| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: distilbert-base-cased |
| tags: |
| - generated_from_trainer |
| metrics: |
| - 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 [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.3395 |
| - Accuracy: 0.6088 |
| - F1 Weighted: 0.6068 |
|
|
| ## 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: 10 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:| |
| | 1.9969 | 0.1562 | 100 | 1.7350 | 0.4181 | 0.3685 | |
| | 1.5932 | 0.3125 | 200 | 1.3946 | 0.5038 | 0.4816 | |
| | 1.3938 | 0.4688 | 300 | 1.3229 | 0.5312 | 0.5406 | |
| | 1.3342 | 0.625 | 400 | 1.2797 | 0.5369 | 0.5280 | |
| | 1.2588 | 0.7812 | 500 | 1.2016 | 0.5737 | 0.5678 | |
| | 1.3359 | 0.9375 | 600 | 1.2025 | 0.5737 | 0.5472 | |
| | 1.0521 | 1.0938 | 700 | 1.2580 | 0.5531 | 0.5427 | |
| | 0.947 | 1.25 | 800 | 1.2156 | 0.5613 | 0.5656 | |
| | 1.0765 | 1.4062 | 900 | 1.1659 | 0.6044 | 0.6011 | |
| | 0.9336 | 1.5625 | 1000 | 1.2022 | 0.5781 | 0.5773 | |
| | 1.0288 | 1.7188 | 1100 | 1.1642 | 0.5756 | 0.5877 | |
| | 0.9665 | 1.875 | 1200 | 1.1635 | 0.5781 | 0.5821 | |
| | 0.8987 | 2.0312 | 1300 | 1.1913 | 0.5969 | 0.5982 | |
| | 0.6252 | 2.1875 | 1400 | 1.2783 | 0.595 | 0.5948 | |
| | 0.5977 | 2.3438 | 1500 | 1.2460 | 0.5938 | 0.5894 | |
| | 0.5646 | 2.5 | 1600 | 1.3038 | 0.5844 | 0.5915 | |
| | 0.6488 | 2.6562 | 1700 | 1.2850 | 0.5925 | 0.5955 | |
| | 0.627 | 2.8125 | 1800 | 1.2690 | 0.59 | 0.5927 | |
| | 0.6441 | 2.9688 | 1900 | 1.3395 | 0.6088 | 0.6068 | |
| | 0.3832 | 3.125 | 2000 | 1.4401 | 0.6088 | 0.6092 | |
| | 0.3338 | 3.2812 | 2100 | 1.5685 | 0.5831 | 0.5864 | |
| | 0.3475 | 3.4375 | 2200 | 1.6456 | 0.5806 | 0.5846 | |
| | 0.4362 | 3.5938 | 2300 | 1.5581 | 0.5825 | 0.5890 | |
| | 0.3565 | 3.75 | 2400 | 1.6010 | 0.5981 | 0.5993 | |
| | 0.3958 | 3.9062 | 2500 | 1.6087 | 0.5938 | 0.5944 | |
| | 0.2844 | 4.0625 | 2600 | 1.6917 | 0.5994 | 0.5980 | |
| | 0.174 | 4.2188 | 2700 | 1.8947 | 0.5906 | 0.5956 | |
| | 0.2393 | 4.375 | 2800 | 1.9103 | 0.5894 | 0.5897 | |
| | 0.2019 | 4.5312 | 2900 | 2.0275 | 0.5819 | 0.5854 | |
| | 0.1895 | 4.6875 | 3000 | 1.9962 | 0.5962 | 0.5935 | |
| | 0.2885 | 4.8438 | 3100 | 2.0387 | 0.5944 | 0.5932 | |
| | 0.2672 | 5.0 | 3200 | 2.0070 | 0.595 | 0.5938 | |
| | 0.1089 | 5.1562 | 3300 | 2.2210 | 0.5919 | 0.5945 | |
| | 0.1114 | 5.3125 | 3400 | 2.3073 | 0.5863 | 0.5884 | |
| | 0.1274 | 5.4688 | 3500 | 2.3061 | 0.5994 | 0.5994 | |
| | 0.1403 | 5.625 | 3600 | 2.2753 | 0.5894 | 0.5932 | |
| | 0.1869 | 5.7812 | 3700 | 2.2661 | 0.5925 | 0.5935 | |
| | 0.1769 | 5.9375 | 3800 | 2.2007 | 0.5975 | 0.6016 | |
| | 0.129 | 6.0938 | 3900 | 2.2289 | 0.6075 | 0.6100 | |
| | 0.0945 | 6.25 | 4000 | 2.3460 | 0.6038 | 0.6080 | |
| | 0.0913 | 6.4062 | 4100 | 2.4089 | 0.6038 | 0.6060 | |
| | 0.111 | 6.5625 | 4200 | 2.3776 | 0.6012 | 0.6039 | |
| | 0.1355 | 6.7188 | 4300 | 2.3579 | 0.6069 | 0.6069 | |
| | 0.1182 | 6.875 | 4400 | 2.3727 | 0.6012 | 0.6050 | |
| | 0.1049 | 7.0312 | 4500 | 2.4246 | 0.6069 | 0.6100 | |
| | 0.0802 | 7.1875 | 4600 | 2.5167 | 0.5988 | 0.6046 | |
| | 0.0665 | 7.3438 | 4700 | 2.5161 | 0.605 | 0.6060 | |
| | 0.0906 | 7.5 | 4800 | 2.5229 | 0.6088 | 0.6166 | |
| | 0.0781 | 7.6562 | 4900 | 2.5169 | 0.5994 | 0.5970 | |
| | 0.0689 | 7.8125 | 5000 | 2.5068 | 0.6 | 0.5987 | |
| | 0.1288 | 7.9688 | 5100 | 2.5147 | 0.5925 | 0.5974 | |
| | 0.0602 | 8.125 | 5200 | 2.5465 | 0.6 | 0.6045 | |
| | 0.0507 | 8.2812 | 5300 | 2.5416 | 0.605 | 0.6079 | |
| | 0.0589 | 8.4375 | 5400 | 2.5926 | 0.5962 | 0.6013 | |
| | 0.0446 | 8.5938 | 5500 | 2.5855 | 0.6062 | 0.6079 | |
| | 0.0994 | 8.75 | 5600 | 2.5714 | 0.6056 | 0.6097 | |
| | 0.0883 | 8.9062 | 5700 | 2.5625 | 0.6088 | 0.6123 | |
| | 0.0495 | 9.0625 | 5800 | 2.5795 | 0.6062 | 0.6095 | |
| | 0.0321 | 9.2188 | 5900 | 2.5991 | 0.6006 | 0.6045 | |
| | 0.0498 | 9.375 | 6000 | 2.5928 | 0.6038 | 0.6062 | |
| | 0.0303 | 9.5312 | 6100 | 2.5942 | 0.6056 | 0.6085 | |
| | 0.0552 | 9.6875 | 6200 | 2.5930 | 0.6069 | 0.6099 | |
| | 0.0394 | 9.8438 | 6300 | 2.5990 | 0.605 | 0.6076 | |
| | 0.0645 | 10.0 | 6400 | 2.5997 | 0.6056 | 0.6088 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.47.0 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 4.5.0 |
| - Tokenizers 0.21.0 |
| |
