Instructions to use RJ3vans/CLNspanTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/CLNspanTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/CLNspanTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/CLNspanTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/CLNspanTagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb93f489ccb229c273f62a3c4a97fe61cecca86edbdeedb733dad09ac8c8c966
- Size of remote file:
- 1.33 GB
- SHA256:
- 96eb84913051f2a329f9dc2a0ade7b7a70d1c1232c4385ccb16b6ecf120012a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.