---
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: 0.767123287671233
    - 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: 0.2847
- F1: 0.7671
- Roc Auc: 0.8361
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.7037        | 1.0   | 12   | 0.6056          | 0.5714 | 0.7341  | 0.0      |
| 0.5962        | 2.0   | 24   | 0.5212          | 0.5067 | 0.6787  | 0.0      |
| 0.5393        | 3.0   | 36   | 0.4396          | 0.6197 | 0.7439  | 0.1      |
| 0.4682        | 4.0   | 48   | 0.3963          | 0.5714 | 0.7079  | 0.0      |
| 0.409         | 5.0   | 60   | 0.3710          | 0.6061 | 0.7297  | 0.0      |
| 0.3923        | 6.0   | 72   | 0.3571          | 0.6286 | 0.7477  | 0.1      |
| 0.3682        | 7.0   | 84   | 0.3439          | 0.6849 | 0.7862  | 0.2      |
| 0.367         | 8.0   | 96   | 0.3286          | 0.6479 | 0.7605  | 0.1      |
| 0.3633        | 9.0   | 108  | 0.3194          | 0.6761 | 0.7772  | 0.2      |
| 0.3359        | 10.0  | 120  | 0.3145          | 0.6761 | 0.7772  | 0.2      |
| 0.3327        | 11.0  | 132  | 0.3054          | 0.6957 | 0.7848  | 0.2      |
| 0.3206        | 12.0  | 144  | 0.2998          | 0.7297 | 0.8156  | 0.2      |
| 0.3125        | 13.0  | 156  | 0.2926          | 0.7222 | 0.8066  | 0.2      |
| 0.3073        | 14.0  | 168  | 0.2847          | 0.7671 | 0.8361  | 0.3      |
| 0.2972        | 15.0  | 180  | 0.2819          | 0.7606 | 0.8271  | 0.3      |
| 0.2933        | 16.0  | 192  | 0.2773          | 0.7606 | 0.8271  | 0.3      |
| 0.2798        | 17.0  | 204  | 0.2752          | 0.7606 | 0.8271  | 0.3      |
| 0.2737        | 18.0  | 216  | 0.2731          | 0.7606 | 0.8271  | 0.3      |
| 0.2866        | 19.0  | 228  | 0.2720          | 0.7606 | 0.8271  | 0.3      |
| 0.2729        | 20.0  | 240  | 0.2713          | 0.7606 | 0.8271  | 0.3      |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1