|
--- |
|
license: apache-2.0 |
|
base_model: google-bert/bert-base-multilingual-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: result-colab |
|
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. --> |
|
|
|
# result-colab |
|
|
|
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3751 |
|
- Accuracy: 0.8853 |
|
- Precision: 0.8785 |
|
- Recall: 0.8773 |
|
- F1: 0.8773 |
|
|
|
## 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: cosine |
|
- num_epochs: 15 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 1.3635 | 1.0 | 48 | 1.1593 | 0.5963 | 0.4671 | 0.5259 | 0.4501 | |
|
| 0.9895 | 2.0 | 96 | 0.7987 | 0.6881 | 0.6824 | 0.6271 | 0.6017 | |
|
| 0.6845 | 3.0 | 144 | 0.6432 | 0.7523 | 0.7789 | 0.7076 | 0.7071 | |
|
| 0.5856 | 4.0 | 192 | 0.5896 | 0.7844 | 0.8315 | 0.7457 | 0.7463 | |
|
| 0.4328 | 5.0 | 240 | 0.4232 | 0.8716 | 0.8750 | 0.8585 | 0.8645 | |
|
| 0.4298 | 6.0 | 288 | 0.4118 | 0.8853 | 0.8783 | 0.8810 | 0.8789 | |
|
| 0.322 | 7.0 | 336 | 0.3988 | 0.8807 | 0.8824 | 0.8655 | 0.8712 | |
|
| 0.3561 | 8.0 | 384 | 0.4169 | 0.8716 | 0.8630 | 0.8679 | 0.8637 | |
|
| 0.27 | 9.0 | 432 | 0.3779 | 0.8991 | 0.8972 | 0.8913 | 0.8938 | |
|
| 0.2472 | 10.0 | 480 | 0.3850 | 0.8991 | 0.8928 | 0.8942 | 0.8924 | |
|
| 0.2349 | 11.0 | 528 | 0.3749 | 0.8945 | 0.8855 | 0.8919 | 0.8875 | |
|
| 0.2491 | 12.0 | 576 | 0.3798 | 0.9037 | 0.8969 | 0.8992 | 0.8975 | |
|
| 0.2239 | 13.0 | 624 | 0.3778 | 0.8853 | 0.8783 | 0.8810 | 0.8793 | |
|
| 0.2276 | 14.0 | 672 | 0.3755 | 0.8899 | 0.8827 | 0.8823 | 0.8822 | |
|
| 0.206 | 15.0 | 720 | 0.3751 | 0.8853 | 0.8785 | 0.8773 | 0.8773 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.42.4 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
