Instructions to use Emanuel/porttagger-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Emanuel/porttagger-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Emanuel/porttagger-base")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Emanuel/porttagger-base") model = AutoModelForTokenClassification.from_pretrained("Emanuel/porttagger-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2e3b465cf9b3a406dc01f8daf47dabff3fc24e76e55fdefb89bdbe884288799
- Size of remote file:
- 433 MB
- SHA256:
- ed00f3bc903d5d0baad453482ad505fda418faf2c68e5262ddd79b288cc6bfe6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.