---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- hyperpartisan_news_detection
metrics:
- accuracy
model-index:
- name: hyperpartisan-classifier
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hyperpartisan_news_detection
      type: hyperpartisan_news_detection
      config: bypublisher
      split: validation
      args: bypublisher
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9988466666666667
---

<!-- 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. -->

# hyperpartisan-classifier

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the hyperpartisan_news_detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0036
- Accuracy: 0.9988

## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1441        | 0.11  | 1000  | 0.1391          | 0.9453   |
| 0.1248        | 0.21  | 2000  | 0.1042          | 0.9595   |
| 0.1027        | 0.32  | 3000  | 0.0913          | 0.9647   |
| 0.0928        | 0.43  | 4000  | 0.0827          | 0.9688   |
| 0.0992        | 0.53  | 5000  | 0.0799          | 0.9698   |
| 0.0881        | 0.64  | 6000  | 0.0710          | 0.9741   |
| 0.078         | 0.75  | 7000  | 0.0640          | 0.9762   |
| 0.0708        | 0.85  | 8000  | 0.0626          | 0.9764   |
| 0.0696        | 0.96  | 9000  | 0.0564          | 0.9792   |
| 0.0586        | 1.07  | 10000 | 0.0516          | 0.9813   |
| 0.0558        | 1.17  | 11000 | 0.0507          | 0.9815   |
| 0.0531        | 1.28  | 12000 | 0.0463          | 0.9829   |
| 0.0585        | 1.39  | 13000 | 0.0468          | 0.9831   |
| 0.0488        | 1.49  | 14000 | 0.0403          | 0.9854   |
| 0.057         | 1.6   | 15000 | 0.0393          | 0.9865   |
| 0.0514        | 1.71  | 16000 | 0.0349          | 0.9879   |
| 0.052         | 1.81  | 17000 | 0.0366          | 0.9868   |
| 0.0572        | 1.92  | 18000 | 0.0300          | 0.9895   |
| 0.0311        | 2.03  | 19000 | 0.0309          | 0.9893   |
| 0.0332        | 2.13  | 20000 | 0.0262          | 0.9908   |
| 0.0396        | 2.24  | 21000 | 0.0250          | 0.9914   |
| 0.0314        | 2.35  | 22000 | 0.0223          | 0.9924   |
| 0.0361        | 2.45  | 23000 | 0.0236          | 0.9919   |
| 0.0289        | 2.56  | 24000 | 0.0197          | 0.9933   |
| 0.0322        | 2.67  | 25000 | 0.0182          | 0.9939   |
| 0.0416        | 2.77  | 26000 | 0.0183          | 0.9937   |
| 0.0273        | 2.88  | 27000 | 0.0159          | 0.9946   |
| 0.0317        | 2.99  | 28000 | 0.0152          | 0.9949   |
| 0.0203        | 3.09  | 29000 | 0.0132          | 0.9957   |
| 0.0182        | 3.2   | 30000 | 0.0146          | 0.9953   |
| 0.0165        | 3.31  | 31000 | 0.0123          | 0.9961   |
| 0.0184        | 3.41  | 32000 | 0.0105          | 0.9968   |
| 0.0208        | 3.52  | 33000 | 0.0103          | 0.9967   |
| 0.0187        | 3.63  | 34000 | 0.0083          | 0.9973   |
| 0.0183        | 3.73  | 35000 | 0.0076          | 0.9977   |
| 0.0258        | 3.84  | 36000 | 0.0073          | 0.9977   |
| 0.0114        | 3.95  | 37000 | 0.0066          | 0.9979   |
| 0.007         | 4.05  | 38000 | 0.0052          | 0.9983   |
| 0.0094        | 4.16  | 39000 | 0.0061          | 0.9981   |
| 0.0106        | 4.27  | 40000 | 0.0053          | 0.9983   |
| 0.0134        | 4.37  | 41000 | 0.0052          | 0.9984   |
| 0.0087        | 4.48  | 42000 | 0.0040          | 0.9987   |
| 0.018         | 4.59  | 43000 | 0.0047          | 0.9985   |
| 0.0118        | 4.69  | 44000 | 0.0041          | 0.9987   |
| 0.012         | 4.8   | 45000 | 0.0038          | 0.9988   |
| 0.0165        | 4.91  | 46000 | 0.0036          | 0.9988   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2