Instructions to use mschiesser/ner-bert-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mschiesser/ner-bert-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mschiesser/ner-bert-german")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mschiesser/ner-bert-german") model = AutoModelForTokenClassification.from_pretrained("mschiesser/ner-bert-german") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 582658788c4df5850fca1f7fe7b3d725837bb114164a2b28cc4e6c4a4dd7a829
- Size of remote file:
- 3.39 kB
- SHA256:
- 5e5b16b75dcc482aad16b09a8de768d2e5227928c9adb642359cbf627a13f9cb
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