Instructions to use zeusfsx/instruction-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zeusfsx/instruction-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="zeusfsx/instruction-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("zeusfsx/instruction-detection") model = AutoModelForTokenClassification.from_pretrained("zeusfsx/instruction-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29b240f8c91eddd5882e4f686fa25942b7a16e2d8d082bffbdba587874af97e7
- Size of remote file:
- 438 MB
- SHA256:
- 3a218fa1522cc9be77cb5f8d51db83bf01d38a04196c973062faa74bbdb634d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.