README.md

metadata

language:
  - de
license: mit
datasets:
  - germaner
metrics:
  - precision
  - recall
  - f1
  - accuracy
widget:
  - text: >
      Philipp ist 26 Jahre alt und lebt in Nürnberg, Deutschland. Derzeit
      arbeitet er als Machine Learning Engineer und Tech Lead bei Hugging Face,
      um künstliche Intelligenz durch Open Source und Open Science zu
      demokratisieren.
base_model: deepset/gbert-base
model-index:
  - name: gbert-base-germaner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: germaner
          type: germaner
          args: default
        metrics:
          - type: precision
            value: 0.8520523797532108
            name: precision
          - type: recall
            value: 0.8754204398447607
            name: recall
          - type: f1
            value: 0.8635783563042368
            name: f1
          - type: accuracy
            value: 0.976147969774973
            name: accuracy

gbert-base-germaner

This model is a fine-tuned version of deepset/gbert-base on the germaner dataset. It achieves the following results on the evaluation set:

• precision: 0.8521
• recall: 0.8754
• f1: 0.8636
• accuracy: 0.9761

If you want to learn how to fine-tune BERT yourself using Keras and Tensorflow check out this blog post:

https://www.philschmid.de/huggingface-transformers-keras-tf

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:

• num_train_epochs: 5
• train_batch_size: 16
• eval_batch_size: 32
• learning_rate: 2e-05
• weight_decay_rate: 0.01
• num_warmup_steps: 0
• fp16: True

Framework versions

• Transformers 4.14.1
• Datasets 1.16.1
• Tokenizers 0.10.3