# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Ruth/gbert-large-germaner")
model = AutoModelForTokenClassification.from_pretrained("Ruth/gbert-large-germaner")Quick Links
gbert-large-germaner
This model is a fine-tuned version of 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
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Dataset used to train Ruth/gbert-large-germaner
Evaluation results
- precision on germanerself-reported0.869
- recall on germanerself-reported0.886
- f1 on germanerself-reported0.877
- accuracy on germanerself-reported0.978
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Ruth/gbert-large-germaner")