---
license: mit
base_model: dbmdz/bert-base-german-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-20000-ner-uncased
  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-uncased

This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Precision: 0.8871
- Recall: 0.9013
- F1: 0.8941
- Accuracy: 0.9848

## 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: 96
- eval_batch_size: 96
- 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        | 0.34  | 64   | 0.0573          | 0.8859    | 0.8526 | 0.8689 | 0.9837   |
| No log        | 0.68  | 128  | 0.0654          | 0.8107    | 0.8957 | 0.8511 | 0.9808   |
| No log        | 1.02  | 192  | 0.0531          | 0.8654    | 0.8846 | 0.8749 | 0.9842   |
| No log        | 1.35  | 256  | 0.0467          | 0.8847    | 0.8853 | 0.8850 | 0.9857   |
| No log        | 1.69  | 320  | 0.0466          | 0.9102    | 0.8883 | 0.8992 | 0.9864   |
| No log        | 2.03  | 384  | 0.0467          | 0.8794    | 0.8951 | 0.8872 | 0.9854   |
| No log        | 2.37  | 448  | 0.0520          | 0.8864    | 0.9001 | 0.8932 | 0.9851   |
| 0.0531        | 2.71  | 512  | 0.0549          | 0.8894    | 0.8877 | 0.8885 | 0.9854   |
| 0.0531        | 3.05  | 576  | 0.0534          | 0.8942    | 0.8920 | 0.8931 | 0.9857   |
| 0.0531        | 3.39  | 640  | 0.0526          | 0.8917    | 0.8994 | 0.8956 | 0.9856   |
| 0.0531        | 3.72  | 704  | 0.0576          | 0.9049    | 0.8976 | 0.9012 | 0.9857   |
| 0.0531        | 4.06  | 768  | 0.0700          | 0.8529    | 0.9229 | 0.8865 | 0.9830   |
| 0.0531        | 4.4   | 832  | 0.0657          | 0.8716    | 0.9167 | 0.8936 | 0.9840   |
| 0.0531        | 4.74  | 896  | 0.0617          | 0.8871    | 0.9013 | 0.8941 | 0.9848   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3