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mbert-profane-final
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4464
- Accuracy: 0.8983
- Precision: 0.8135
- Recall: 0.8120
- F1: 0.8128
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 296 | 0.2313 | 0.9154 | 0.8687 | 0.8010 | 0.8294 |
| 0.3077 | 2.0 | 592 | 0.2223 | 0.9125 | 0.8473 | 0.8205 | 0.8330 |
| 0.3077 | 3.0 | 888 | 0.2137 | 0.9259 | 0.8784 | 0.8379 | 0.8563 |
| 0.2102 | 4.0 | 1184 | 0.2334 | 0.9163 | 0.8483 | 0.8417 | 0.8449 |
| 0.2102 | 5.0 | 1480 | 0.2737 | 0.9068 | 0.8305 | 0.8242 | 0.8273 |
| 0.1533 | 6.0 | 1776 | 0.3214 | 0.8964 | 0.8034 | 0.8510 | 0.8239 |
| 0.1092 | 7.0 | 2072 | 0.3409 | 0.9002 | 0.8115 | 0.8414 | 0.8252 |
| 0.1092 | 8.0 | 2368 | 0.3849 | 0.9049 | 0.8322 | 0.8066 | 0.8185 |
| 0.0775 | 9.0 | 2664 | 0.4408 | 0.8983 | 0.8113 | 0.8215 | 0.8162 |
| 0.0775 | 10.0 | 2960 | 0.4464 | 0.8983 | 0.8135 | 0.8120 | 0.8128 |
Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
