---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-german-cased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wikiann
      type: wikiann
      config: de
      split: validation
      args: de
    metrics:
    - name: Precision
      type: precision
      value: 0.8400889939511924
    - name: Recall
      type: recall
      value: 0.8744391373570705
    - name: F1
      type: f1
      value: 0.8569199673770433
    - name: Accuracy
      type: accuracy
      value: 0.9548258089954094
---

<!-- 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. -->

# distilbert-base-german-cased-finetuned-ner

This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1871
- Precision: 0.8401
- Recall: 0.8744
- F1: 0.8569
- Accuracy: 0.9548

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1785        | 1.0   | 2500 | 0.1728          | 0.8134    | 0.8414 | 0.8271 | 0.9490   |
| 0.1252        | 2.0   | 5000 | 0.1743          | 0.8434    | 0.8659 | 0.8545 | 0.9545   |
| 0.0867        | 3.0   | 7500 | 0.1871          | 0.8401    | 0.8744 | 0.8569 | 0.9548   |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.2