Token Classification
Transformers
Safetensors
longformer
Generated from Trainer
Eval Results (legacy)
Instructions to use Theoreticallyhugo/longformer-full_labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Theoreticallyhugo/longformer-full_labels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Theoreticallyhugo/longformer-full_labels")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Theoreticallyhugo/longformer-full_labels") model = AutoModelForTokenClassification.from_pretrained("Theoreticallyhugo/longformer-full_labels") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0cb0261004c4177bb2a56946f8529bb582d02ee2aa21422661f9f93dc025f45
- Size of remote file:
- 1.01 GB
- SHA256:
- 58ff7032ee523c3655461fc35bfe4f2832ad5c684ce3aa06ed6966e33152caf6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.