transformers pytorch bert text-classification generated_from_trainer dataset:filter_v2 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- filter_v2
license: apache-2.0
metrics:
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: favs_filter_classification_v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: filter_v2
      type: filter_v2
      config: default
      split: train
      args: default
    metrics:
    - type: f1
      value: 0.9761904761904762
      name: F1
    - type: accuracy
      value: 0.9545454545454546
      name: Accuracy
---

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# 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: 0.2016
- F1: 0.9762
- Roc Auc: 0.9844
- Accuracy: 0.9545

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6596        | 1.0   | 16   | 0.6086          | 0.2687 | 0.5474  | 0.0      |
| 0.5448        | 2.0   | 32   | 0.5354          | 0.3824 | 0.6063  | 0.0      |
| 0.5106        | 3.0   | 48   | 0.4874          | 0.4444 | 0.6382  | 0.0455   |
| 0.4353        | 4.0   | 64   | 0.4301          | 0.5352 | 0.6889  | 0.1818   |
| 0.3699        | 5.0   | 80   | 0.3890          | 0.6579 | 0.7640  | 0.3636   |
| 0.349         | 6.0   | 96   | 0.3663          | 0.6667 | 0.7633  | 0.3182   |
| 0.3104        | 7.0   | 112  | 0.3327          | 0.7105 | 0.7953  | 0.4545   |
| 0.3023        | 8.0   | 128  | 0.2971          | 0.7733 | 0.8303  | 0.5455   |
| 0.2676        | 9.0   | 144  | 0.2766          | 0.8395 | 0.8861  | 0.7727   |
| 0.2374        | 10.0  | 160  | 0.2541          | 0.8537 | 0.8980  | 0.7727   |
| 0.2238        | 11.0  | 176  | 0.2399          | 0.9024 | 0.9293  | 0.8182   |
| 0.2084        | 12.0  | 192  | 0.2221          | 0.9286 | 0.9531  | 0.8636   |
| 0.2143        | 13.0  | 208  | 0.2138          | 0.9286 | 0.9531  | 0.8636   |
| 0.1846        | 14.0  | 224  | 0.2016          | 0.9762 | 0.9844  | 0.9545   |
| 0.1812        | 15.0  | 240  | 0.1957          | 0.9762 | 0.9844  | 0.9545   |
| 0.1756        | 16.0  | 256  | 0.1881          | 0.9647 | 0.9806  | 0.9091   |
| 0.1662        | 17.0  | 272  | 0.1845          | 0.9762 | 0.9844  | 0.9545   |
| 0.1715        | 18.0  | 288  | 0.1802          | 0.9762 | 0.9844  | 0.9545   |
| 0.1585        | 19.0  | 304  | 0.1782          | 0.9762 | 0.9844  | 0.9545   |
| 0.1595        | 20.0  | 320  | 0.1775          | 0.9762 | 0.9844  | 0.9545   |


### Framework versions

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