Hugging Face's logo

henryscheible
/
xlnet-base-cased_crows_pairs_finetuned

Text Classification
Transformers
PyTorch
xlnet
generated_from_trainer
Eval Results
Inference Endpoints
Model card Files Community
xlnet-base-cased_crows_pairs_finetuned / README.md
Henry Scheible
rollback model to probed version
b57f99d
preview code
|
raw history blame contribute delete
No virus
5.15 kB
metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - crows_pairs
metrics:
  - accuracy
model-index:
  - name: xlnet-base-cased_crows_pairs_finetuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: crows_pairs
          type: crows_pairs
          config: crows_pairs
          split: test
          args: crows_pairs
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7119205298013245

xlnet-base-cased_crows_pairs_finetuned

This model is a fine-tuned version of xlnet-base-cased on the crows_pairs dataset. It achieves the following results on the evaluation set:

  • Loss: 2.5652
  • Accuracy: 0.7119

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: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.728 0.53 10 0.6939 0.4901
0.6914 1.05 20 0.6939 0.4901
0.705 1.58 30 0.6925 0.5066
0.6993 2.11 40 0.6949 0.5066
0.6979 2.63 50 0.6996 0.5066
0.7152 3.16 60 0.6940 0.4901
0.7158 3.68 70 0.7007 0.4934
0.6968 4.21 80 0.6999 0.5066
0.7164 4.74 90 0.6977 0.4934
0.6698 5.26 100 0.7079 0.4536
0.611 5.79 110 0.8882 0.5099
0.6487 6.32 120 0.8360 0.5066
0.5223 6.84 130 0.8047 0.5728
0.2879 7.37 140 1.1483 0.5795
0.2369 7.89 150 1.1773 0.5993
0.2542 8.42 160 0.9170 0.6424
0.1743 8.95 170 1.3674 0.6424
0.1307 9.47 180 1.0740 0.7152
0.0718 10.0 190 1.4397 0.6424
0.0278 10.53 200 1.9821 0.6523
0.0519 11.05 210 1.6970 0.6755
0.0269 11.58 220 1.8299 0.6656
0.0556 12.11 230 1.9459 0.7086
0.0455 12.63 240 1.6443 0.6854
0.0665 13.16 250 1.9887 0.6821
0.009 13.68 260 2.0236 0.6788
0.0146 14.21 270 1.8515 0.7152
0.0034 14.74 280 1.9315 0.7252
0.0248 15.26 290 2.0754 0.7119
0.0536 15.79 300 2.0371 0.7053
0.0393 16.32 310 1.9381 0.6987
0.0255 16.84 320 1.9074 0.6788
0.0116 17.37 330 2.2182 0.6623
0.0128 17.89 340 2.3002 0.6689
0.0006 18.42 350 2.2353 0.6788
0.0053 18.95 360 2.4277 0.6755
0.0013 19.47 370 2.5156 0.6490
0.0004 20.0 380 2.5091 0.6689
0.0003 20.53 390 2.4096 0.6854
0.0017 21.05 400 2.3497 0.6921
0.0001 21.58 410 2.3376 0.6854
0.012 22.11 420 2.3832 0.6854
0.0002 22.63 430 2.4388 0.7053
0.0001 23.16 440 2.4821 0.7152
0.0001 23.68 450 2.5027 0.7119
0.0001 24.21 460 2.5105 0.7152
0.0001 24.74 470 2.5145 0.7152
0.0002 25.26 480 2.5143 0.6954
0.0001 25.79 490 2.5629 0.6821
0.0002 26.32 500 2.5414 0.6887
0.0001 26.84 510 2.5301 0.7119
0.0012 27.37 520 2.5360 0.7020
0.0 27.89 530 2.5428 0.6921
0.0117 28.42 540 2.5455 0.6954
0.0001 28.95 550 2.5598 0.7086
0.0001 29.47 560 2.5648 0.7119
0.0001 30.0 570 2.5652 0.7119

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1
  • Datasets 2.10.1
  • Tokenizers 0.13.2