---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: distilbert-base-german-cased
model-index:
- name: distilbert-base-german-cased-finetuned-tagesschau-subcategories
  results: []
---

<!-- 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. -->

# distilbert-base-german-cased-finetuned-tagesschau-subcategories

This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5230
- Accuracy: 0.8267

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.4   | 30   | 1.5130          | 0.5733   |
| No log        | 0.8   | 60   | 1.0629          | 0.7133   |
| No log        | 1.2   | 90   | 0.8431          | 0.76     |
| No log        | 1.6   | 120  | 0.7812          | 0.7467   |
| No log        | 2.0   | 150  | 0.6373          | 0.78     |
| No log        | 2.4   | 180  | 0.5567          | 0.8133   |
| No log        | 2.8   | 210  | 0.5650          | 0.8067   |
| No log        | 3.2   | 240  | 0.5068          | 0.8267   |
| No log        | 3.6   | 270  | 0.5230          | 0.8267   |
| No log        | 4.0   | 300  | 0.5318          | 0.8133   |
| No log        | 4.4   | 330  | 0.5327          | 0.8067   |
| No log        | 4.8   | 360  | 0.4918          | 0.82     |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2