Token Classification
Transformers
Safetensors
PEFT
German
named-entity-recognition
german
xlm-roberta
lora
Instructions to use fau/GermaNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fau/GermaNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fau/GermaNER")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fau/GermaNER", device_map="auto") - PEFT
How to use fau/GermaNER with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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- `r=16`, `alpha=32`, `dropout=0.1`
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- LoRA applied to: `query`, `key`, `value` projection layers
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- **Max sequence length**: 128 tokens
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- **Mixed-precision training**:
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- **Training samples**: 44,000 sentences
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- **Epochs**: 2
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- `r=16`, `alpha=32`, `dropout=0.1`
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- LoRA applied to: `query`, `key`, `value` projection layers
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- **Max sequence length**: 128 tokens
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- **Mixed-precision training**: (fp16)
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- **Training samples**: 44,000 sentences
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