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---
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: 0.3660
- Accuracy: 0.8991
- Precision: 0.8990
- Recall: 0.8942
- F1: 0.8959
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3839 | 1.0 | 24 | 0.4077 | 0.8716 | 0.8635 | 0.8717 | 0.8639 |
| 0.3268 | 2.0 | 48 | 0.4052 | 0.8578 | 0.8510 | 0.8489 | 0.8467 |
| 0.2524 | 3.0 | 72 | 0.4014 | 0.8899 | 0.8938 | 0.8795 | 0.8843 |
| 0.2171 | 4.0 | 96 | 0.3582 | 0.8899 | 0.8860 | 0.8849 | 0.8846 |
| 0.1712 | 5.0 | 120 | 0.3983 | 0.8899 | 0.8885 | 0.8804 | 0.8826 |
| 0.1627 | 6.0 | 144 | 0.3789 | 0.8991 | 0.8984 | 0.8998 | 0.8983 |
| 0.1462 | 7.0 | 168 | 0.3884 | 0.8991 | 0.9004 | 0.8922 | 0.8955 |
| 0.1499 | 8.0 | 192 | 0.3727 | 0.9083 | 0.9069 | 0.9080 | 0.9070 |
| 0.1557 | 9.0 | 216 | 0.3669 | 0.8991 | 0.8990 | 0.8942 | 0.8959 |
| 0.1462 | 10.0 | 240 | 0.3660 | 0.8991 | 0.8990 | 0.8942 | 0.8959 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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