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roberta-large-ner-qlorafinetune-runs-colab

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@@ -5,7 +5,7 @@ base_model: FacebookAI/xlm-roberta-large
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  tags:
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  - generated_from_trainer
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  datasets:
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- - biobert_json
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  metrics:
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  - precision
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  - recall
@@ -21,13 +21,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # roberta-large-ner-qlorafinetune-runs-colab
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24
- This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the biobert_json dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0688
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- - Precision: 0.9390
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- - Recall: 0.9598
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- - F1: 0.9493
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- - Accuracy: 0.9821
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32
  ## Model description
33
 
@@ -50,86 +50,112 @@ The following hyperparameters were used during training:
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  - train_batch_size: 32
51
  - eval_batch_size: 32
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  - seed: 42
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- - optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
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- - training_steps: 1300
56
  - mixed_precision_training: Native AMP
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58
  ### Training results
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
61
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
62
- | 2.4626 | 0.0654 | 20 | 0.9421 | 0.4829 | 0.1165 | 0.1877 | 0.7544 |
63
- | 0.7547 | 0.1307 | 40 | 0.3993 | 0.7557 | 0.6814 | 0.7166 | 0.8940 |
64
- | 0.4022 | 0.1961 | 60 | 0.2119 | 0.8276 | 0.8158 | 0.8217 | 0.9396 |
65
- | 0.2732 | 0.2614 | 80 | 0.1631 | 0.8250 | 0.8746 | 0.8491 | 0.9512 |
66
- | 0.2083 | 0.3268 | 100 | 0.1423 | 0.8591 | 0.9037 | 0.8808 | 0.9576 |
67
- | 0.2216 | 0.3922 | 120 | 0.1392 | 0.8562 | 0.9147 | 0.8845 | 0.9572 |
68
- | 0.1787 | 0.4575 | 140 | 0.1114 | 0.8940 | 0.9173 | 0.9055 | 0.9664 |
69
- | 0.1642 | 0.5229 | 160 | 0.1191 | 0.8840 | 0.9270 | 0.9050 | 0.9657 |
70
- | 0.1557 | 0.5882 | 180 | 0.1089 | 0.8825 | 0.9284 | 0.9049 | 0.9665 |
71
- | 0.1406 | 0.6536 | 200 | 0.0982 | 0.8967 | 0.9279 | 0.9121 | 0.9700 |
72
- | 0.1359 | 0.7190 | 220 | 0.0879 | 0.9182 | 0.9269 | 0.9225 | 0.9733 |
73
- | 0.1272 | 0.7843 | 240 | 0.1047 | 0.8940 | 0.9506 | 0.9214 | 0.9697 |
74
- | 0.1157 | 0.8497 | 260 | 0.0985 | 0.9198 | 0.9266 | 0.9232 | 0.9719 |
75
- | 0.1191 | 0.9150 | 280 | 0.1166 | 0.8827 | 0.9427 | 0.9117 | 0.9656 |
76
- | 0.1298 | 0.9804 | 300 | 0.0878 | 0.9211 | 0.9315 | 0.9263 | 0.9736 |
77
- | 0.1107 | 1.0458 | 320 | 0.0834 | 0.9205 | 0.9512 | 0.9356 | 0.9762 |
78
- | 0.0942 | 1.1111 | 340 | 0.0874 | 0.9097 | 0.9574 | 0.9329 | 0.9745 |
79
- | 0.0979 | 1.1765 | 360 | 0.0771 | 0.9259 | 0.9518 | 0.9387 | 0.9779 |
80
- | 0.0971 | 1.2418 | 380 | 0.0814 | 0.9280 | 0.9478 | 0.9378 | 0.9781 |
81
- | 0.1053 | 1.3072 | 400 | 0.0804 | 0.9214 | 0.9399 | 0.9306 | 0.9761 |
82
- | 0.1075 | 1.3725 | 420 | 0.0835 | 0.9083 | 0.9369 | 0.9224 | 0.9738 |
83
- | 0.0893 | 1.4379 | 440 | 0.0773 | 0.9329 | 0.9469 | 0.9398 | 0.9784 |
84
- | 0.09 | 1.5033 | 460 | 0.0737 | 0.9316 | 0.9522 | 0.9418 | 0.9787 |
85
- | 0.0947 | 1.5686 | 480 | 0.0787 | 0.9141 | 0.9549 | 0.9340 | 0.9763 |
86
- | 0.0907 | 1.6340 | 500 | 0.0813 | 0.9179 | 0.9522 | 0.9347 | 0.9770 |
87
- | 0.0752 | 1.6993 | 520 | 0.0802 | 0.9130 | 0.9575 | 0.9347 | 0.9772 |
88
- | 0.0801 | 1.7647 | 540 | 0.0703 | 0.9302 | 0.9530 | 0.9415 | 0.9797 |
89
- | 0.092 | 1.8301 | 560 | 0.0739 | 0.9301 | 0.9513 | 0.9406 | 0.9785 |
90
- | 0.0862 | 1.8954 | 580 | 0.0899 | 0.9034 | 0.9526 | 0.9274 | 0.9735 |
91
- | 0.0869 | 1.9608 | 600 | 0.0782 | 0.9164 | 0.9510 | 0.9334 | 0.9765 |
92
- | 0.0713 | 2.0261 | 620 | 0.0771 | 0.9225 | 0.9579 | 0.9399 | 0.9785 |
93
- | 0.0635 | 2.0915 | 640 | 0.0729 | 0.9356 | 0.9524 | 0.9439 | 0.9797 |
94
- | 0.0527 | 2.1569 | 660 | 0.0764 | 0.9088 | 0.9475 | 0.9277 | 0.9765 |
95
- | 0.0738 | 2.2222 | 680 | 0.0747 | 0.9233 | 0.9576 | 0.9401 | 0.9783 |
96
- | 0.0628 | 2.2876 | 700 | 0.0751 | 0.9334 | 0.9589 | 0.9460 | 0.9801 |
97
- | 0.0574 | 2.3529 | 720 | 0.0713 | 0.9354 | 0.9580 | 0.9465 | 0.9807 |
98
- | 0.0628 | 2.4183 | 740 | 0.0700 | 0.9347 | 0.9540 | 0.9443 | 0.9809 |
99
- | 0.0771 | 2.4837 | 760 | 0.0707 | 0.9326 | 0.9607 | 0.9465 | 0.9811 |
100
- | 0.068 | 2.5490 | 780 | 0.0753 | 0.9318 | 0.9648 | 0.9480 | 0.9807 |
101
- | 0.0653 | 2.6144 | 800 | 0.0680 | 0.9400 | 0.9583 | 0.9491 | 0.9820 |
102
- | 0.0567 | 2.6797 | 820 | 0.0762 | 0.9327 | 0.9540 | 0.9433 | 0.9791 |
103
- | 0.066 | 2.7451 | 840 | 0.0719 | 0.9297 | 0.9570 | 0.9431 | 0.9805 |
104
- | 0.0576 | 2.8105 | 860 | 0.0723 | 0.9360 | 0.9597 | 0.9477 | 0.9808 |
105
- | 0.0608 | 2.8758 | 880 | 0.0744 | 0.9309 | 0.9566 | 0.9436 | 0.9791 |
106
- | 0.0521 | 2.9412 | 900 | 0.0679 | 0.9355 | 0.9599 | 0.9475 | 0.9814 |
107
- | 0.051 | 3.0065 | 920 | 0.0688 | 0.9373 | 0.9594 | 0.9482 | 0.9818 |
108
- | 0.0444 | 3.0719 | 940 | 0.0723 | 0.9335 | 0.9607 | 0.9469 | 0.9814 |
109
- | 0.0468 | 3.1373 | 960 | 0.0767 | 0.9246 | 0.9554 | 0.9397 | 0.9787 |
110
- | 0.0433 | 3.2026 | 980 | 0.0681 | 0.9376 | 0.9591 | 0.9482 | 0.9819 |
111
- | 0.0468 | 3.2680 | 1000 | 0.0722 | 0.9318 | 0.9589 | 0.9452 | 0.9808 |
112
- | 0.0496 | 3.3333 | 1020 | 0.0708 | 0.9341 | 0.9496 | 0.9418 | 0.9803 |
113
- | 0.0473 | 3.3987 | 1040 | 0.0699 | 0.9315 | 0.9666 | 0.9487 | 0.9819 |
114
- | 0.0534 | 3.4641 | 1060 | 0.0675 | 0.9368 | 0.9569 | 0.9468 | 0.9819 |
115
- | 0.0421 | 3.5294 | 1080 | 0.0698 | 0.9322 | 0.9564 | 0.9442 | 0.9809 |
116
- | 0.0444 | 3.5948 | 1100 | 0.0715 | 0.9303 | 0.9539 | 0.9420 | 0.9799 |
117
- | 0.0366 | 3.6601 | 1120 | 0.0671 | 0.9382 | 0.9615 | 0.9497 | 0.9823 |
118
- | 0.0505 | 3.7255 | 1140 | 0.0687 | 0.9376 | 0.9554 | 0.9464 | 0.9814 |
119
- | 0.0431 | 3.7908 | 1160 | 0.0698 | 0.9338 | 0.9594 | 0.9465 | 0.9813 |
120
- | 0.0519 | 3.8562 | 1180 | 0.0696 | 0.9378 | 0.9604 | 0.9490 | 0.9820 |
121
- | 0.0471 | 3.9216 | 1200 | 0.0712 | 0.9380 | 0.9599 | 0.9488 | 0.9817 |
122
- | 0.0544 | 3.9869 | 1220 | 0.0688 | 0.9407 | 0.9588 | 0.9497 | 0.9819 |
123
- | 0.0392 | 4.0523 | 1240 | 0.0688 | 0.9389 | 0.9599 | 0.9493 | 0.9822 |
124
- | 0.0303 | 4.1176 | 1260 | 0.0698 | 0.9376 | 0.9601 | 0.9487 | 0.9817 |
125
- | 0.0383 | 4.1830 | 1280 | 0.0689 | 0.9393 | 0.9605 | 0.9498 | 0.9821 |
126
- | 0.0389 | 4.2484 | 1300 | 0.0688 | 0.9390 | 0.9598 | 0.9493 | 0.9821 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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129
  ### Framework versions
130
 
131
  - PEFT 0.14.0
132
- - Transformers 4.47.1
133
- - Pytorch 2.5.1+cu121
134
- - Datasets 3.2.0
135
  - Tokenizers 0.21.0
 
5
  tags:
6
  - generated_from_trainer
7
  datasets:
8
+ - conll2002
9
  metrics:
10
  - precision
11
  - recall
 
21
 
22
  # roberta-large-ner-qlorafinetune-runs-colab
23
 
24
+ This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the conll2002 dataset.
25
  It achieves the following results on the evaluation set:
26
+ - Loss: 0.0862
27
+ - Precision: 0.8825
28
+ - Recall: 0.8853
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+ - F1: 0.8839
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+ - Accuracy: 0.9813
31
 
32
  ## Model description
33
 
 
50
  - train_batch_size: 32
51
  - eval_batch_size: 32
52
  - seed: 42
53
+ - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
54
  - lr_scheduler_type: linear
55
+ - training_steps: 1820
56
  - mixed_precision_training: Native AMP
57
 
58
  ### Training results
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
61
  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
62
+ | 1.335 | 0.0766 | 20 | 0.4433 | 0.1426 | 0.0857 | 0.1071 | 0.8719 |
63
+ | 0.3122 | 0.1533 | 40 | 0.2540 | 0.4993 | 0.5722 | 0.5332 | 0.9326 |
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+ | 0.1508 | 0.2299 | 60 | 0.1346 | 0.7299 | 0.7569 | 0.7431 | 0.9647 |
65
+ | 0.0954 | 0.3065 | 80 | 0.1159 | 0.7791 | 0.8024 | 0.7906 | 0.9708 |
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+ | 0.095 | 0.3831 | 100 | 0.0957 | 0.8206 | 0.8323 | 0.8264 | 0.9747 |
67
+ | 0.085 | 0.4598 | 120 | 0.0941 | 0.8307 | 0.8447 | 0.8376 | 0.9762 |
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+ | 0.0963 | 0.5364 | 140 | 0.0859 | 0.8188 | 0.8421 | 0.8303 | 0.9769 |
69
+ | 0.0861 | 0.6130 | 160 | 0.0863 | 0.8080 | 0.8449 | 0.8260 | 0.9755 |
70
+ | 0.0775 | 0.6897 | 180 | 0.0798 | 0.8417 | 0.8587 | 0.8501 | 0.9790 |
71
+ | 0.0706 | 0.7663 | 200 | 0.0806 | 0.8446 | 0.8428 | 0.8437 | 0.9775 |
72
+ | 0.0613 | 0.8429 | 220 | 0.0805 | 0.8377 | 0.8573 | 0.8474 | 0.9786 |
73
+ | 0.0609 | 0.9195 | 240 | 0.0805 | 0.8395 | 0.8483 | 0.8439 | 0.9774 |
74
+ | 0.054 | 0.9962 | 260 | 0.0819 | 0.8529 | 0.8683 | 0.8605 | 0.9786 |
75
+ | 0.0449 | 1.0728 | 280 | 0.0789 | 0.8353 | 0.8660 | 0.8504 | 0.9799 |
76
+ | 0.0504 | 1.1494 | 300 | 0.0769 | 0.8571 | 0.8686 | 0.8628 | 0.9788 |
77
+ | 0.0568 | 1.2261 | 320 | 0.0803 | 0.8719 | 0.8771 | 0.8745 | 0.9798 |
78
+ | 0.0528 | 1.3027 | 340 | 0.1051 | 0.7653 | 0.7852 | 0.7751 | 0.9691 |
79
+ | 0.0484 | 1.3793 | 360 | 0.1179 | 0.7920 | 0.7989 | 0.7955 | 0.9724 |
80
+ | 0.0531 | 1.4559 | 380 | 0.1006 | 0.8043 | 0.8148 | 0.8095 | 0.9728 |
81
+ | 0.0521 | 1.5326 | 400 | 0.0945 | 0.8060 | 0.8394 | 0.8224 | 0.9742 |
82
+ | 0.0574 | 1.6092 | 420 | 0.0840 | 0.8166 | 0.8493 | 0.8326 | 0.9774 |
83
+ | 0.07 | 1.6858 | 440 | 0.0772 | 0.8262 | 0.8500 | 0.8379 | 0.9782 |
84
+ | 0.0657 | 1.7625 | 460 | 0.0745 | 0.8573 | 0.8601 | 0.8587 | 0.9787 |
85
+ | 0.0512 | 1.8391 | 480 | 0.0795 | 0.8320 | 0.8513 | 0.8416 | 0.9780 |
86
+ | 0.0648 | 1.9157 | 500 | 0.0670 | 0.8427 | 0.8764 | 0.8592 | 0.9804 |
87
+ | 0.0438 | 1.9923 | 520 | 0.0726 | 0.8530 | 0.8640 | 0.8584 | 0.9799 |
88
+ | 0.0604 | 2.0690 | 540 | 0.0711 | 0.8525 | 0.8752 | 0.8637 | 0.9792 |
89
+ | 0.0379 | 2.1456 | 560 | 0.0724 | 0.8573 | 0.8725 | 0.8648 | 0.9792 |
90
+ | 0.0323 | 2.2222 | 580 | 0.0719 | 0.8625 | 0.8720 | 0.8672 | 0.9799 |
91
+ | 0.0389 | 2.2989 | 600 | 0.0772 | 0.8477 | 0.8681 | 0.8578 | 0.9791 |
92
+ | 0.0333 | 2.3755 | 620 | 0.0759 | 0.8407 | 0.8571 | 0.8488 | 0.9784 |
93
+ | 0.0345 | 2.4521 | 640 | 0.0758 | 0.8473 | 0.8693 | 0.8581 | 0.9802 |
94
+ | 0.0366 | 2.5287 | 660 | 0.0730 | 0.8562 | 0.8644 | 0.8603 | 0.9790 |
95
+ | 0.0414 | 2.6054 | 680 | 0.0820 | 0.8548 | 0.8631 | 0.8589 | 0.9780 |
96
+ | 0.0392 | 2.6820 | 700 | 0.0773 | 0.8549 | 0.8649 | 0.8599 | 0.9780 |
97
+ | 0.0353 | 2.7586 | 720 | 0.0707 | 0.8549 | 0.8653 | 0.8601 | 0.9794 |
98
+ | 0.0325 | 2.8352 | 740 | 0.0717 | 0.8595 | 0.8686 | 0.8640 | 0.9797 |
99
+ | 0.0337 | 2.9119 | 760 | 0.0752 | 0.8650 | 0.8761 | 0.8705 | 0.9803 |
100
+ | 0.0405 | 2.9885 | 780 | 0.0698 | 0.8623 | 0.8720 | 0.8671 | 0.9799 |
101
+ | 0.0407 | 3.0651 | 800 | 0.0805 | 0.8557 | 0.8791 | 0.8673 | 0.9817 |
102
+ | 0.0288 | 3.1418 | 820 | 0.0691 | 0.8753 | 0.8821 | 0.8787 | 0.9807 |
103
+ | 0.0277 | 3.2184 | 840 | 0.0829 | 0.8588 | 0.8775 | 0.8681 | 0.9799 |
104
+ | 0.0264 | 3.2950 | 860 | 0.0725 | 0.8699 | 0.8773 | 0.8736 | 0.9801 |
105
+ | 0.0248 | 3.3716 | 880 | 0.0749 | 0.8568 | 0.8716 | 0.8641 | 0.9794 |
106
+ | 0.0267 | 3.4483 | 900 | 0.0740 | 0.8587 | 0.8716 | 0.8651 | 0.9793 |
107
+ | 0.0295 | 3.5249 | 920 | 0.0701 | 0.8691 | 0.8833 | 0.8761 | 0.9812 |
108
+ | 0.0231 | 3.6015 | 940 | 0.0704 | 0.8710 | 0.8794 | 0.8751 | 0.9815 |
109
+ | 0.0256 | 3.6782 | 960 | 0.0722 | 0.8758 | 0.8865 | 0.8811 | 0.9814 |
110
+ | 0.0223 | 3.7548 | 980 | 0.0721 | 0.8756 | 0.8849 | 0.8802 | 0.9807 |
111
+ | 0.0312 | 3.8314 | 1000 | 0.0773 | 0.8667 | 0.8736 | 0.8701 | 0.9802 |
112
+ | 0.0275 | 3.9080 | 1020 | 0.0744 | 0.8655 | 0.8766 | 0.8710 | 0.9800 |
113
+ | 0.0323 | 3.9847 | 1040 | 0.0738 | 0.8819 | 0.8819 | 0.8819 | 0.9813 |
114
+ | 0.0193 | 4.0613 | 1060 | 0.0772 | 0.8787 | 0.8853 | 0.8820 | 0.9811 |
115
+ | 0.0213 | 4.1379 | 1080 | 0.0806 | 0.8778 | 0.8801 | 0.8789 | 0.9812 |
116
+ | 0.0177 | 4.2146 | 1100 | 0.0785 | 0.8723 | 0.8789 | 0.8756 | 0.9807 |
117
+ | 0.0193 | 4.2912 | 1120 | 0.0808 | 0.8764 | 0.8817 | 0.8790 | 0.9810 |
118
+ | 0.0191 | 4.3678 | 1140 | 0.0728 | 0.8783 | 0.8936 | 0.8859 | 0.9816 |
119
+ | 0.0222 | 4.4444 | 1160 | 0.0772 | 0.8754 | 0.8830 | 0.8792 | 0.9811 |
120
+ | 0.0189 | 4.5211 | 1180 | 0.0763 | 0.8795 | 0.8892 | 0.8844 | 0.9812 |
121
+ | 0.0185 | 4.5977 | 1200 | 0.0793 | 0.8808 | 0.8895 | 0.8851 | 0.9820 |
122
+ | 0.0195 | 4.6743 | 1220 | 0.0796 | 0.8783 | 0.8874 | 0.8828 | 0.9820 |
123
+ | 0.0248 | 4.7510 | 1240 | 0.0765 | 0.8659 | 0.8711 | 0.8685 | 0.9805 |
124
+ | 0.0205 | 4.8276 | 1260 | 0.0786 | 0.8667 | 0.8693 | 0.8680 | 0.9801 |
125
+ | 0.0186 | 4.9042 | 1280 | 0.0799 | 0.8684 | 0.875 | 0.8717 | 0.9804 |
126
+ | 0.0162 | 4.9808 | 1300 | 0.0780 | 0.8824 | 0.8828 | 0.8826 | 0.9816 |
127
+ | 0.0147 | 5.0575 | 1320 | 0.0787 | 0.8767 | 0.8869 | 0.8818 | 0.9819 |
128
+ | 0.013 | 5.1341 | 1340 | 0.0823 | 0.8777 | 0.8837 | 0.8807 | 0.9811 |
129
+ | 0.0126 | 5.2107 | 1360 | 0.0826 | 0.8796 | 0.8849 | 0.8822 | 0.9811 |
130
+ | 0.0149 | 5.2874 | 1380 | 0.0869 | 0.8783 | 0.8771 | 0.8777 | 0.9803 |
131
+ | 0.0126 | 5.3640 | 1400 | 0.0859 | 0.8708 | 0.875 | 0.8729 | 0.9804 |
132
+ | 0.0158 | 5.4406 | 1420 | 0.0842 | 0.8738 | 0.8782 | 0.8760 | 0.9802 |
133
+ | 0.0135 | 5.5172 | 1440 | 0.0839 | 0.8777 | 0.8805 | 0.8791 | 0.9806 |
134
+ | 0.0173 | 5.5939 | 1460 | 0.0866 | 0.8711 | 0.8761 | 0.8736 | 0.9800 |
135
+ | 0.0124 | 5.6705 | 1480 | 0.0831 | 0.8715 | 0.8819 | 0.8767 | 0.9807 |
136
+ | 0.012 | 5.7471 | 1500 | 0.0827 | 0.8801 | 0.8858 | 0.8830 | 0.9813 |
137
+ | 0.0147 | 5.8238 | 1520 | 0.0825 | 0.8784 | 0.8847 | 0.8815 | 0.9809 |
138
+ | 0.0154 | 5.9004 | 1540 | 0.0827 | 0.8771 | 0.8810 | 0.8791 | 0.9808 |
139
+ | 0.0101 | 5.9770 | 1560 | 0.0833 | 0.8779 | 0.8842 | 0.8811 | 0.9812 |
140
+ | 0.008 | 6.0536 | 1580 | 0.0883 | 0.8782 | 0.8833 | 0.8807 | 0.9810 |
141
+ | 0.0096 | 6.1303 | 1600 | 0.0875 | 0.8820 | 0.8849 | 0.8835 | 0.9810 |
142
+ | 0.0113 | 6.2069 | 1620 | 0.0893 | 0.8816 | 0.8844 | 0.8830 | 0.9810 |
143
+ | 0.0126 | 6.2835 | 1640 | 0.0841 | 0.8846 | 0.8892 | 0.8869 | 0.9817 |
144
+ | 0.0115 | 6.3602 | 1660 | 0.0825 | 0.8861 | 0.8865 | 0.8863 | 0.9814 |
145
+ | 0.0108 | 6.4368 | 1680 | 0.0855 | 0.8828 | 0.8881 | 0.8855 | 0.9814 |
146
+ | 0.0089 | 6.5134 | 1700 | 0.0845 | 0.8803 | 0.8874 | 0.8839 | 0.9813 |
147
+ | 0.0132 | 6.5900 | 1720 | 0.0829 | 0.8827 | 0.8867 | 0.8847 | 0.9814 |
148
+ | 0.0094 | 6.6667 | 1740 | 0.0848 | 0.8833 | 0.8853 | 0.8843 | 0.9814 |
149
+ | 0.0091 | 6.7433 | 1760 | 0.0853 | 0.8826 | 0.8849 | 0.8838 | 0.9813 |
150
+ | 0.0104 | 6.8199 | 1780 | 0.0862 | 0.8820 | 0.8849 | 0.8835 | 0.9812 |
151
+ | 0.0103 | 6.8966 | 1800 | 0.0863 | 0.8818 | 0.8844 | 0.8831 | 0.9813 |
152
+ | 0.0075 | 6.9732 | 1820 | 0.0862 | 0.8825 | 0.8853 | 0.8839 | 0.9813 |
153
 
154
 
155
  ### Framework versions
156
 
157
  - PEFT 0.14.0
158
+ - Transformers 4.49.0
159
+ - Pytorch 2.5.1+cu124
160
+ - Datasets 3.3.1
161
  - Tokenizers 0.21.0