---
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