| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: bert_small_summarized |
| | 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_small_summarized |
| |
|
| | 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: 2.1652 |
| | - Accuracy: 0.82 |
| | - Precision: 0.4667 |
| | - Recall: 0.2 |
| | - F1: 0.2800 |
| | - D-index: 1.5200 |
| |
|
| | ## 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: 4 |
| | - 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: 1600 |
| | - 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 | 200 | 0.4533 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 | |
| | | No log | 2.0 | 400 | 0.4694 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 | |
| | | 0.5094 | 3.0 | 600 | 0.6237 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 | |
| | | 0.5094 | 4.0 | 800 | 0.7898 | 0.81 | 0.4286 | 0.2571 | 0.3214 | 1.5270 | |
| | | 0.3984 | 5.0 | 1000 | 0.9268 | 0.83 | 0.5556 | 0.1429 | 0.2273 | 1.5127 | |
| | | 0.3984 | 6.0 | 1200 | 1.3541 | 0.8 | 0.4074 | 0.3143 | 0.3548 | 1.5339 | |
| | | 0.3984 | 7.0 | 1400 | 1.4264 | 0.805 | 0.375 | 0.1714 | 0.2353 | 1.4893 | |
| | | 0.0939 | 8.0 | 1600 | 1.8870 | 0.8 | 0.4194 | 0.3714 | 0.3939 | 1.5539 | |
| | | 0.0939 | 9.0 | 1800 | 1.8734 | 0.825 | 0.5 | 0.1143 | 0.1860 | 1.4955 | |
| | | 0.0061 | 10.0 | 2000 | 1.8938 | 0.825 | 0.5 | 0.1714 | 0.2553 | 1.5164 | |
| | | 0.0061 | 11.0 | 2200 | 2.0755 | 0.825 | 0.5 | 0.1143 | 0.1860 | 1.4955 | |
| | | 0.0061 | 12.0 | 2400 | 2.1068 | 0.805 | 0.4231 | 0.3143 | 0.3607 | 1.5406 | |
| | | 0.0134 | 13.0 | 2600 | 2.0895 | 0.82 | 0.4444 | 0.1143 | 0.1818 | 1.4887 | |
| | | 0.0134 | 14.0 | 2800 | 2.0520 | 0.815 | 0.4545 | 0.2857 | 0.3509 | 1.5439 | |
| | | 0.0011 | 15.0 | 3000 | 2.0795 | 0.81 | 0.4211 | 0.2286 | 0.2963 | 1.5168 | |
| | | 0.0011 | 16.0 | 3200 | 2.1177 | 0.815 | 0.4444 | 0.2286 | 0.3019 | 1.5235 | |
| | | 0.0011 | 17.0 | 3400 | 2.1396 | 0.815 | 0.4444 | 0.2286 | 0.3019 | 1.5235 | |
| | | 0.0003 | 18.0 | 3600 | 2.1605 | 0.825 | 0.5 | 0.2286 | 0.3137 | 1.5370 | |
| | | 0.0003 | 19.0 | 3800 | 2.1677 | 0.825 | 0.5 | 0.2286 | 0.3137 | 1.5370 | |
| | | 0.0 | 20.0 | 4000 | 2.1652 | 0.82 | 0.4667 | 0.2 | 0.2800 | 1.5200 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.28.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.3 |
| | |
