File size: 7,483 Bytes
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license: apache-2.0
base_model: robzchhangte/MizBERT
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [robzchhangte/MizBERT](https://huggingface.co/robzchhangte/MizBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7248
- Accuracy: 0.5346
- F1: 0.5346
- Precision: 0.5346
- Recall: 0.5346
## 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: 15
- eval_batch_size: 15
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.6747 | 0.0585 | 10 | 1.2733 | 0.5 | 0.5 | 0.5 | 0.5 |
| 1.2979 | 0.1170 | 20 | 1.1629 | 0.5219 | 0.5219 | 0.5219 | 0.5219 |
| 1.0906 | 0.1754 | 30 | 1.1048 | 0.5234 | 0.5234 | 0.5234 | 0.5234 |
| 0.9134 | 0.2339 | 40 | 0.8426 | 0.5109 | 0.5109 | 0.5109 | 0.5109 |
| 0.7985 | 0.2924 | 50 | 0.7739 | 0.525 | 0.525 | 0.525 | 0.525 |
| 0.7278 | 0.3509 | 60 | 0.7949 | 0.4969 | 0.4969 | 0.4969 | 0.4969 |
| 0.7522 | 0.4094 | 70 | 0.7225 | 0.525 | 0.525 | 0.525 | 0.525 |
| 0.7134 | 0.4678 | 80 | 0.7187 | 0.5109 | 0.5109 | 0.5109 | 0.5109 |
| 0.6897 | 0.5263 | 90 | 0.7682 | 0.4781 | 0.4781 | 0.4781 | 0.4781 |
| 0.7369 | 0.5848 | 100 | 0.7019 | 0.5078 | 0.5078 | 0.5078 | 0.5078 |
| 0.6917 | 0.6433 | 110 | 0.6980 | 0.5109 | 0.5109 | 0.5109 | 0.5109 |
| 0.698 | 0.7018 | 120 | 0.7038 | 0.5297 | 0.5297 | 0.5297 | 0.5297 |
| 0.6974 | 0.7602 | 130 | 0.7039 | 0.5125 | 0.5125 | 0.5125 | 0.5125 |
| 0.7141 | 0.8187 | 140 | 0.6941 | 0.5047 | 0.5047 | 0.5047 | 0.5047 |
| 0.7127 | 0.8772 | 150 | 0.6937 | 0.5 | 0.5 | 0.5 | 0.5 |
| 0.7007 | 0.9357 | 160 | 0.7047 | 0.5266 | 0.5266 | 0.5266 | 0.5266 |
| 0.7483 | 0.9942 | 170 | 0.6975 | 0.4828 | 0.4828 | 0.4828 | 0.4828 |
| 0.7063 | 1.0526 | 180 | 0.6929 | 0.5266 | 0.5266 | 0.5266 | 0.5266 |
| 0.6848 | 1.1111 | 190 | 0.7107 | 0.4797 | 0.4797 | 0.4797 | 0.4797 |
| 0.7014 | 1.1696 | 200 | 0.6891 | 0.5422 | 0.5422 | 0.5422 | 0.5422 |
| 0.7113 | 1.2281 | 210 | 0.6950 | 0.5141 | 0.5141 | 0.5141 | 0.5141 |
| 0.6915 | 1.2865 | 220 | 0.6901 | 0.5391 | 0.5391 | 0.5391 | 0.5391 |
| 0.6834 | 1.3450 | 230 | 0.7117 | 0.5188 | 0.5188 | 0.5188 | 0.5188 |
| 0.7032 | 1.4035 | 240 | 0.7029 | 0.5031 | 0.5031 | 0.5031 | 0.5031 |
| 0.6962 | 1.4620 | 250 | 0.6952 | 0.5312 | 0.5312 | 0.5312 | 0.5312 |
| 0.7103 | 1.5205 | 260 | 0.7165 | 0.5297 | 0.5297 | 0.5297 | 0.5297 |
| 0.7405 | 1.5789 | 270 | 0.8608 | 0.475 | 0.4750 | 0.475 | 0.475 |
| 0.7633 | 1.6374 | 280 | 0.6994 | 0.5344 | 0.5344 | 0.5344 | 0.5344 |
| 0.7061 | 1.6959 | 290 | 0.6887 | 0.5531 | 0.5531 | 0.5531 | 0.5531 |
| 0.6975 | 1.7544 | 300 | 0.7105 | 0.475 | 0.4750 | 0.475 | 0.475 |
| 0.7098 | 1.8129 | 310 | 0.6959 | 0.5297 | 0.5297 | 0.5297 | 0.5297 |
| 0.7703 | 1.8713 | 320 | 0.6954 | 0.5281 | 0.5281 | 0.5281 | 0.5281 |
| 0.6948 | 1.9298 | 330 | 0.7116 | 0.475 | 0.4750 | 0.475 | 0.475 |
| 0.689 | 1.9883 | 340 | 0.7261 | 0.475 | 0.4750 | 0.475 | 0.475 |
| 0.7011 | 2.0468 | 350 | 0.7265 | 0.5234 | 0.5234 | 0.5234 | 0.5234 |
| 0.7026 | 2.1053 | 360 | 0.7217 | 0.4734 | 0.4734 | 0.4734 | 0.4734 |
| 0.6837 | 2.1637 | 370 | 0.7001 | 0.4984 | 0.4984 | 0.4984 | 0.4984 |
| 0.6579 | 2.2222 | 380 | 0.7106 | 0.525 | 0.525 | 0.525 | 0.525 |
| 0.6755 | 2.2807 | 390 | 0.7218 | 0.525 | 0.525 | 0.525 | 0.525 |
| 0.6739 | 2.3392 | 400 | 0.7054 | 0.5172 | 0.5172 | 0.5172 | 0.5172 |
| 0.6757 | 2.3977 | 410 | 0.7015 | 0.5406 | 0.5406 | 0.5406 | 0.5406 |
| 0.7135 | 2.4561 | 420 | 0.7396 | 0.4828 | 0.4828 | 0.4828 | 0.4828 |
| 0.6801 | 2.5146 | 430 | 0.7323 | 0.4906 | 0.4906 | 0.4906 | 0.4906 |
| 0.7349 | 2.5731 | 440 | 0.6939 | 0.5047 | 0.5047 | 0.5047 | 0.5047 |
| 0.6813 | 2.6316 | 450 | 0.6957 | 0.5234 | 0.5234 | 0.5234 | 0.5234 |
| 0.7054 | 2.6901 | 460 | 0.7156 | 0.5344 | 0.5344 | 0.5344 | 0.5344 |
| 0.7052 | 2.7485 | 470 | 0.7143 | 0.5437 | 0.5437 | 0.5437 | 0.5437 |
| 0.6915 | 2.8070 | 480 | 0.6947 | 0.5062 | 0.5062 | 0.5062 | 0.5062 |
| 0.679 | 2.8655 | 490 | 0.7109 | 0.5312 | 0.5312 | 0.5312 | 0.5312 |
| 0.6729 | 2.9240 | 500 | 0.7442 | 0.4938 | 0.4938 | 0.4938 | 0.4938 |
| 0.7035 | 2.9825 | 510 | 0.7041 | 0.5281 | 0.5281 | 0.5281 | 0.5281 |
| 0.7069 | 3.0409 | 520 | 0.7023 | 0.4766 | 0.4766 | 0.4766 | 0.4766 |
| 0.7089 | 3.0994 | 530 | 0.6936 | 0.5359 | 0.5359 | 0.5359 | 0.5359 |
| 0.6675 | 3.1579 | 540 | 0.6931 | 0.5188 | 0.5188 | 0.5188 | 0.5188 |
| 0.6202 | 3.2164 | 550 | 0.8091 | 0.4703 | 0.4703 | 0.4703 | 0.4703 |
| 0.6183 | 3.2749 | 560 | 0.7316 | 0.5406 | 0.5406 | 0.5406 | 0.5406 |
| 0.5781 | 3.3333 | 570 | 0.7620 | 0.5437 | 0.5437 | 0.5437 | 0.5437 |
| 0.6383 | 3.3918 | 580 | 0.7552 | 0.5219 | 0.5219 | 0.5219 | 0.5219 |
| 0.628 | 3.4503 | 590 | 0.7266 | 0.5437 | 0.5437 | 0.5437 | 0.5437 |
| 0.6198 | 3.5088 | 600 | 0.7217 | 0.5672 | 0.5672 | 0.5672 | 0.5672 |
| 0.6572 | 3.5673 | 610 | 0.7962 | 0.5047 | 0.5047 | 0.5047 | 0.5047 |
| 0.6119 | 3.6257 | 620 | 0.7258 | 0.5563 | 0.5563 | 0.5563 | 0.5563 |
| 0.6651 | 3.6842 | 630 | 0.7445 | 0.55 | 0.55 | 0.55 | 0.55 |
| 0.5399 | 3.7427 | 640 | 0.8115 | 0.5062 | 0.5062 | 0.5062 | 0.5062 |
| 0.6291 | 3.8012 | 650 | 0.8045 | 0.5312 | 0.5312 | 0.5312 | 0.5312 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
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