InternLM/Sage-SciPerception：連接科學編碼器與語言模型
InternLM/Sage-SciPerception
Sage Sci Perception 透過可訓練投影器，將分子圖、光譜、晶體結構、蛋白質及核酸序列和科學訊號映射至語言模型的輸入空間，支援自然語言分析及結構化科學回答。QM9S 的 Acc@1 為 49.75%，SciTS PHU01 的 Macro-F1 為 71.53%；投影器檢查點將於 Hugging Face 提供。
Sage Sci Perception · 书生·元征
Scientific perception for language models.
English · 简体中文
🤗 Models · GitHub · Demos · Results
Introduction
Sage Sci Perception (书生·元征) connects scientific encoders to a language model through trainable projectors. It maps molecular graphs, spectra, crystal structures, protein and nucleic-acid sequences, and scientific signals into the language model's input space, enabling natural-language analysis and structured scientific answers. Projectors are trained for each task.
Framework
Demos
Results
Selected results from task-specific checkpoints, evaluated on generated answers.
| Benchmark | Task | Metric ↑ | Score |
|---|---|---|---|
| QM9S | Joint-spectrum structure identification | Acc@1 (%) | 49.75 |
| JARVIS-DFT | Formation-energy prediction | MAD/MAE | 15.03 |
| MPBind, assembly-1 reconstruction | PPI interface sites | Mean per-protein F1 (%) | 61.12 |
| SciTS PHU01 | ECG classification | Macro-F1 (%) | 71.53 |
| SciTS NEU06 | Sleep staging | Macro-F1 (%) | 72.01 |
| BEACON ncRNA | RNA family classification | Accuracy (%) | 97.35 |
Models
Projector checkpoints will be available on Hugging Face.
來源：上海人工智能實驗室 InternLM：原創項目 · github.com