---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- filter_v2
metrics:
- f1
- accuracy
model-index:
- name: favs_filter_classification_v2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: filter_v2
      type: filter_v2
      config: default
      split: train
      args: default
    metrics:
    - name: F1
      type: f1
      value: 0.9666666666666667
    - name: Accuracy
      type: accuracy
      value: 0.9375
---

<!-- 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_filter_classification_v2

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the filter_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2721
- F1: 0.9667
- Roc Auc: 0.9772
- Accuracy: 0.9375

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.7591        | 1.0   | 14   | 0.6660          | 0.3137 | 0.5541  | 0.0      |
| 0.6506        | 2.0   | 28   | 0.5575          | 0.4706 | 0.6451  | 0.0625   |
| 0.5006        | 3.0   | 42   | 0.5010          | 0.5385 | 0.6846  | 0.0625   |
| 0.4416        | 4.0   | 56   | 0.4536          | 0.6538 | 0.7528  | 0.125    |
| 0.3815        | 5.0   | 70   | 0.4127          | 0.8070 | 0.8589  | 0.5      |
| 0.3468        | 6.0   | 84   | 0.3748          | 0.8621 | 0.8984  | 0.5625   |
| 0.3316        | 7.0   | 98   | 0.3487          | 0.8621 | 0.8984  | 0.5625   |
| 0.2834        | 8.0   | 112  | 0.3191          | 0.9    | 0.9317  | 0.6875   |
| 0.2565        | 9.0   | 126  | 0.2970          | 0.9492 | 0.9606  | 0.875    |
| 0.2241        | 10.0  | 140  | 0.2721          | 0.9667 | 0.9772  | 0.9375   |
| 0.214         | 11.0  | 154  | 0.2563          | 0.9492 | 0.9606  | 0.875    |
| 0.2041        | 12.0  | 168  | 0.2499          | 0.9492 | 0.9606  | 0.875    |
| 0.1831        | 13.0  | 182  | 0.2353          | 0.9492 | 0.9606  | 0.875    |
| 0.1852        | 14.0  | 196  | 0.2285          | 0.9492 | 0.9606  | 0.875    |
| 0.1636        | 15.0  | 210  | 0.2178          | 0.9667 | 0.9772  | 0.9375   |
| 0.1581        | 16.0  | 224  | 0.2110          | 0.9667 | 0.9772  | 0.9375   |
| 0.1473        | 17.0  | 238  | 0.2057          | 0.9492 | 0.9606  | 0.875    |
| 0.1479        | 18.0  | 252  | 0.2025          | 0.9667 | 0.9772  | 0.9375   |
| 0.141         | 19.0  | 266  | 0.2038          | 0.9667 | 0.9772  | 0.9375   |
| 0.1424        | 20.0  | 280  | 0.2032          | 0.9667 | 0.9772  | 0.9375   |


### Framework versions

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