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bert-german-ner

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bert-german-ner
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---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-german-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: validation
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.8333588604686782
    - name: Recall
      type: recall
      value: 0.8620088719898605
    - name: F1
      type: f1
      value: 0.8474417880227396
    - name: Accuracy
      type: accuracy
      value: 0.9292245320451997
---

<!-- 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-german-ner

This model is a fine-tuned version of [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3196
- Precision: 0.8334
- Recall: 0.8620
- F1: 0.8474
- Accuracy: 0.9292

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 300  | 0.3617          | 0.7310    | 0.7733 | 0.7516 | 0.8908   |
| 0.5428        | 2.0   | 600  | 0.2897          | 0.7789    | 0.8395 | 0.8081 | 0.9132   |
| 0.5428        | 3.0   | 900  | 0.2805          | 0.8147    | 0.8465 | 0.8303 | 0.9221   |
| 0.2019        | 4.0   | 1200 | 0.2816          | 0.8259    | 0.8498 | 0.8377 | 0.9260   |
| 0.1215        | 5.0   | 1500 | 0.2942          | 0.8332    | 0.8599 | 0.8463 | 0.9285   |
| 0.1215        | 6.0   | 1800 | 0.3053          | 0.8293    | 0.8619 | 0.8452 | 0.9287   |
| 0.0814        | 7.0   | 2100 | 0.3190          | 0.8249    | 0.8634 | 0.8437 | 0.9267   |
| 0.0814        | 8.0   | 2400 | 0.3196          | 0.8334    | 0.8620 | 0.8474 | 0.9292   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2