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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert_sm_cv_defined_summarized_4
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. -->
# bert_sm_cv_defined_summarized_4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8001
- Accuracy: 0.801
- Precision: 0.4677
- Recall: 0.1487
- F1: 0.2257
- D-index: 1.4847
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8000
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 250 | 0.4931 | 0.805 | 0.5 | 0.0308 | 0.0580 | 1.4481 |
| 0.5724 | 2.0 | 500 | 0.4850 | 0.806 | 0.5263 | 0.0513 | 0.0935 | 1.4569 |
| 0.5724 | 3.0 | 750 | 0.4842 | 0.811 | 0.6 | 0.0923 | 0.16 | 1.4785 |
| 0.4468 | 4.0 | 1000 | 0.4954 | 0.81 | 0.5806 | 0.0923 | 0.1593 | 1.4771 |
| 0.4468 | 5.0 | 1250 | 0.5307 | 0.81 | 0.5862 | 0.0872 | 0.1518 | 1.4753 |
| 0.381 | 6.0 | 1500 | 0.5312 | 0.809 | 0.5455 | 0.1231 | 0.2008 | 1.4866 |
| 0.381 | 7.0 | 1750 | 0.5354 | 0.807 | 0.5161 | 0.1641 | 0.2490 | 1.4983 |
| 0.283 | 8.0 | 2000 | 0.7003 | 0.811 | 0.6364 | 0.0718 | 0.1290 | 1.4712 |
| 0.283 | 9.0 | 2250 | 0.7079 | 0.798 | 0.4568 | 0.1897 | 0.2681 | 1.4949 |
| 0.1621 | 10.0 | 2500 | 0.9032 | 0.8 | 0.4603 | 0.1487 | 0.2248 | 1.4833 |
| 0.1621 | 11.0 | 2750 | 1.0875 | 0.797 | 0.4474 | 0.1744 | 0.2509 | 1.4881 |
| 0.0678 | 12.0 | 3000 | 1.2256 | 0.769 | 0.3861 | 0.3128 | 0.3456 | 1.4975 |
| 0.0678 | 13.0 | 3250 | 1.6378 | 0.793 | 0.4 | 0.1231 | 0.1882 | 1.4645 |
| 0.039 | 14.0 | 3500 | 1.7475 | 0.767 | 0.2841 | 0.1282 | 0.1767 | 1.4301 |
| 0.039 | 15.0 | 3750 | 1.8575 | 0.804 | 0.4848 | 0.0821 | 0.1404 | 1.4652 |
| 0.0295 | 16.0 | 4000 | 1.8151 | 0.775 | 0.3370 | 0.1590 | 0.2160 | 1.4522 |
| 0.0295 | 17.0 | 4250 | 1.8788 | 0.795 | 0.4219 | 0.1385 | 0.2085 | 1.4728 |
| 0.0416 | 18.0 | 4500 | 1.8193 | 0.765 | 0.3462 | 0.2308 | 0.2769 | 1.4636 |
| 0.0416 | 19.0 | 4750 | 1.6942 | 0.788 | 0.3896 | 0.1538 | 0.2206 | 1.4685 |
| 0.0322 | 20.0 | 5000 | 1.8001 | 0.801 | 0.4677 | 0.1487 | 0.2257 | 1.4847 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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