| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: tmp6tsjsfbf |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should |
| probably proofread and complete it, then remove this comment. --> |
|
|
| # tmp6tsjsfbf |
|
|
| This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/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 |
|
|
| 0. art |
| 1. personal development |
| 2. world |
| 3. health |
| 4. science |
| 5. business |
| 6. humanities |
| 7. technology. |
|
|
| This model is used to support [Sanderling](https://sanderling.app) |
|
|
| ## 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 |
|
|
