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
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| "accuracy_all_lower_bound": 0.812, | |
| "accuracy_scored": 0.812, | |
| "classification_report": { | |
| "Agricultural and Veterinary Sciences": { | |
| "f1-score": 0.7692307692307693, | |
| "precision": 0.8072916666666666, | |
| "recall": 0.7345971563981043, | |
| "support": 211.0 | |
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| "Engineering and Technology": { | |
| "f1-score": 0.8514851485148515, | |
| "precision": 0.8704453441295547, | |
| "recall": 0.8333333333333334, | |
| "support": 258.0 | |
| }, | |
| "Humanities and the Arts": { | |
| "f1-score": 0.8218527315914489, | |
| "precision": 0.8084112149532711, | |
| "recall": 0.8357487922705314, | |
| "support": 207.0 | |
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| "Medical and Health Sciences": { | |
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| "precision": 0.9071428571428571, | |
| "recall": 0.8698630136986302, | |
| "support": 292.0 | |
| }, | |
| "Natural Sciences": { | |
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| "precision": 0.6917293233082706, | |
| "recall": 0.7763713080168776, | |
| "support": 237.0 | |
| }, | |
| "Social Sciences": { | |
| "f1-score": 0.7953020134228188, | |
| "precision": 0.7873754152823921, | |
| "recall": 0.8033898305084746, | |
| "support": 295.0 | |
| }, | |
| "accuracy": 0.812, | |
| "macro avg": { | |
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| "precision": 0.8120659702471688, | |
| "recall": 0.8088839057043252, | |
| "support": 1500.0 | |
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| "weighted avg": { | |
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| "precision": 0.8155705822436659, | |
| "recall": 0.812, | |
| "support": 1500.0 | |
| } | |
| }, | |
| "cohen_kappa_scored": 0.7735384799881362, | |
| "confusion_matrix": { | |
| "columns": [ | |
| "Agricultural and Veterinary Sciences", | |
| "Engineering and Technology", | |
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| "Agricultural and Veterinary Sciences", | |
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| "coverage": 1.0, | |
| "generated_at": "2026-07-19T18:05:53.939674+00:00", | |
| "macro_f1_all_with_abstentions": 0.809598814807324, | |
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| "median_max_probability": 0.8443766236305237, | |
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| "n_scored": 1500, | |
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| "proposal_disagreement_diagnostic": { | |
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| "checkpoint_matches_openalex": 144, | |
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| "n_panel_openalex_disagree": 498, | |
| "reference_status": "provisional_unadjudicated_codex_panel" | |
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| "provenance": { | |
| "checkpoint_path": "/opt/bibr-training-oecd-run/oecd_minilm_ablation_20260719/checkpoints/oecd_minilm_deepseek_teacher_20260719", | |
| "encoder_name": "sentence-transformers/all-MiniLM-L6-v2", | |
| "inference_config_sha256": "44c7c38bbebf60b20eaa75ce173267ade9814e6acdd2f2a0084b2609f70814ca", | |
| "input_template": "title_abstract_v1", | |
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| "model_sha256": "7a46c595cf3bb8e1eff39303786e8b7d16c95ea87f9f06b12d4a753f0be1e580", | |
| "rows_path": "/opt/bibr-training-oecd-run/oecd_minilm_ablation_20260719/data/oecd_phaseA_panel_20260719.parquet", | |
| "rows_sha256": "3554e33eea4680848d99268ed0745fcdbcb95f9b88cf2019f67e327adad1cd28", | |
| "runtime": { | |
| "cuda_device": "NVIDIA GeForce RTX 3090", | |
| "device": "cuda", | |
| "python": "3.12.12", | |
| "sklearn": "1.8.0", | |
| "torch": "2.11.0+cu130", | |
| "transformers": "5.4.0" | |
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| "scripts/diagnose_paper_classifier.py": "943f7d1e84469c0e2d1817168b607e50ba12db2d94a95774820fa0f04407e39e", | |
| "src/bibr_training/models/oecd_multitask.py": "8ffeb67be8cac60b288270ef6b6a737e888b3437a399cf4e76471cdc7bd057bb", | |
| "src/bibr_training/paper_classifier/transformer_dataset.py": "1214996cd87cfedbfd26e9642c53add97095ebd173a64bbbf1348577df7c204e" | |
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| "source_root": "/home/jakub/pco" | |
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| "reference_status": "provisional_unadjudicated_codex_panel" | |
| } | |