---
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: 0.823529411764706
    - name: Accuracy
      type: accuracy
      value: 0.0
---

<!-- 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: 0.4283
- F1: 0.8235
- Roc Auc: 0.9058
- Accuracy: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| No log        | 1.0   | 9    | 0.5901          | 0.6087 | 0.7289  | 0.0      |
| 0.665         | 2.0   | 18   | 0.5667          | 0.4545 | 0.6201  | 0.0      |
| 0.6099        | 3.0   | 27   | 0.5573          | 0.5    | 0.6558  | 0.0      |
| 0.5463        | 4.0   | 36   | 0.5561          | 0.3889 | 0.5966  | 0.0      |
| 0.5071        | 5.0   | 45   | 0.5484          | 0.3889 | 0.5966  | 0.0      |
| 0.4669        | 6.0   | 54   | 0.5462          | 0.4324 | 0.6193  | 0.0      |
| 0.4371        | 7.0   | 63   | 0.5326          | 0.4737 | 0.6420  | 0.0      |
| 0.4145        | 8.0   | 72   | 0.5202          | 0.5854 | 0.7102  | 0.0      |
| 0.3959        | 9.0   | 81   | 0.5020          | 0.6364 | 0.7468  | 0.0      |
| 0.3733        | 10.0  | 90   | 0.4944          | 0.6364 | 0.7468  | 0.0      |
| 0.3733        | 11.0  | 99   | 0.4675          | 0.7234 | 0.8149  | 0.0      |
| 0.3622        | 12.0  | 108  | 0.4626          | 0.7843 | 0.8742  | 0.0      |
| 0.3382        | 13.0  | 117  | 0.4499          | 0.8077 | 0.8969  | 0.0      |
| 0.3341        | 14.0  | 126  | 0.4482          | 0.8077 | 0.8969  | 0.0      |
| 0.315         | 15.0  | 135  | 0.4332          | 0.8077 | 0.8969  | 0.0      |
| 0.3253        | 16.0  | 144  | 0.4283          | 0.8235 | 0.9058  | 0.0      |
| 0.3031        | 17.0  | 153  | 0.4246          | 0.8077 | 0.8969  | 0.0      |
| 0.3071        | 18.0  | 162  | 0.4155          | 0.8077 | 0.8969  | 0.0      |
| 0.2944        | 19.0  | 171  | 0.4129          | 0.8077 | 0.8969  | 0.0      |
| 0.2996        | 20.0  | 180  | 0.4129          | 0.8077 | 0.8969  | 0.0      |


### Framework versions

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