Instructions to use lukasweber/WG_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukasweber/WG_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lukasweber/WG_BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lukasweber/WG_BERT") model = AutoModelForTokenClassification.from_pretrained("lukasweber/WG_BERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9cd35b8be2a3c651c804848be5c7343b0cf45ce54f7b601b2e50f54dfaac4d8
- Size of remote file:
- 436 MB
- SHA256:
- 92312d7abc61188541c60492b66babeed299be163bfb57c7eb64428c70f98bed
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