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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: training
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. -->
# training
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3256
- Accuracy: 0.6768
- F1: 0.6764
- Precision: 0.6772
- Recall: 0.6768
## 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: 2e-05
- train_batch_size: 40
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 66 | 0.7029 | 0.4939 | 0.3623 | 0.4289 | 0.4939 |
| No log | 2.0 | 132 | 0.6985 | 0.4726 | 0.4074 | 0.4429 | 0.4726 |
| No log | 3.0 | 198 | 0.7052 | 0.5091 | 0.5079 | 0.5101 | 0.5091 |
| No log | 4.0 | 264 | 0.7277 | 0.5732 | 0.5687 | 0.5746 | 0.5732 |
| No log | 5.0 | 330 | 0.8226 | 0.5747 | 0.5711 | 0.5791 | 0.5747 |
| No log | 6.0 | 396 | 0.9070 | 0.6098 | 0.6084 | 0.6126 | 0.6098 |
| No log | 7.0 | 462 | 0.9877 | 0.6296 | 0.6288 | 0.6299 | 0.6296 |
| 0.4904 | 8.0 | 528 | 1.2868 | 0.5976 | 0.5814 | 0.6198 | 0.5976 |
| 0.4904 | 9.0 | 594 | 1.2709 | 0.6433 | 0.6396 | 0.6517 | 0.6433 |
| 0.4904 | 10.0 | 660 | 1.3541 | 0.6494 | 0.6494 | 0.6494 | 0.6494 |
| 0.4904 | 11.0 | 726 | 1.4138 | 0.6631 | 0.6572 | 0.6724 | 0.6631 |
| 0.4904 | 12.0 | 792 | 1.5116 | 0.6631 | 0.6616 | 0.6676 | 0.6631 |
| 0.4904 | 13.0 | 858 | 1.5349 | 0.6738 | 0.6687 | 0.6825 | 0.6738 |
| 0.4904 | 14.0 | 924 | 1.5437 | 0.6845 | 0.6845 | 0.6845 | 0.6845 |
| 0.4904 | 15.0 | 990 | 1.8465 | 0.6585 | 0.6581 | 0.6588 | 0.6585 |
| 0.0493 | 16.0 | 1056 | 1.8186 | 0.6662 | 0.6661 | 0.6667 | 0.6662 |
| 0.0493 | 17.0 | 1122 | 1.9234 | 0.6601 | 0.6589 | 0.6635 | 0.6601 |
| 0.0493 | 18.0 | 1188 | 1.9517 | 0.6707 | 0.6689 | 0.6763 | 0.6707 |
| 0.0493 | 19.0 | 1254 | 1.9673 | 0.6616 | 0.6609 | 0.6639 | 0.6616 |
| 0.0493 | 20.0 | 1320 | 2.0034 | 0.6768 | 0.6768 | 0.6769 | 0.6768 |
| 0.0493 | 21.0 | 1386 | 2.0452 | 0.6707 | 0.6707 | 0.6707 | 0.6707 |
| 0.0493 | 22.0 | 1452 | 2.1151 | 0.6570 | 0.6569 | 0.6578 | 0.6570 |
| 0.0085 | 23.0 | 1518 | 2.0888 | 0.6631 | 0.6627 | 0.6633 | 0.6631 |
| 0.0085 | 24.0 | 1584 | 2.1101 | 0.6646 | 0.6646 | 0.6649 | 0.6646 |
| 0.0085 | 25.0 | 1650 | 2.1330 | 0.6662 | 0.6661 | 0.6666 | 0.6662 |
| 0.0085 | 26.0 | 1716 | 2.1890 | 0.6662 | 0.6659 | 0.6663 | 0.6662 |
| 0.0085 | 27.0 | 1782 | 2.2275 | 0.6601 | 0.6598 | 0.6602 | 0.6601 |
| 0.0085 | 28.0 | 1848 | 2.2380 | 0.6662 | 0.6648 | 0.6704 | 0.6662 |
| 0.0085 | 29.0 | 1914 | 2.2606 | 0.6646 | 0.6646 | 0.6650 | 0.6646 |
| 0.0085 | 30.0 | 1980 | 2.2708 | 0.6738 | 0.6734 | 0.6755 | 0.6738 |
| 0.0029 | 31.0 | 2046 | 2.2827 | 0.6677 | 0.6675 | 0.6677 | 0.6677 |
| 0.0029 | 32.0 | 2112 | 2.2992 | 0.6738 | 0.6738 | 0.6738 | 0.6738 |
| 0.0029 | 33.0 | 2178 | 2.2926 | 0.6768 | 0.6757 | 0.6782 | 0.6768 |
| 0.0029 | 34.0 | 2244 | 2.3100 | 0.6738 | 0.6738 | 0.6740 | 0.6738 |
| 0.0029 | 35.0 | 2310 | 2.3081 | 0.6768 | 0.6767 | 0.6768 | 0.6768 |
| 0.0029 | 36.0 | 2376 | 2.3080 | 0.6768 | 0.6764 | 0.6772 | 0.6768 |
| 0.0029 | 37.0 | 2442 | 2.3242 | 0.6784 | 0.6783 | 0.6787 | 0.6784 |
| 0.0004 | 38.0 | 2508 | 2.3252 | 0.6799 | 0.6799 | 0.6799 | 0.6799 |
| 0.0004 | 39.0 | 2574 | 2.3228 | 0.6784 | 0.6782 | 0.6784 | 0.6784 |
| 0.0004 | 40.0 | 2640 | 2.3256 | 0.6768 | 0.6764 | 0.6772 | 0.6768 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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