yuzhimanhua/Awesome-Scientific-Language-Models

• highreadme#1

  Reposition README opening to explicitly state 'awesome list' and 'survey' nature

  原因：

  当前

  A curated list of pre-trained language models in scientific domains (e.g., **mathematics**, **physics**, **chemistry**, **materials science**, **biology**, **medicine**, **geoscience**), covering different model sizes (from **100M** to **100B parameters**) and modalities (e.g., **language**, **graph**, **vision**, **table**, **molecule**, **protein**, **genome**, **climate time series**).

  复制粘贴的修复

  复制

  This repository is **Awesome Scientific Language Models**, a comprehensive, curated **awesome list** and **survey** of pre-trained language models in scientific domains (e.g., **mathematics**, **physics**, **chemistry**, **materials science**, **biology**, **medicine**, **geoscience**). It covers different model sizes (from **100M** to **100B parameters**) and modalities (e.g., **language**, **graph**, **vision**, **table**, **molecule**, **protein**, **genome**, **climate time series**).

  待办完成忽略
• mediumtopics#2

  Add 'awesome-list' to repository topics

  原因：

  当前

  ai4science, awesome-resources, large-language-models, scientific-text-mining, survey

  复制粘贴的修复

  复制

  ai4science, awesome-resources, awesome-list, large-language-models, scientific-text-mining, survey

  待办完成忽略
• mediumreadme#3

  Add a 'Why this list?' section to highlight differentiators

  原因：

  复制粘贴的修复

  复制

  ## Why Awesome Scientific Language Models?
  
  Unlike general-purpose LLM lists or those focused on a single scientific field (e.g., biomedical), this repository provides a uniquely curated and comprehensive overview of Language Models specifically applied across diverse **scientific domains** such as mathematics, physics, chemistry, materials science, biology, medicine, and geoscience.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Where can I find a comprehensive list of large language models for scientific research?

  你：未被推荐
  AI 推荐顺序：

  1. Hugging Face Models
  2. Papers With Code
  3. arXiv
  4. Google Scholar
  5. BioNLP Shared Tasks
  6. ChemNLP
  7. Materials Project
  8. Towards Data Science
  9. SyncedReview
  10. ChatGPT
  11. GPT-4

  AI 推荐了 11 个替代方案，却始终没点名 yuzhimanhua/Awesome-Scientific-Language-Models。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  What are the best pre-trained language models for chemistry or biology applications?

  你：未被推荐
  AI 推荐顺序：

  1. ChemBERTa-2
  2. BioMegatron
  3. PubMedBERT
  4. MolFormer
  5. SciBERT
  6. ESM-2

  AI 推荐了 6 个替代方案，却始终没点名 yuzhimanhua/Awesome-Scientific-Language-Models。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of yuzhimanhua/Awesome-Scientific-Language-Models?

  pass
  ⚠AI 未点名 yuzhimanhua/Awesome-Scientific-Language-Models —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts yuzhimanhua/Awesome-Scientific-Language-Models in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 yuzhimanhua/Awesome-Scientific-Language-Models

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo yuzhimanhua/Awesome-Scientific-Language-Models solve, and who is the primary audience?

  pass
  ⚠AI 未点名 yuzhimanhua/Awesome-Scientific-Language-Models —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？