--- title: "stanford_alpaca vs Keras-TextClassification" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tatsu-lab-stanford-alpaca-vs-yongzhuo-keras-textclassification" tools: ["tatsu-lab-stanford-alpaca", "yongzhuo-keras-textclassification"] --- # stanford_alpaca vs Keras-TextClassification *GraphCanon updated Jul 11, 2026* ## Verdict Pick stanford_alpaca when license: stanford_alpaca is Apache-2.0, Keras-TextClassification is MIT; pick Keras-TextClassification when license: Keras-TextClassification is MIT, stanford_alpaca is Apache-2.0. [stanford_alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html) reports 30k GitHub stars, 4.0k forks, and 188 open issues, last pushed Jul 17, 2024. [Keras-TextClassification](https://blog.csdn.net/rensihui) has 1.8k stars, 398 forks, and 4 open issues, last pushed Jun 17, 2024. Figures are from public GitHub metadata via [stanford_alpaca's repository](https://github.com/tatsu-lab/stanford_alpaca) and [Keras-TextClassification's repository](https://github.com/yongzhuo/Keras-TextClassification). | | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Tagline | Code and documentation to train Stanford's Alpaca models, and generate the data. | 中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RC | | Stars | 30,250 | 1,811 | | Forks | 3,985 | 398 | | Open issues | 188 | 4 | | Language | Python | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training, Vector Databases | Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [stanford_alpaca](/tools/tatsu-lab-stanford-alpaca.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Days since push | 724d | 753d | | Open issues (now) | 188 | 4 | | Owner type | Organization | User | | Security scan | 46 low (46 low) | No lockfile | | Full report | [trust report](/tools/tatsu-lab-stanford-alpaca/trust.md) | [trust report](/tools/yongzhuo-keras-textclassification/trust.md) | ## Choose when ### Choose stanford_alpaca if… - License: stanford_alpaca is Apache-2.0, Keras-TextClassification is MIT. - Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model, python. - Also covers LLM Frameworks. ### Choose Keras-TextClassification if… - License: Keras-TextClassification is MIT, stanford_alpaca is Apache-2.0. - Tags unique to Keras-TextClassification: albert, bert, capsule, charcnn. - Leaner open-issue backlog (4). ## When NOT to use stanford_alpaca - Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use Keras-TextClassification - Last GitHub push was 754 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on Keras-TextClassification. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between stanford_alpaca and Keras-TextClassification? stanford_alpaca: Code and documentation to train Stanford's Alpaca models, and generate the data.. Keras-TextClassification: 中文长文本分类、短句子分类、多标签分类、两句子相似度（Chinese Text Classification of Keras NLP, multi-label classify, or sentence classify, long or short），字词句向量嵌入层（embeddings）和网络层（graph）构建基类，FastText，TextCNN，CharCNN，TextRNN, RC. See the comparison table for live GitHub stats and shared categories. ### When should I choose stanford_alpaca over Keras-TextClassification? Choose stanford_alpaca over Keras-TextClassification when License: stanford_alpaca is Apache-2.0, Keras-TextClassification is MIT; Tags unique to stanford_alpaca: deep-learning, instruction-following, language-model, python; Also covers LLM Frameworks. ### When should I choose Keras-TextClassification over stanford_alpaca? Choose Keras-TextClassification over stanford_alpaca when License: Keras-TextClassification is MIT, stanford_alpaca is Apache-2.0; Tags unique to Keras-TextClassification: albert, bert, capsule, charcnn; Leaner open-issue backlog (4). ### When should I avoid stanford_alpaca? Last GitHub push was 725 days ago (dormant maintenance, Jul 17, 2024). Validate activity before betting a new project on stanford_alpaca. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid Keras-TextClassification? Last GitHub push was 754 days ago (dormant maintenance, Jun 17, 2024). Validate activity before betting a new project on Keras-TextClassification. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is stanford_alpaca or Keras-TextClassification more popular on GitHub? stanford_alpaca has more GitHub stars (30,250 vs 1,811). Stars measure visibility, not whether either tool fits your constraints. ### Are stanford_alpaca and Keras-TextClassification open source? Yes - both are open-source projects on GitHub (stanford_alpaca: Apache-2.0, Keras-TextClassification: MIT). ### Where can I find alternatives to stanford_alpaca or Keras-TextClassification? GraphCanon lists graph-backed alternatives at [stanford_alpaca alternatives](/tools/tatsu-lab-stanford-alpaca/alternatives) and [Keras-TextClassification alternatives](/tools/yongzhuo-keras-textclassification/alternatives) ([stanford_alpaca markdown twin](/tools/tatsu-lab-stanford-alpaca/alternatives.md), [Keras-TextClassification markdown twin](/tools/yongzhuo-keras-textclassification/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/tatsu-lab-stanford-alpaca-vs-yongzhuo-keras-textclassification.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, stanford_alpaca or Keras-TextClassification? stanford_alpaca: Dormant. Keras-TextClassification: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for stanford_alpaca and Keras-TextClassification? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [stanford_alpaca trust report](/tools/tatsu-lab-stanford-alpaca/trust); [Keras-TextClassification trust report](/tools/yongzhuo-keras-textclassification/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tatsu-lab-stanford-alpaca`](/api/graphcanon/graph?tool=tatsu-lab-stanford-alpaca) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_