| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | 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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5524 |
| | - Precision: 0.6970 |
| | - Recall: 0.6661 |
| | - F1: 0.6798 |
| | - Accuracy: 0.9081 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 20 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | No log | 1.0 | 250 | 0.2537 | 0.7645 | 0.5606 | 0.5850 | 0.9188 | |
| | | 0.2695 | 2.0 | 500 | 0.2528 | 0.7559 | 0.6099 | 0.6464 | 0.9205 | |
| | | 0.2695 | 3.0 | 750 | 0.2524 | 0.7359 | 0.6409 | 0.6729 | 0.9183 | |
| | | 0.2201 | 4.0 | 1000 | 0.2660 | 0.7015 | 0.6596 | 0.6773 | 0.9099 | |
| | | 0.2201 | 5.0 | 1250 | 0.2926 | 0.6924 | 0.6821 | 0.6870 | 0.9053 | |
| | | 0.1629 | 6.0 | 1500 | 0.3055 | 0.6904 | 0.6848 | 0.6876 | 0.9042 | |
| | | 0.1629 | 7.0 | 1750 | 0.3332 | 0.7037 | 0.6532 | 0.6736 | 0.9109 | |
| | | 0.1144 | 8.0 | 2000 | 0.3661 | 0.6870 | 0.6759 | 0.6812 | 0.9038 | |
| | | 0.1144 | 9.0 | 2250 | 0.3670 | 0.6950 | 0.6597 | 0.6750 | 0.9079 | |
| | | 0.081 | 10.0 | 2500 | 0.4031 | 0.6969 | 0.6588 | 0.6751 | 0.9086 | |
| | | 0.081 | 11.0 | 2750 | 0.4176 | 0.6883 | 0.6734 | 0.6804 | 0.9045 | |
| | | 0.0611 | 12.0 | 3000 | 0.4531 | 0.7003 | 0.6552 | 0.6739 | 0.9098 | |
| | | 0.0611 | 13.0 | 3250 | 0.4733 | 0.6970 | 0.6600 | 0.6758 | 0.9085 | |
| | | 0.0476 | 14.0 | 3500 | 0.4815 | 0.6997 | 0.6533 | 0.6724 | 0.9098 | |
| | | 0.0476 | 15.0 | 3750 | 0.5058 | 0.6977 | 0.6580 | 0.6748 | 0.9089 | |
| | | 0.039 | 16.0 | 4000 | 0.5027 | 0.7011 | 0.6646 | 0.6804 | 0.9095 | |
| | | 0.039 | 17.0 | 4250 | 0.5196 | 0.6993 | 0.6635 | 0.6790 | 0.9090 | |
| | | 0.0309 | 18.0 | 4500 | 0.5462 | 0.6986 | 0.6687 | 0.6819 | 0.9085 | |
| | | 0.0309 | 19.0 | 4750 | 0.5406 | 0.6939 | 0.6684 | 0.6799 | 0.9069 | |
| | | 0.0273 | 20.0 | 5000 | 0.5524 | 0.6970 | 0.6661 | 0.6798 | 0.9081 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.42.4 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 3.0.0 |
| | - Tokenizers 0.19.1 |
| |
|
