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---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: gg-bert-base-uncased
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. -->
# gg-bert-base-uncased
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7791
- Accuracy: 0.752
- Precision: 0.7388
- Recall: 0.7570
- F1: 0.7396
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.2541 | 1.0 | 469 | 2.2063 | 0.1488 | 0.2698 | 0.1552 | 0.1036 |
| 1.8967 | 2.0 | 938 | 1.8773 | 0.5168 | 0.5264 | 0.5331 | 0.4788 |
| 1.5747 | 3.0 | 1407 | 1.5546 | 0.5984 | 0.6125 | 0.6118 | 0.5636 |
| 1.4206 | 4.0 | 1876 | 1.3029 | 0.6528 | 0.6732 | 0.6666 | 0.6224 |
| 1.2804 | 5.0 | 2345 | 1.1876 | 0.6928 | 0.6972 | 0.6989 | 0.6844 |
| 1.1587 | 6.0 | 2814 | 1.0644 | 0.7136 | 0.7105 | 0.7136 | 0.6858 |
| 1.1589 | 7.0 | 3283 | 0.9883 | 0.7216 | 0.7173 | 0.7261 | 0.7031 |
| 1.0745 | 8.0 | 3752 | 0.9485 | 0.728 | 0.7151 | 0.7318 | 0.7195 |
| 1.0348 | 9.0 | 4221 | 0.9278 | 0.7328 | 0.7306 | 0.7372 | 0.7166 |
| 1.0019 | 10.0 | 4690 | 0.9114 | 0.72 | 0.7316 | 0.7231 | 0.7006 |
| 1.0204 | 11.0 | 5159 | 0.8967 | 0.7152 | 0.7187 | 0.7215 | 0.6895 |
| 1.0651 | 12.0 | 5628 | 0.8574 | 0.7424 | 0.7446 | 0.7474 | 0.7327 |
| 0.9841 | 13.0 | 6097 | 0.8461 | 0.7328 | 0.7495 | 0.7370 | 0.7076 |
| 0.9794 | 14.0 | 6566 | 0.8510 | 0.7248 | 0.7157 | 0.7319 | 0.7022 |
| 1.0242 | 15.0 | 7035 | 0.8127 | 0.7264 | 0.7112 | 0.7300 | 0.6998 |
| 0.9614 | 16.0 | 7504 | 0.8146 | 0.7312 | 0.7210 | 0.7376 | 0.7149 |
| 0.9358 | 17.0 | 7973 | 0.8288 | 0.736 | 0.7487 | 0.7439 | 0.7275 |
| 0.9719 | 18.0 | 8442 | 0.7958 | 0.7488 | 0.7403 | 0.7530 | 0.7349 |
| 0.9159 | 19.0 | 8911 | 0.7973 | 0.7472 | 0.7388 | 0.7522 | 0.7357 |
| 0.9824 | 20.0 | 9380 | 0.7921 | 0.7504 | 0.7439 | 0.7562 | 0.7363 |
| 1.0215 | 21.0 | 9849 | 0.7831 | 0.7536 | 0.7415 | 0.7586 | 0.7392 |
| 0.9191 | 22.0 | 10318 | 0.7780 | 0.7504 | 0.7387 | 0.7554 | 0.7399 |
| 0.9087 | 23.0 | 10787 | 0.7843 | 0.7472 | 0.7352 | 0.7536 | 0.7345 |
| 0.9198 | 24.0 | 11256 | 0.7793 | 0.7504 | 0.7358 | 0.7554 | 0.7374 |
| 0.9162 | 25.0 | 11725 | 0.7791 | 0.752 | 0.7388 | 0.7570 | 0.7396 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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