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my_awesome_model

Text Classification
Transformers
PyTorch
camembert
Generated from Trainer
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metadata
license: mit
base_model: camembert-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: my_awesome_model
    results: []

my_awesome_model

This model is a fine-tuned version of camembert-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1882
  • Accuracy: 1.0

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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 Accuracy
No log 1.0 1 0.6856 0.5
No log 2.0 2 0.6825 0.5
No log 3.0 3 0.6796 0.5
No log 4.0 4 0.6775 0.5
No log 5.0 5 0.6750 0.5
No log 6.0 6 0.6718 0.5
No log 7.0 7 0.6680 0.5
No log 8.0 8 0.6613 0.5
No log 9.0 9 0.6675 0.5
No log 10.0 10 0.6638 0.5
No log 11.0 11 0.6603 0.5
No log 12.0 12 0.6568 0.5
No log 13.0 13 0.6528 0.5
No log 14.0 14 0.6459 0.5
No log 15.0 15 0.6389 0.5
No log 16.0 16 0.6246 0.5
No log 17.0 17 0.6152 0.5
No log 18.0 18 0.6050 0.5
No log 19.0 19 0.5939 0.5
No log 20.0 20 0.5820 0.5
No log 21.0 21 0.5707 0.5
No log 22.0 22 0.5604 0.5
No log 23.0 23 0.5504 0.5
No log 24.0 24 0.5376 0.5
No log 25.0 25 0.5233 1.0
No log 26.0 26 0.5108 1.0
No log 27.0 27 0.4983 1.0
No log 28.0 28 0.4864 1.0
No log 29.0 29 0.4744 1.0
No log 30.0 30 0.4632 1.0
No log 31.0 31 0.4523 1.0
No log 32.0 32 0.4423 1.0
No log 33.0 33 0.4331 1.0
No log 34.0 34 0.4246 1.0
No log 35.0 35 0.4168 1.0
No log 36.0 36 0.4089 1.0
No log 37.0 37 0.4007 1.0
No log 38.0 38 0.3936 1.0
No log 39.0 39 0.3873 1.0
No log 40.0 40 0.3795 1.0
No log 41.0 41 0.3698 1.0
No log 42.0 42 0.3599 1.0
No log 43.0 43 0.3509 1.0
No log 44.0 44 0.3430 1.0
No log 45.0 45 0.3359 1.0
No log 46.0 46 0.3289 1.0
No log 47.0 47 0.3204 1.0
No log 48.0 48 0.3130 1.0
No log 49.0 49 0.3065 1.0
No log 50.0 50 0.2998 1.0
No log 51.0 51 0.2943 1.0
No log 52.0 52 0.2889 1.0
No log 53.0 53 0.2832 1.0
No log 54.0 54 0.2783 1.0
No log 55.0 55 0.2733 1.0
No log 56.0 56 0.2693 1.0
No log 57.0 57 0.2658 1.0
No log 58.0 58 0.2625 1.0
No log 59.0 59 0.2591 1.0
No log 60.0 60 0.2562 1.0
No log 61.0 61 0.2531 1.0
No log 62.0 62 0.2497 1.0
No log 63.0 63 0.2460 1.0
No log 64.0 64 0.2424 1.0
No log 65.0 65 0.2389 1.0
No log 66.0 66 0.2356 1.0
No log 67.0 67 0.2329 1.0
No log 68.0 68 0.2300 1.0
No log 69.0 69 0.2269 1.0
No log 70.0 70 0.2243 1.0
No log 71.0 71 0.2212 1.0
No log 72.0 72 0.2186 1.0
No log 73.0 73 0.2158 1.0
No log 74.0 74 0.2129 1.0
No log 75.0 75 0.2104 1.0
No log 76.0 76 0.2082 1.0
No log 77.0 77 0.2061 1.0
No log 78.0 78 0.2043 1.0
No log 79.0 79 0.2029 1.0
No log 80.0 80 0.2017 1.0
No log 81.0 81 0.2005 1.0
No log 82.0 82 0.1994 1.0
No log 83.0 83 0.1981 1.0
No log 84.0 84 0.1969 1.0
No log 85.0 85 0.1959 1.0
No log 86.0 86 0.1951 1.0
No log 87.0 87 0.1943 1.0
No log 88.0 88 0.1935 1.0
No log 89.0 89 0.1928 1.0
No log 90.0 90 0.1920 1.0
No log 91.0 91 0.1913 1.0
No log 92.0 92 0.1908 1.0
No log 93.0 93 0.1904 1.0
No log 94.0 94 0.1900 1.0
No log 95.0 95 0.1894 1.0
No log 96.0 96 0.1890 1.0
No log 97.0 97 0.1887 1.0
No log 98.0 98 0.1884 1.0
No log 99.0 99 0.1883 1.0
No log 100.0 100 0.1882 1.0

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1
  • Datasets 2.14.7
  • Tokenizers 0.13.2