Text Classification
Transformers
Safetensors
English
bibr
OECD
scientific-paper-classification
MiniLM
Instructions to use scienceverse/bibr-paper-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scienceverse/bibr-paper-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scienceverse/bibr-paper-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scienceverse/bibr-paper-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "test_combined_macro_f1": 0.6698617929411549, | |
| "test_l1_eval_rows": 18355.0, | |
| "test_l1_macro_f1": 0.7715727445943492, | |
| "test_l1_micro_f1": 0.7719967311359303, | |
| "test_l2_constrained_macro_f1": 0.4661771349151901, | |
| "test_l2_constrained_micro_f1": 0.5740845070422536, | |
| "test_l2_eval_rows": 15975.0, | |
| "test_l2_macro_f1": 0.4719512458194132, | |
| "test_l2_micro_f1": 0.5799687010954616, | |
| "test_loss": 2.2446630001068115, | |
| "test_paper_type_eval_rows": 548.0, | |
| "test_paper_type_macro_f1": 0.7660613884097018, | |
| "test_paper_type_micro_f1": 0.8959854014598541, | |
| "test_runtime": 55.1841, | |
| "test_samples_per_second": 332.614, | |
| "test_steps_per_second": 5.201 | |
| } | |