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
| { | |
| "epoch": 3.0, | |
| "eval_samples": 1233, | |
| "test_false_f1": 0.789407313997478, | |
| "test_loss": 0.616358757019043, | |
| "test_macro_f1": 0.6733562800163174, | |
| "test_micro_f1": 0.7923763179237632, | |
| "test_mixture_f1": 0.5283950617283951, | |
| "test_runtime": 37.1249, | |
| "test_samples_per_second": 33.212, | |
| "test_steps_per_second": 1.051, | |
| "test_true_f1": 0.9064869418702612, | |
| "test_unproven_f1": 0.46913580246913583, | |
| "train_loss": 0.49966207906692944, | |
| "train_runtime": 2323.0594, | |
| "train_samples": 9804, | |
| "train_samples_per_second": 12.661, | |
| "train_steps_per_second": 0.792 | |
| } |