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---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fosh-detector-v2-tmp
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. -->
# fosh-detector-v2-tmp
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: 0.1027
- Accuracy: 0.9684
- Precision: 0.8378
- Recall: 0.8692
- F1: 0.8532
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3176 | 0.1305 | 50 | 0.2282 | 0.9150 | 0.6981 | 0.3458 | 0.4625 |
| 0.1735 | 0.2611 | 100 | 0.1324 | 0.9526 | 0.8242 | 0.7009 | 0.7576 |
| 0.1358 | 0.3916 | 150 | 0.1283 | 0.9615 | 0.8542 | 0.7664 | 0.8079 |
| 0.1332 | 0.5222 | 200 | 0.1142 | 0.9664 | 0.8411 | 0.8411 | 0.8411 |
| 0.1125 | 0.6527 | 250 | 0.1268 | 0.9634 | 0.85 | 0.7944 | 0.8213 |
| 0.1022 | 0.7833 | 300 | 0.1251 | 0.9644 | 0.7983 | 0.8879 | 0.8407 |
| 0.1107 | 0.9138 | 350 | 0.0972 | 0.9644 | 0.8381 | 0.8224 | 0.8302 |
| 0.1017 | 1.0444 | 400 | 0.1070 | 0.9654 | 0.8396 | 0.8318 | 0.8357 |
| 0.0925 | 1.1749 | 450 | 0.0977 | 0.9664 | 0.8288 | 0.8598 | 0.8440 |
| 0.0834 | 1.3055 | 500 | 0.0867 | 0.9674 | 0.8936 | 0.7850 | 0.8358 |
| 0.0748 | 1.4360 | 550 | 0.0951 | 0.9704 | 0.8348 | 0.8972 | 0.8649 |
| 0.0697 | 1.5666 | 600 | 0.0950 | 0.9713 | 0.8611 | 0.8692 | 0.8651 |
| 0.0818 | 1.6971 | 650 | 0.0871 | 0.9674 | 0.8136 | 0.8972 | 0.8533 |
| 0.073 | 1.8277 | 700 | 0.0813 | 0.9684 | 0.8319 | 0.8785 | 0.8545 |
| 0.0742 | 1.9582 | 750 | 0.0841 | 0.9713 | 0.875 | 0.8505 | 0.8626 |
| 0.0599 | 2.0888 | 800 | 0.0926 | 0.9713 | 0.8824 | 0.8411 | 0.8612 |
| 0.0522 | 2.2193 | 850 | 0.1015 | 0.9674 | 0.8426 | 0.8505 | 0.8465 |
| 0.0581 | 2.3499 | 900 | 0.1000 | 0.9694 | 0.88 | 0.8224 | 0.8502 |
| 0.0562 | 2.4804 | 950 | 0.1066 | 0.9674 | 0.8364 | 0.8598 | 0.8479 |
| 0.0553 | 2.6110 | 1000 | 0.0989 | 0.9674 | 0.8190 | 0.8879 | 0.8520 |
| 0.0546 | 2.7415 | 1050 | 0.0921 | 0.9694 | 0.8725 | 0.8318 | 0.8517 |
| 0.0541 | 2.8721 | 1100 | 0.0920 | 0.9644 | 0.8034 | 0.8785 | 0.8393 |
| 0.0494 | 3.0026 | 1150 | 0.0981 | 0.9713 | 0.8611 | 0.8692 | 0.8651 |
| 0.0358 | 3.1332 | 1200 | 0.1033 | 0.9713 | 0.8679 | 0.8598 | 0.8638 |
| 0.038 | 3.2637 | 1250 | 0.1109 | 0.9684 | 0.8378 | 0.8692 | 0.8532 |
| 0.0408 | 3.3943 | 1300 | 0.0996 | 0.9684 | 0.8205 | 0.8972 | 0.8571 |
| 0.0456 | 3.5248 | 1350 | 0.0976 | 0.9684 | 0.8440 | 0.8598 | 0.8519 |
| 0.0367 | 3.6554 | 1400 | 0.1075 | 0.9694 | 0.8455 | 0.8692 | 0.8571 |
| 0.0332 | 3.7859 | 1450 | 0.1081 | 0.9684 | 0.8440 | 0.8598 | 0.8519 |
| 0.0376 | 3.9164 | 1500 | 0.1027 | 0.9684 | 0.8378 | 0.8692 | 0.8532 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1
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