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
| { | |
| "classification_report": { | |
| "Agricultural and Veterinary Sciences": { | |
| "f1-score": 0.7455357142857143, | |
| "precision": 0.7636031092821216, | |
| "recall": 0.7283035324901875, | |
| "support": 2293.0 | |
| }, | |
| "Engineering and Technology": { | |
| "f1-score": 0.804904214559387, | |
| "precision": 0.8033037626185378, | |
| "recall": 0.8065110565110565, | |
| "support": 3256.0 | |
| }, | |
| "Humanities and the Arts": { | |
| "f1-score": 0.7972758229284903, | |
| "precision": 0.797819173103135, | |
| "recall": 0.7967332123411979, | |
| "support": 2204.0 | |
| }, | |
| "Medical and Health Sciences": { | |
| "f1-score": 0.8291206296352354, | |
| "precision": 0.8154212563262876, | |
| "recall": 0.8432881773399015, | |
| "support": 3248.0 | |
| }, | |
| "Natural Sciences": { | |
| "f1-score": 0.6668681069647983, | |
| "precision": 0.6720462850182704, | |
| "recall": 0.6617691154422789, | |
| "support": 3335.0 | |
| }, | |
| "Social Sciences": { | |
| "f1-score": 0.7857319791924696, | |
| "precision": 0.7822441430332923, | |
| "recall": 0.7892510574769843, | |
| "support": 4019.0 | |
| }, | |
| "accuracy": 0.7719967311359303, | |
| "macro avg": { | |
| "f1-score": 0.7715727445943492, | |
| "precision": 0.7724062882302741, | |
| "recall": 0.7709760252669344, | |
| "support": 18355.0 | |
| }, | |
| "weighted avg": { | |
| "f1-score": 0.7715784906213174, | |
| "precision": 0.7713698855964962, | |
| "recall": 0.7719967311359303, | |
| "support": 18355.0 | |
| } | |
| }, | |
| "confidence_curve": [ | |
| { | |
| "accuracy": 0.7719967311359303, | |
| "coverage": 1.0, | |
| "macro_f1": 0.7715727445943492, | |
| "rows": 18355.0, | |
| "threshold": 0.0 | |
| }, | |
| { | |
| "accuracy": 0.7976120456416892, | |
| "coverage": 0.9262871152274584, | |
| "macro_f1": 0.7967970019133345, | |
| "rows": 17002.0, | |
| "threshold": 0.5 | |
| }, | |
| { | |
| "accuracy": 0.8593491035106222, | |
| "coverage": 0.7231816943612095, | |
| "macro_f1": 0.8561099696894608, | |
| "rows": 13274.0, | |
| "threshold": 0.7 | |
| }, | |
| { | |
| "accuracy": 0.8935934177683277, | |
| "coverage": 0.5893217107055299, | |
| "macro_f1": 0.8880868103664564, | |
| "rows": 10817.0, | |
| "threshold": 0.8 | |
| }, | |
| { | |
| "accuracy": 0.9318342525809645, | |
| "coverage": 0.38523563061836014, | |
| "macro_f1": 0.9213157044536953, | |
| "rows": 7071.0, | |
| "threshold": 0.9 | |
| }, | |
| { | |
| "accuracy": 0.961308750973773, | |
| "coverage": 0.2098065922092073, | |
| "macro_f1": 0.9281730445872894, | |
| "rows": 3851.0, | |
| "threshold": 0.95 | |
| } | |
| ], | |
| "confusion_matrix": { | |
| "labels": [ | |
| "Agricultural and Veterinary Sciences", | |
| "Engineering and Technology", | |
| "Humanities and the Arts", | |
| "Medical and Health Sciences", | |
| "Natural Sciences", | |
| "Social Sciences" | |
| ], | |
| "matrix": [ | |
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| [ | |
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| 3172 | |
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| } | |