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ES-ENG-mBERT-sentiment
This model is a fine-tuned version of bert-base-multilingual-cased on a Custom dataset.
The best model (stopped after 14 epochs) achieves the following results on the evaluation set:
- Loss: 0.8110
- Accuracy: 0.6307
- F1: 0.6298
- Precision: 0.6291
- Recall: 0.6307
Intended uses & limitations
Note that commercial use with this model is prohibited.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
1.063 | 1.0 | 208 | 0.9989 | 0.4731 | 0.4044 | 0.4885 | 0.4731 |
0.9664 | 2.0 | 416 | 0.9144 | 0.5262 | 0.4845 | 0.5270 | 0.5262 |
0.9067 | 3.0 | 624 | 0.8648 | 0.5896 | 0.5844 | 0.5935 | 0.5896 |
0.8572 | 4.0 | 832 | 0.8294 | 0.6065 | 0.5984 | 0.6102 | 0.6065 |
0.8168 | 5.0 | 1040 | 0.8101 | 0.6107 | 0.6092 | 0.6119 | 0.6107 |
0.7897 | 6.0 | 1248 | 0.8213 | 0.6074 | 0.6015 | 0.6018 | 0.6074 |
0.7568 | 7.0 | 1456 | 0.7992 | 0.6194 | 0.6181 | 0.6176 | 0.6194 |
0.7465 | 8.0 | 1664 | 0.8089 | 0.6246 | 0.6183 | 0.6206 | 0.6246 |
0.7223 | 9.0 | 1872 | 0.7988 | 0.6236 | 0.6214 | 0.6207 | 0.6236 |
0.7045 | 10.0 | 2080 | 0.8390 | 0.6165 | 0.6080 | 0.6126 | 0.6165 |
0.6888 | 11.0 | 2288 | 0.8042 | 0.6291 | 0.6260 | 0.6257 | 0.6291 |
0.671 | 12.0 | 2496 | 0.8088 | 0.6239 | 0.6212 | 0.6216 | 0.6239 |
0.6543 | 13.0 | 2704 | 0.8104 | 0.6256 | 0.6227 | 0.6216 | 0.6256 |
0.6409 | 14.0 | 2912 | 0.8110 | 0.6307 | 0.6298 | 0.6291 | 0.6307 |
0.6275 | 15.0 | 3120 | 0.8127 | 0.6298 | 0.6292 | 0.6299 | 0.6298 |
0.6176 | 16.0 | 3328 | 0.8334 | 0.6252 | 0.6217 | 0.6206 | 0.6252 |
0.6096 | 17.0 | 3536 | 0.8331 | 0.6256 | 0.6210 | 0.6210 | 0.6256 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3