---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-20000-ner
  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. -->

# bert-base-german-cased-20000-ner

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0527
- Precision: 0.7423
- Recall: 0.8643
- F1: 0.7987
- Accuracy: 0.9879

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.1   | 64   | 0.0867          | 0.5127    | 0.8679 | 0.6446 | 0.975    |
| No log        | 0.19  | 128  | 0.0539          | 0.4549    | 0.8643 | 0.5961 | 0.9774   |
| No log        | 0.29  | 192  | 0.0382          | 0.7763    | 0.8429 | 0.8082 | 0.9902   |
| No log        | 0.38  | 256  | 0.0405          | 0.7697    | 0.8714 | 0.8174 | 0.9898   |
| No log        | 0.48  | 320  | 0.0292          | 0.8027    | 0.8429 | 0.8223 | 0.9923   |
| No log        | 0.58  | 384  | 0.0478          | 0.7227    | 0.875  | 0.7916 | 0.9875   |
| No log        | 0.67  | 448  | 0.0293          | 0.7774    | 0.8107 | 0.7937 | 0.9918   |
| 0.09          | 0.77  | 512  | 0.0527          | 0.7423    | 0.8643 | 0.7987 | 0.9879   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1