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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filter_sort
metrics:
- f1
- accuracy
model-index:
- name: favs-filtersort-multilabel-classification-bert-base-cased
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: filter_sort
type: filter_sort
config: default
split: train
args: default
metrics:
- name: F1
type: f1
value: 0.7887323943661971
- name: Accuracy
type: accuracy
value: 0.3
---
<!-- 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. -->
# favs-filtersort-multilabel-classification-bert-base-cased
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_sort dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2822
- F1: 0.7887
- Roc Auc: 0.8437
- Accuracy: 0.3
## 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: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6319 | 1.0 | 12 | 0.5814 | 0.4889 | 0.6714 | 0.0 |
| 0.5697 | 2.0 | 24 | 0.5046 | 0.6111 | 0.7401 | 0.0 |
| 0.5224 | 3.0 | 36 | 0.4393 | 0.6923 | 0.8004 | 0.0 |
| 0.4666 | 4.0 | 48 | 0.4048 | 0.6835 | 0.7965 | 0.0 |
| 0.4188 | 5.0 | 60 | 0.3795 | 0.6933 | 0.7952 | 0.0 |
| 0.4023 | 6.0 | 72 | 0.3634 | 0.7027 | 0.7990 | 0.0 |
| 0.3776 | 7.0 | 84 | 0.3526 | 0.7143 | 0.7976 | 0.0 |
| 0.3635 | 8.0 | 96 | 0.3423 | 0.7143 | 0.7976 | 0.0 |
| 0.3648 | 9.0 | 108 | 0.3288 | 0.7059 | 0.7886 | 0.0 |
| 0.3284 | 10.0 | 120 | 0.3192 | 0.7429 | 0.8142 | 0.2 |
| 0.3267 | 11.0 | 132 | 0.3151 | 0.7353 | 0.8052 | 0.1 |
| 0.3113 | 12.0 | 144 | 0.3066 | 0.7536 | 0.8181 | 0.2 |
| 0.3043 | 13.0 | 156 | 0.3018 | 0.7606 | 0.8271 | 0.3 |
| 0.2924 | 14.0 | 168 | 0.2940 | 0.7606 | 0.8271 | 0.3 |
| 0.2843 | 15.0 | 180 | 0.2936 | 0.7714 | 0.8309 | 0.3 |
| 0.2794 | 16.0 | 192 | 0.2856 | 0.7778 | 0.8399 | 0.3 |
| 0.2678 | 17.0 | 204 | 0.2860 | 0.7714 | 0.8309 | 0.3 |
| 0.2631 | 18.0 | 216 | 0.2822 | 0.7887 | 0.8437 | 0.3 |
| 0.269 | 19.0 | 228 | 0.2806 | 0.7887 | 0.8437 | 0.3 |
| 0.2609 | 20.0 | 240 | 0.2799 | 0.7887 | 0.8437 | 0.3 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
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