---
library_name: transformers
license: mit
base_model: google-bert/bert-base-german-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-requirements
  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-finetuned-ner-requirements

This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4528
- Precision: 0.6724
- Recall: 0.6842
- F1: 0.6783
- Accuracy: 0.875

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 38   | 0.6381          | 0.6203    | 0.6082 | 0.6142 | 0.8239   |
| No log        | 2.0   | 76   | 0.4657          | 0.6718    | 0.6745 | 0.6732 | 0.8678   |
| No log        | 3.0   | 114  | 0.4412          | 0.6679    | 0.6979 | 0.6826 | 0.8746   |
| No log        | 4.0   | 152  | 0.4533          | 0.6705    | 0.6745 | 0.6725 | 0.8719   |
| No log        | 5.0   | 190  | 0.4528          | 0.6724    | 0.6842 | 0.6783 | 0.875    |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1