Instructions to use kleinay/nominalization-candidate-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kleinay/nominalization-candidate-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kleinay/nominalization-candidate-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kleinay/nominalization-candidate-classifier") model = AutoModelForTokenClassification.from_pretrained("kleinay/nominalization-candidate-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ff585316531f1c1042db23b98059f63f56db1c475ab7d7607f9d7c809434e1f
- Size of remote file:
- 867 MB
- SHA256:
- 7d8984fe8795e44a809bb0de778f5afcf6fdbaaccfd33926d863c4dff54ebbb4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.