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:
- 09d14edcda7ef3f273de77ca728f3074a9db5d120cbd90790a2f9eef5704a364
- Size of remote file:
- 709 MB
- SHA256:
- 3c9db0620afb24403ddc54fceb8af61d27f482380402fc7b1a04c188c53eb931
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.