README.md

metadata

language:
  - de
license: mit
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.
datasets:
  - germaner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: gbert-base-germaner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: germaner
          type: germaner
          args: default
        metrics:
          - name: precision
            type: precision
            value: 0.8520523797532108
          - name: recall
            type: recall
            value: 0.8754204398447607
          - name: f1
            type: f1
            value: 0.8635783563042368
          - name: accuracy
            type: accuracy
            value: 0.976147969774973

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