| language: | |
| - de | |
| license: mit | |
| datasets: | |
| - germaner | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| base_model: deepset/gbert-large | |
| model-index: | |
| - name: gbert-large-germaner | |
| results: | |
| - task: | |
| type: token-classification | |
| name: Token Classification | |
| dataset: | |
| name: germaner | |
| type: germaner | |
| args: default | |
| metrics: | |
| - type: precision | |
| value: 0.8693333333333333 | |
| name: precision | |
| - type: recall | |
| value: 0.885640362225097 | |
| name: recall | |
| - type: f1 | |
| value: 0.8774110861903236 | |
| name: f1 | |
| - type: accuracy | |
| value: 0.9784210744831022 | |
| name: accuracy | |
| <!-- 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. --> | |
| # gbert-large-germaner | |
| This model is a fine-tuned version of [deepset/gbert-large](https://huggingface.co/deepset/gbert-large) on the germaner dataset. | |
| It achieves the following results on the evaluation set: | |
| - precision: 0.8693 | |
| - recall: 0.8856 | |
| - f1: 0.8774 | |
| - accuracy: 0.9784 | |
| ## 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: 8 | |
| - eval_batch_size: 8 | |
| - learning_rate: 2e-05 | |
| - weight_decay_rate: 0.01 | |
| - num_warmup_steps: 0 | |
| - fp16: True | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Datasets 1.18.0 | |
| - Tokenizers 0.12.1 | |