Text Classification
Transformers
PyTorch
Safetensors
English
longformer
Generated from Trainer
Eval Results (legacy)
Instructions to use nbroad/longformer-base-health-fact with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nbroad/longformer-base-health-fact with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nbroad/longformer-base-health-fact")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nbroad/longformer-base-health-fact") model = AutoModelForSequenceClassification.from_pretrained("nbroad/longformer-base-health-fact") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 826ec90db08bed4dcccd9cd09a48ae515b5d62d0e2166a4cda66b3304d83e977
- Size of remote file:
- 595 MB
- SHA256:
- c844354abd643e3b56232e3f5a63ad2c01781dd446211a2cb0ac47cf7ae63c1d
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