| --- |
| license: apache-2.0 |
| base_model: bert-base-multilingual-uncased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - recall |
| - accuracy |
| model-index: |
| - name: multibert_dataaugmentation |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # multibert_dataaugmentation |
| |
| This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7138 |
| - Precisions: 0.8609 |
| - Recall: 0.8356 |
| - F-measure: 0.8464 |
| - Accuracy: 0.8989 |
| |
| ## 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: 7.5e-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: 14 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:| |
| | 0.5775 | 1.0 | 285 | 0.4827 | 0.7847 | 0.7040 | 0.7340 | 0.8509 | |
| | 0.2623 | 2.0 | 570 | 0.5829 | 0.8035 | 0.7359 | 0.7591 | 0.8613 | |
| | 0.1503 | 3.0 | 855 | 0.5609 | 0.7946 | 0.8083 | 0.7917 | 0.8804 | |
| | 0.088 | 4.0 | 1140 | 0.5481 | 0.8406 | 0.7997 | 0.8170 | 0.8860 | |
| | 0.0592 | 5.0 | 1425 | 0.6359 | 0.8207 | 0.8210 | 0.8120 | 0.8828 | |
| | 0.0414 | 6.0 | 1710 | 0.6589 | 0.8313 | 0.8171 | 0.8198 | 0.8843 | |
| | 0.0271 | 7.0 | 1995 | 0.7117 | 0.8689 | 0.7882 | 0.8216 | 0.8936 | |
| | 0.0179 | 8.0 | 2280 | 0.7138 | 0.8609 | 0.8356 | 0.8464 | 0.8989 | |
| | 0.0121 | 9.0 | 2565 | 0.7289 | 0.8456 | 0.8128 | 0.8278 | 0.8946 | |
| | 0.0081 | 10.0 | 2850 | 0.7603 | 0.8344 | 0.8223 | 0.8278 | 0.8956 | |
| | 0.0058 | 11.0 | 3135 | 0.8126 | 0.8576 | 0.8107 | 0.8322 | 0.8942 | |
| | 0.0041 | 12.0 | 3420 | 0.8004 | 0.8582 | 0.8267 | 0.8415 | 0.8955 | |
| | 0.0031 | 13.0 | 3705 | 0.7936 | 0.8599 | 0.8275 | 0.8426 | 0.8961 | |
| | 0.0028 | 14.0 | 3990 | 0.8076 | 0.8602 | 0.8226 | 0.8401 | 0.8966 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.34.0 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.14.1 |
| |
