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DimasikKurd
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longformer_pos_neg

Token Classification
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longformer_pos_neg

This model is a fine-tuned version of severinsimmler/xlm-roberta-longformer-base-16384 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5549
  • Precision: 0.5599
  • Recall: 0.5786
  • F1: 0.5691
  • Accuracy: 0.9030

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.35 50 0.7729 0.0 0.0 0.0 0.7762
No log 2.7 100 0.5497 0.0220 0.0078 0.0115 0.8017
No log 4.05 150 0.4143 0.0706 0.0698 0.0702 0.8383
No log 5.41 200 0.3607 0.2329 0.2578 0.2447 0.8632
No log 6.76 250 0.3320 0.3628 0.3101 0.3344 0.8807
No log 8.11 300 0.3261 0.5108 0.4574 0.4826 0.8939
No log 9.46 350 0.3190 0.4229 0.5950 0.4944 0.8826
No log 10.81 400 0.2662 0.4821 0.6008 0.5349 0.9014
No log 12.16 450 0.2714 0.5901 0.5775 0.5837 0.9137
0.3792 13.51 500 0.2852 0.5769 0.5891 0.5829 0.9105
0.3792 14.86 550 0.3868 0.5876 0.5329 0.5589 0.9082
0.3792 16.22 600 0.3218 0.5444 0.6531 0.5938 0.9129
0.3792 17.57 650 0.3022 0.5645 0.6357 0.5980 0.9112
0.3792 18.92 700 0.3737 0.5419 0.6764 0.6017 0.9025
0.3792 20.27 750 0.3730 0.5411 0.6628 0.5958 0.9119
0.3792 21.62 800 0.4021 0.6145 0.6240 0.6192 0.9109
0.3792 22.97 850 0.3358 0.5159 0.6298 0.5672 0.9008
0.3792 24.32 900 0.3779 0.6065 0.6124 0.6095 0.9138
0.3792 25.68 950 0.4435 0.5293 0.6298 0.5752 0.9063
0.0755 27.03 1000 0.4230 0.6333 0.6124 0.6227 0.9169
0.0755 28.38 1050 0.3666 0.5911 0.6415 0.6152 0.9163
0.0755 29.73 1100 0.3335 0.6098 0.6512 0.6298 0.9178
0.0755 31.08 1150 0.4606 0.5725 0.6202 0.5953 0.9075
0.0755 32.43 1200 0.4280 0.5656 0.6434 0.6020 0.9065
0.0755 33.78 1250 0.4003 0.5833 0.6376 0.6093 0.9158
0.0755 35.14 1300 0.5802 0.6422 0.5775 0.6082 0.9020
0.0755 36.49 1350 0.4503 0.6014 0.6550 0.6271 0.9172
0.0755 37.84 1400 0.5614 0.6643 0.5523 0.6032 0.9044
0.0755 39.19 1450 0.5082 0.628 0.6085 0.6181 0.9119
0.0407 40.54 1500 0.3964 0.6072 0.6531 0.6293 0.9165
0.0407 41.89 1550 0.5447 0.4572 0.6938 0.5512 0.8799
0.0407 43.24 1600 0.5303 0.4816 0.6589 0.5565 0.8947
0.0407 44.59 1650 0.4461 0.6409 0.6260 0.6333 0.9138
0.0407 45.95 1700 0.6884 0.5561 0.4031 0.4674 0.8766
0.0407 47.3 1750 0.4556 0.5431 0.6105 0.5748 0.9097
0.0407 48.65 1800 0.4272 0.6771 0.5853 0.6279 0.9183
0.0407 50.0 1850 0.4904 0.5603 0.6570 0.6048 0.9015
0.0407 51.35 1900 0.4206 0.5655 0.6357 0.5985 0.9135

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2
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Evaluation results

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