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bert-base-german-cased-fine-tuned-ner

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bert-base-german-cased-fine-tuned-ner
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---
license: mit
tags:
- generated_from_trainer
datasets:
- germa_ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-german-cased-fine-tuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: germa_ner
      type: germa_ner
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8089260808926081
    - name: Recall
      type: recall
      value: 0.872836719337848
    - name: F1
      type: f1
      value: 0.8396670285921101
    - name: Accuracy
      type: accuracy
      value: 0.9748511630761677
---

<!-- 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-fine-tuned-ner

This model is a fine-tuned version of [bert-base-german-cased](https://huggingface.co/bert-base-german-cased) on the germa_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0966
- Precision: 0.8089
- Recall: 0.8728
- F1: 0.8397
- Accuracy: 0.9749

## 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: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.159         | 1.0   | 737  | 0.0922          | 0.7472    | 0.8461 | 0.7936 | 0.9703   |
| 0.0714        | 2.0   | 1474 | 0.0916          | 0.7886    | 0.8713 | 0.8279 | 0.9731   |
| 0.0319        | 3.0   | 2211 | 0.0966          | 0.8089    | 0.8728 | 0.8397 | 0.9749   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.0+cu111
- Datasets 2.1.0
- Tokenizers 0.12.1