| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: bert_sm_cv_defined_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_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: 1.8439 |
| | - Accuracy: 0.802 |
| | - Precision: 0.4824 |
| | - Recall: 0.2103 |
| | - F1: 0.2929 |
| | - D-index: 1.5075 |
| |
|
| | ## 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 | 0.4642 | 0.813 | 0.5833 | 0.1436 | 0.2305 | 1.4993 | |
| | | 0.5606 | 2.0 | 500 | 0.4524 | 0.816 | 0.6 | 0.1692 | 0.264 | 1.5124 | |
| | | 0.5606 | 3.0 | 750 | 0.4448 | 0.82 | 0.6154 | 0.2051 | 0.3077 | 1.5303 | |
| | | 0.4415 | 4.0 | 1000 | 0.4583 | 0.819 | 0.6591 | 0.1487 | 0.2427 | 1.5093 | |
| | | 0.4415 | 5.0 | 1250 | 0.4659 | 0.817 | 0.5652 | 0.2667 | 0.3624 | 1.5473 | |
| | | 0.3644 | 6.0 | 1500 | 0.4927 | 0.805 | 0.5 | 0.3077 | 0.3810 | 1.5449 | |
| | | 0.3644 | 7.0 | 1750 | 0.5493 | 0.82 | 0.6230 | 0.1949 | 0.2969 | 1.5267 | |
| | | 0.2821 | 8.0 | 2000 | 0.6262 | 0.815 | 0.5893 | 0.1692 | 0.2629 | 1.5110 | |
| | | 0.2821 | 9.0 | 2250 | 0.6864 | 0.798 | 0.4685 | 0.2667 | 0.3399 | 1.5215 | |
| | | 0.2084 | 10.0 | 2500 | 0.8556 | 0.816 | 0.5696 | 0.2308 | 0.3285 | 1.5337 | |
| | | 0.2084 | 11.0 | 2750 | 0.9422 | 0.814 | 0.5542 | 0.2359 | 0.3309 | 1.5327 | |
| | | 0.114 | 12.0 | 3000 | 1.0528 | 0.807 | 0.5109 | 0.2410 | 0.3275 | 1.5249 | |
| | | 0.114 | 13.0 | 3250 | 1.2325 | 0.802 | 0.4889 | 0.3385 | 0.4000 | 1.5513 | |
| | | 0.0612 | 14.0 | 3500 | 1.3032 | 0.782 | 0.4148 | 0.2872 | 0.3394 | 1.5066 | |
| | | 0.0612 | 15.0 | 3750 | 1.7304 | 0.806 | 0.5102 | 0.1282 | 0.2049 | 1.4843 | |
| | | 0.0454 | 16.0 | 4000 | 1.6902 | 0.797 | 0.4643 | 0.2667 | 0.3388 | 1.5201 | |
| | | 0.0454 | 17.0 | 4250 | 1.6552 | 0.78 | 0.4172 | 0.3231 | 0.3642 | 1.5161 | |
| | | 0.0379 | 18.0 | 4500 | 1.7798 | 0.806 | 0.5054 | 0.2410 | 0.3264 | 1.5236 | |
| | | 0.0379 | 19.0 | 4750 | 1.8226 | 0.816 | 0.5753 | 0.2154 | 0.3134 | 1.5284 | |
| | | 0.0487 | 20.0 | 5000 | 1.8439 | 0.802 | 0.4824 | 0.2103 | 0.2929 | 1.5075 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| | |
