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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: tes1-UASNLP2
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. -->
# tes1-UASNLP2
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3413
- Accuracy: 0.8705
- Precision: 0.8858
- Recall: 0.8803
- F1: 0.8830
## 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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4596 | 1.2121 | 100 | 0.3631 | 0.8554 | 0.8767 | 0.8605 | 0.8685 |
| 0.254 | 2.4242 | 200 | 0.3413 | 0.8705 | 0.8858 | 0.8803 | 0.8830 |
| 0.1674 | 3.6364 | 300 | 0.3847 | 0.8793 | 0.8758 | 0.9118 | 0.8934 |
| 0.0968 | 4.8485 | 400 | 0.4927 | 0.8759 | 0.9145 | 0.8564 | 0.8845 |
| 0.0614 | 6.0606 | 500 | 0.5308 | 0.8721 | 0.8748 | 0.8981 | 0.8863 |
| 0.0418 | 7.2727 | 600 | 0.6098 | 0.8759 | 0.8988 | 0.8748 | 0.8867 |
| 0.0296 | 8.4848 | 700 | 0.6507 | 0.8751 | 0.8910 | 0.8830 | 0.8870 |
| 0.0183 | 9.6970 | 800 | 0.6822 | 0.8789 | 0.8944 | 0.8865 | 0.8904 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
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