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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: 0.7887323943661971 |
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- name: Accuracy |
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type: accuracy |
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value: 0.3 |
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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: 0.2822 |
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- F1: 0.7887 |
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- Roc Auc: 0.8437 |
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- Accuracy: 0.3 |
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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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| 0.6319 | 1.0 | 12 | 0.5814 | 0.4889 | 0.6714 | 0.0 | |
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| 0.5697 | 2.0 | 24 | 0.5046 | 0.6111 | 0.7401 | 0.0 | |
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| 0.5224 | 3.0 | 36 | 0.4393 | 0.6923 | 0.8004 | 0.0 | |
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| 0.4666 | 4.0 | 48 | 0.4048 | 0.6835 | 0.7965 | 0.0 | |
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| 0.4188 | 5.0 | 60 | 0.3795 | 0.6933 | 0.7952 | 0.0 | |
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| 0.4023 | 6.0 | 72 | 0.3634 | 0.7027 | 0.7990 | 0.0 | |
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| 0.3776 | 7.0 | 84 | 0.3526 | 0.7143 | 0.7976 | 0.0 | |
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| 0.3635 | 8.0 | 96 | 0.3423 | 0.7143 | 0.7976 | 0.0 | |
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| 0.3648 | 9.0 | 108 | 0.3288 | 0.7059 | 0.7886 | 0.0 | |
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| 0.3284 | 10.0 | 120 | 0.3192 | 0.7429 | 0.8142 | 0.2 | |
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| 0.3267 | 11.0 | 132 | 0.3151 | 0.7353 | 0.8052 | 0.1 | |
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| 0.3113 | 12.0 | 144 | 0.3066 | 0.7536 | 0.8181 | 0.2 | |
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| 0.3043 | 13.0 | 156 | 0.3018 | 0.7606 | 0.8271 | 0.3 | |
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| 0.2924 | 14.0 | 168 | 0.2940 | 0.7606 | 0.8271 | 0.3 | |
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| 0.2843 | 15.0 | 180 | 0.2936 | 0.7714 | 0.8309 | 0.3 | |
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| 0.2794 | 16.0 | 192 | 0.2856 | 0.7778 | 0.8399 | 0.3 | |
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| 0.2678 | 17.0 | 204 | 0.2860 | 0.7714 | 0.8309 | 0.3 | |
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| 0.2631 | 18.0 | 216 | 0.2822 | 0.7887 | 0.8437 | 0.3 | |
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| 0.269 | 19.0 | 228 | 0.2806 | 0.7887 | 0.8437 | 0.3 | |
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| 0.2609 | 20.0 | 240 | 0.2799 | 0.7887 | 0.8437 | 0.3 | |
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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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