MBERTdataaugmentation1511
This model is a fine-tuned version of bert-base-multilingual-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4799
- Precisions: 0.8775
- Recall: 0.8066
- F-measure: 0.8358
- Accuracy: 0.9364
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: 34
- 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 |
|---|---|---|---|---|---|---|---|
| 0.4277 | 1.0 | 284 | 0.2782 | 0.7721 | 0.7565 | 0.7579 | 0.9183 |
| 0.1776 | 2.0 | 568 | 0.2903 | 0.8401 | 0.7736 | 0.8007 | 0.9265 |
| 0.0953 | 3.0 | 852 | 0.3116 | 0.8565 | 0.7792 | 0.8124 | 0.9337 |
| 0.067 | 4.0 | 1136 | 0.3747 | 0.8019 | 0.7974 | 0.7931 | 0.9253 |
| 0.0424 | 5.0 | 1420 | 0.3834 | 0.8297 | 0.7751 | 0.7971 | 0.9306 |
| 0.0288 | 6.0 | 1704 | 0.4208 | 0.8641 | 0.7981 | 0.8245 | 0.9313 |
| 0.0187 | 7.0 | 1988 | 0.4472 | 0.8374 | 0.8033 | 0.8148 | 0.9323 |
| 0.0105 | 8.0 | 2272 | 0.4297 | 0.8652 | 0.7903 | 0.8181 | 0.9332 |
| 0.0085 | 9.0 | 2556 | 0.4425 | 0.8567 | 0.8019 | 0.8250 | 0.9344 |
| 0.0044 | 10.0 | 2840 | 0.4744 | 0.8493 | 0.8075 | 0.8256 | 0.9347 |
| 0.004 | 11.0 | 3124 | 0.4967 | 0.8581 | 0.8017 | 0.8251 | 0.9361 |
| 0.003 | 12.0 | 3408 | 0.4799 | 0.8775 | 0.8066 | 0.8358 | 0.9364 |
| 0.0015 | 13.0 | 3692 | 0.5093 | 0.8803 | 0.7943 | 0.8294 | 0.9356 |
| 0.0014 | 14.0 | 3976 | 0.4985 | 0.8762 | 0.7964 | 0.8281 | 0.9359 |
Framework versions
- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1
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Base model
google-bert/bert-base-multilingual-uncased