README.md

---
language:
  - en
library_name: transformers
license: apache-2.0
pipeline_tag: text-classification
tags:
  - bibr
  - OECD
  - scientific-paper-classification
  - MiniLM
---

# bibr OECD/paper-type classifier — DeepSeek teacher, 2026-07-19

This is the DeepSeek-v4-Flash-teacher MiniLM candidate for bibr's multitask
scientific-paper classifier. It predicts OECD Level 1, OECD Level 2, and paper
type from title plus abstract.

## Architecture

- Encoder: `sentence-transformers/all-MiniLM-L6-v2`
- Input template: `title_abstract_v1`
- Maximum input length: 256 tokens
- Heads: OECD L1, OECD L2, paper type
- Model SHA-256:
  `7a46c595cf3bb8e1eff39303786e8b7d16c95ea87f9f06b12d4a753f0be1e580`

## Training provenance

- Training corpus rows: 122,363
- Deterministic split: 91,771 train / 12,237 validation / 18,355 test
- DeepSeek teacher changed 16,220 L1 labels relative to the matched OpenAlex
  baseline.
- Teacher-labelled rows are training supervision, not evaluation gold.

## Evaluation

On the frozen 1,500-row Phase-A panel:

| Model | Accuracy | Macro-F1 |
|---|---:|---:|
| Matched OpenAlex MiniLM baseline | 0.7333 | 0.7280 |
| This checkpoint | **0.8120** | **0.8096** |
| Previously shipped SPECTER2 checkpoint | 0.6887 | 0.6874 |

The matched MiniLM gain was +0.0787 accuracy and +0.0816 macro-F1. The paired
correctness table contained 158 teacher-only correct rows and 40 baseline-only
correct rows; exact McNemar p-value was `8.687e-18`.

The Phase-A reference is explicitly
`provisional_unadjudicated_codex_panel`. It is not human-adjudicated gold, so
these figures support candidate selection but not a final scientific-quality
claim.

## Bundle contract

The bibr loader requires:

- `model.safetensors`
- `tokenizer.json`
- `tokenizer_config.json`
- `label_maps.json`
- `inference_config.json`

`test_metrics.json`, `test_l1_diagnostics.json`, and `phaseA_metrics.json`
provide training and provisional-panel diagnostics.