9395         | 0.9858      | 0.9621  | 0.9665        | 0.9397        | 0.9857     | 0.9622 | 0.9666       | 0.9400         | 0.9856      | 0.9623  | 0.9667        | 0.9403         | 0.9856      | 0.9624  | 0.9668        | 0.9406         | 0.9855      | 0.9625  | 0.9669        | 0.9407         | 0.9855      | 0.9626  | 0.9670        | 0.9408         | 0.9855      | 0.9626  | 0.9670        | 0.9411         | 0.9855      | 0.9628  | 0.9671        | 0.9414         | 0.9853      | 0.9628  | 0.9672        | 0.9415         | 0.9852      | 0.9628  | 0.9672        | 0.9416         | 0.9850      | 0.9628  | 0.9672        | 0.9416        | 0.9850     | 0.9628 | 0.9672       | 0.9416         | 0.9849      | 0.9628  | 0.9671        | 0.9419         | 0.9849      | 0.9629  | 0.9673        | 0.9421         | 0.9849      | 0.9630  | 0.9674        | 0.9421         | 0.9849      | 0.9630  | 0.9674        | 0.9422         | 0.9846      | 0.9629  | 0.9673        | 0.9422         | 0.9846      | 0.9629  | 0.9673        | 0.9423         | 0.9846      | 0.9629  | 0.9673        | 0.9423         | 0.9845      | 0.9629  | 0.9673        | 0.9424         | 0.9844      | 0.9629  | 0.9673        | 0.9425        | 0.9843     | 0.9629 | 0.9673       | 0.9425         | 0.9843      | 0.9629  | 0.9673        | 0.9428         | 0.9841      | 0.9630  | 0.9674        | 0.9428         | 0.9840      | 0.9630  | 0.9674        | 0.9429         | 0.9839      | 0.9630  | 0.9674        | 0.9431         | 0.9838      | 0.9630  | 0.9674        | 0.9431         | 0.9838      | 0.9631  | 0.9674        | 0.9432         | 0.9837      | 0.9631  | 0.9674        | 0.9432         | 0.9837      | 0.9631  | 0.9674        | 0.9432         | 0.9837      | 0.9631  | 0.9674        | 0.9433        | 0.9836     | 0.9631 | 0.9674       | 0.9433         | 0.9835      | 0.9630  | 0.9674        | 0.9433         | 0.9835      | 0.9630  | 0.9674        | 0.9434         | 0.9834      | 0.9630  | 0.9674        | 0.9435         | 0.9834      | 0.9630  | 0.9674        | 0.9436         | 0.9832      | 0.9630  | 0.9674        | 0.9438         | 0.9832      | 0.9631  | 0.9675        | 0.9438         | 0.9831      | 0.9631  | 0.9675        | 0.9440         | 0.9831      | 0.9632  | 0.9676        | 0.9442         | 0.9831      | 0.9633  | 0.9677        | 0.9445        | 0.9830     | 0.9634 | 0.9678       | 0.9446         | 0.9829      | 0.9634  | 0.9678        | 0.9447         | 0.9829      | 0.9634  | 0.9678        | 0.9448         | 0.9828      | 0.9635  | 0.9678        | 0.9449         | 0.9827      | 0.9635  | 0.9678        | 0.9449         | 0.9827      | 0.9635  | 0.9678        | 0.9450         | 0.9826      | 0.9635  | 0.9678        | 0.9451         | 0.9826      | 0.9635  | 0.9679        | 0.9453         | 0.9826      | 0.9636  | 0.9680        | 0.9456         | 0.9826      | 0.9637  | 0.9681        | 0.9457        | 0.9825     | 0.9637 | 0.9681       | 0.9457         | 0.9825      | 0.9637  | 0.9681        | 0.9459         | 0.9824      | 0.9638  | 0.9682        | 0.9459         | 0.9824      | 0.9638  | 0.9682        | 0.9460         | 0.9824      | 0.9639  | 0.9682        | 0.9461         | 0.9824      | 0.9639  | 0.9683        | 0.9462         | 0.9822      | 0.9639  | 0.9682        | 0.9463         | 0.9822      | 0.9639  | 0.9683        | 0.9464         | 0.9820      | 0.9639  | 0.9682        | 0.9464         | 0.9817      | 0.9637  | 0.9681        | 0.9465        | 0.9817     | 0.9638 | 0.9682       | 0.9466         | 0.9817      | 0.9638  | 0.9682        | 0.9467         | 0.9816      | 0.9639  | 0.9682        | 0.9467         | 0.9816      | 0.9639  | 0.9682        | 0.9468         | 0.9815      | 0.9638  | 0.9682        | 0.9471         | 0.9815      | 0.9640  | 0.9684        | 0.9475         | 0.9815      | 0.9642  | 0.9686        | 0.9476         | 0.9815      | 0.9642  | 0.9686        | 0.9478         | 0.9813      | 0.9643  | 0.9686        | 0.9479         | 0.9812      | 0.9643  | 0.9686        | 0.9479        | 0.9812     | 0.9643 | 0.9686       | 0.9480         | 0.9811      | 0.9643  | 0.9686        | 0.9481         | 0.9809      | 0.9642  | 0.9686        | 0.9484         | 0.9809      | 0.9644  | 0.9687        | 0.9487         | 0.9807      | 0.9644  | 0.9688        | 0.9489         | 0.9806      | 0.9645  | 0.9689        | 0.9492         | 0.9805      | 0.9646  | 0.9689        | 0.9497         | 0.9805      | 0.9648  | 0.9692        | 0.9500         | 0.9801      | 0.9648  | 0.9692        | 0.9502         | 0.9798      | 0.9648  | 0.9691        | 0.9505        | 0.9797     | 0.9649 | 0.9692       | 0.9506         | 0.9794      | 0.9648  | 0.9692        | 0.9509         | 0.9792      | 0.9649  | 0.9693        | 0.9519         | 0.9790      | 0.9653  | 0.9696        | 0.9525         | 0.9782      | 0.9652  | 0.9696        | 0.9536         | 0.9774      | 0.9654  | 0.9697        | 0.9547         | 0.9761      | 0.9653  | 0.9697        | 0.9565         | 0.9745      | 0.9655  | 0.9699        | 0.9615         | 0.9707      | 0.9661  | 0.9706        | 0.9671         | 0.9612      | 0.9642  | 0.9692        |
+
+
+### Framework versions
+
+- Transformers 4.48.0.dev0
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

best_model/config.json

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+{
+  "_name_or_path": "answerdotai/ModernBERT-large",
+  "architectures": [
+    "ModernBertForSequenceClassification"
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
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+  "decoder_bias": true,
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+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
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+  "global_rope_theta": 160000.0,
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+  "hidden_activation": "gelu",
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+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "reference_compile": true,
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+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0",
+  "vocab_size": 50368
+}

best_model/model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7200930050afb9f26d83238dc57c3e95818a8baf581b2a89ed6aade257b0060a
+size 1583351632

best_model/special_tokens_map.json

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+{
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+    "content": "[CLS]",
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "normalized": false,
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+  },
+  "pad_token": {
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+    "lstrip": false,
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+    "single_word": false
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+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

best_model/tokenizer_config.json

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