README.md

metadata

language:
  - de
license: mit
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