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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- filter_sort |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: favs-filtersort-multilabel-classification-bert-base-cased |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: filter_sort |
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type: filter_sort |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.7428571428571428 |
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- name: Accuracy |
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type: accuracy |
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value: 0.2 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# favs-filtersort-multilabel-classification-bert-base-cased |
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This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3066 |
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- F1: 0.7429 |
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- Roc Auc: 0.8142 |
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- Accuracy: 0.2 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1.5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.7601 | 1.0 | 12 | 0.6966 | 0.2564 | 0.4518 | 0.0 | |
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| 0.6757 | 2.0 | 24 | 0.5629 | 0.6667 | 0.7785 | 0.0 | |
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| 0.5796 | 3.0 | 36 | 0.4652 | 0.6286 | 0.7477 | 0.0 | |
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| 0.5026 | 4.0 | 48 | 0.4161 | 0.6479 | 0.7605 | 0.0 | |
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| 0.4282 | 5.0 | 60 | 0.3830 | 0.6849 | 0.7862 | 0.0 | |
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| 0.4085 | 6.0 | 72 | 0.3658 | 0.7273 | 0.7962 | 0.0 | |
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| 0.3847 | 7.0 | 84 | 0.3538 | 0.7353 | 0.8052 | 0.0 | |
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| 0.3829 | 8.0 | 96 | 0.3457 | 0.6761 | 0.7772 | 0.0 | |
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| 0.3758 | 9.0 | 108 | 0.3409 | 0.6857 | 0.7810 | 0.0 | |
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| 0.3487 | 10.0 | 120 | 0.3327 | 0.7143 | 0.7976 | 0.0 | |
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| 0.3421 | 11.0 | 132 | 0.3268 | 0.6866 | 0.7758 | 0.0 | |
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| 0.3351 | 12.0 | 144 | 0.3183 | 0.7059 | 0.7886 | 0.0 | |
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| 0.3245 | 13.0 | 156 | 0.3149 | 0.7246 | 0.8014 | 0.0 | |
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| 0.3191 | 14.0 | 168 | 0.3087 | 0.7246 | 0.8014 | 0.1 | |
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| 0.3083 | 15.0 | 180 | 0.3066 | 0.7429 | 0.8142 | 0.2 | |
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| 0.3061 | 16.0 | 192 | 0.3062 | 0.7429 | 0.8142 | 0.2 | |
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| 0.2935 | 17.0 | 204 | 0.3017 | 0.7429 | 0.8142 | 0.2 | |
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| 0.2888 | 18.0 | 216 | 0.3009 | 0.7429 | 0.8142 | 0.2 | |
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| 0.297 | 19.0 | 228 | 0.3022 | 0.7429 | 0.8142 | 0.2 | |
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| 0.2868 | 20.0 | 240 | 0.3014 | 0.7429 | 0.8142 | 0.2 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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