---
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: 0.0368
- Precision: 0.8221
- Recall: 0.875
- F1: 0.8478
- Accuracy: 0.9920

## 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   | 0.0427          | 0.7796    | 0.8714 | 0.8229 | 0.9893   |
| No log        | 0.19  | 128  | 0.0472          | 0.5471    | 0.85   | 0.6657 | 0.9831   |
| No log        | 0.29  | 192  | 0.0384          | 0.7897    | 0.8179 | 0.8035 | 0.9899   |
| No log        | 0.38  | 256  | 0.0488          | 0.4970    | 0.8786 | 0.6348 | 0.9793   |
| No log        | 0.48  | 320  | 0.0412          | 0.7548    | 0.8464 | 0.7980 | 0.9895   |
| No log        | 0.58  | 384  | 0.0437          | 0.8373    | 0.8821 | 0.8591 | 0.9914   |
| No log        | 0.67  | 448  | 0.0399          | 0.7727    | 0.85   | 0.8095 | 0.9899   |
| 0.0914        | 0.77  | 512  | 0.0394          | 0.7859    | 0.8786 | 0.8297 | 0.9899   |
| 0.0914        | 0.86  | 576  | 0.0368          | 0.8221    | 0.875  | 0.8478 | 0.9920   |


### Framework versions

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