---
library_name: transformers
license: apache-2.0
base_model: mschiesser/ner-bert-german
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_model
  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. -->

# ner_model

This model is a fine-tuned version of [mschiesser/ner-bert-german](https://huggingface.co/mschiesser/ner-bert-german) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3135
- Precision: 0.0517
- Recall: 0.0070
- F1: 0.0123
- Accuracy: 0.9287

## 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: 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        | 0     | 0    | 2.5309          | 0.0       | 0.0    | 0.0    | 0.0171   |
| No log        | 1.0   | 5    | 0.4653          | 0.0       | 0.0    | 0.0    | 0.9205   |
| No log        | 2.0   | 10   | 0.3807          | 0.0       | 0.0    | 0.0    | 0.9205   |
| No log        | 3.0   | 15   | 0.3448          | 0.0323    | 0.0047 | 0.0081 | 0.9269   |
| No log        | 4.0   | 20   | 0.3248          | 0.0455    | 0.0070 | 0.0121 | 0.9283   |
| No log        | 5.0   | 25   | 0.3135          | 0.0517    | 0.0070 | 0.0123 | 0.9287   |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.2