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
File size: 679 Bytes
c4dd45e b4ef7f4 c4dd45e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"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
}
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