Instructions to use ihk/skillner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ihk/skillner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ihk/skillner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ihk/skillner") model = AutoModelForTokenClassification.from_pretrained("ihk/skillner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff8943a1573e50e9c2b01a73b9f6f8eb5b09b7e7d47d068b59ac8c9e554296ae
- Size of remote file:
- 431 MB
- SHA256:
- 1a7099317ca4e6bd41e180aa3ef8032faa61ee6a62c33c5c3ac47ce9c69835b5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.