multibertfinetuned2209
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: 0.3973
- Precision: 0.7567
- Recall: 0.7607
- F1: 0.7587
- Accuracy: 0.9064
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: 5e-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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 118 | 0.4058 | 0.7597 | 0.7343 | 0.7468 | 0.9032 |
| No log | 2.0 | 236 | 0.3973 | 0.7567 | 0.7607 | 0.7587 | 0.9064 |
| No log | 3.0 | 354 | 0.4153 | 0.7540 | 0.7677 | 0.7608 | 0.9062 |
| No log | 4.0 | 472 | 0.4656 | 0.7645 | 0.7466 | 0.7555 | 0.9082 |
| 0.0692 | 5.0 | 590 | 0.4940 | 0.7594 | 0.7554 | 0.7574 | 0.9043 |
| 0.0692 | 6.0 | 708 | 0.5446 | 0.7668 | 0.7484 | 0.7575 | 0.9059 |
| 0.0692 | 7.0 | 826 | 0.5732 | 0.7818 | 0.7420 | 0.7613 | 0.9069 |
| 0.0692 | 8.0 | 944 | 0.5668 | 0.7844 | 0.7431 | 0.7632 | 0.9082 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3