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tmp6tsjsfbf

This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0178
  • Train Sparse Categorical Accuracy: 0.9962
  • Epoch: 49

Model description

This model classifies the title of a content (e.g., YouTube video, article, or podcast episode) into 1 of 8 subjects

  1. art
  2. personal development
  3. world
  4. health
  5. science
  6. business
  7. humanities
  8. technology.

This model is used to support Sanderling

Intended uses & limitations

More information needed

Training and evaluation data

We used 1.5k labeled titles to train the model. Majority of the training dataset are English titles. The rest are Chinese titles.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'Adam', 'learning_rate': 5e-06, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Train Sparse Categorical Accuracy Epoch
1.8005 0.3956 0
1.3302 0.5916 1
0.8998 0.7575 2
0.6268 0.8468 3
0.4239 0.9062 4
0.2982 0.9414 5
0.2245 0.9625 6
0.1678 0.9730 7
0.1399 0.9745 8
0.1059 0.9827 9
0.0822 0.9850 10
0.0601 0.9902 11
0.0481 0.9932 12
0.0386 0.9955 13
0.0292 0.9977 14
0.0353 0.9940 15
0.0336 0.9932 16
0.0345 0.9910 17
0.0179 0.9985 18
0.0150 0.9985 19
0.0365 0.9895 20
0.0431 0.9895 21
0.0243 0.9955 22
0.0317 0.9925 23
0.0375 0.9902 24
0.0138 0.9970 25
0.0159 0.9977 26
0.0160 0.9962 27
0.0151 0.9977 28
0.0337 0.9902 29
0.0119 0.9977 30
0.0165 0.9955 31
0.0133 0.9977 32
0.0047 1.0 33
0.0037 1.0 34
0.0033 1.0 35
0.0031 1.0 36
0.0036 1.0 37
0.0343 0.9887 38
0.0234 0.9962 39
0.0034 1.0 40
0.0036 1.0 41
0.0261 0.9917 42
0.0111 0.9970 43
0.0039 1.0 44
0.0214 0.9932 45
0.0044 0.9985 46
0.0122 0.9985 47
0.0119 0.9962 48
0.0178 0.9962 49

Framework versions

  • Transformers 4.15.0
  • TensorFlow 2.7.0
  • Tokenizers 0.10.3
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