Instructions to use peanutacake/ajmc_ner_de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peanutacake/ajmc_ner_de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="peanutacake/ajmc_ner_de")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("peanutacake/ajmc_ner_de") model = AutoModelForTokenClassification.from_pretrained("peanutacake/ajmc_ner_de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1c3dfe1861ae7c011def2054fa34785260113d39386f091bf6bd0608d104b80
- Size of remote file:
- 1.34 GB
- SHA256:
- 8908a16ba7cec27d1cb292c4c12170e1cc6c92fd4505e380edff8ffbe0be752b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.