BettaFish (åūŪč) is a multi-agent public opinion analysis system that automates the end-to-end process of gathering, analyzing, and reporting on public sentiment across 30+ Chinese social media platforms. The system is built around four specialized agents: QueryEngine (news breadth search via Tavily), MediaEngine (multimodal search via Bocha), InsightEngine (database mining via SQLAlchemy), and ReportEngine (template-based report generation with intermediate representation and HTML/PDF/Markdown rendering). A ForumEngine coordinates agent collaboration through log monitoring and LLM-generated moderator comments. The architecture is a monolithic Flask application that manages multiple Streamlit sub-applications and CLI tools, with a separate MindSpider crawler subsystem for data collection. While primarily an application, it includes significant data/research components such as sentiment analysis model training scripts (BERT, GPT-2, Qwen3, traditional ML), a Weibo sentiment dataset, and data pipeline scripts. The system relies heavily on external LLM APIs (OpenAI-compatible), search APIs (Tavily, Bocha, Anspire), and system dependencies (Playwright, WeasyPrint). The codebase demonstrates solid engineering with reusable patterns like robust JSON parsing, chart validation/repair, and a hardened GitHub Actions star-history script, but suffers from significant code duplication across engines and tight coupling to external APIs.
text2vec is a Python library for converting text (words, sentences, paragraphs) into dense vector embeddings, with a strong focus on Chinese semantic similarity. It provides multiple embedding backends: Word2Vec (Tencent embeddings), Sentence-BERT, CoSENT, BGE, and BM25 for lexical ranking. The library offers a unified API via `SentenceModel` and `Similarity` classes, a command-line interface for batch embedding, and training/fine-tuning scripts for supervised and unsupervised text matching. It also includes deployment examples (FastAPI, Jina, Gradio) and evaluation benchmarks (C-MTEB, MTEB). While primarily an application/library, it has strong research components with detailed model comparison reports and dataset building scripts. The codebase is modular and well-documented, though some training code contains boilerplate repetition and dependency version ranges are broad, introducing potential drift.
This is a research repository from the SCIR-HI lab at Harbin Institute of Technology, focused on instruction-tuning large language models (LLaMA, Alpaca, Bloom, Huozi) with Chinese medical knowledge. The core contribution is the construction of Chinese medical instruction datasets using structured knowledge bases (cMeKG) and medical literature via GPT3.5, followed by LoRA-based fine-tuning. The repository provides scripts for fine-tuning, inference, and model export, along with pre-trained LoRA weights. While it demonstrates a complete research pipeline (data generation, training, evaluation), it is primarily a research artifact rather than a production application. The code is functional and includes a clean, reusable Prompter class for template management, but lacks software engineering rigor (no tests, no CI). Reproducibility is moderate: dependencies are pinned, but the data generation process is not fully automated, and external base models and LoRA weights must be obtained separately.
This repository is a curated collection of five large-scale Chinese NLP datasets (Wikipedia articles, news, QA pairs, community Q&A, and translation pairs) with descriptions and download links. It functions as a data catalog rather than a software project: there is no executable code, no ETL scripts, and no pipelines. The datasets are hosted externally (Google Drive, Baidu) and require manual download. The repository provides clear formatting examples and suggested use cases, but lacks any automation or reproducibility support such as download scripts, environment definitions, or data processing code. It is a valuable reference for researchers needing Chinese text corpora, but it is not a reusable data engineering artifact.
This repository is a Chinese-language chatbot built with Rasa 1.10.18, a now-deprecated version of the Rasa framework. It implements a simple customer service bot for telecom queries (phone bill and data usage) using a BERT-based NLU pipeline (HFTransformersNLP with bert-base-chinese) and a rule-based dialogue management system with story-driven flows. The codebase is minimal: a single custom action for querying consumption, a few intents and entities, and a set of dialogue stories. The project is functional but lacks tests, CI/CD, or modern Rasa practices. The pinned dependencies (rasa==1.10.18, transformers==2.4.1) are severely outdated, and the code relies on deprecated Rasa components. While the repository has accumulated stars (983) due to its accompanying blog series, the actual code is a basic Rasa template with little reusable craftsmanship. It serves as a learning example for building a Chinese chatbot with Rasa 1.x, but is not suitable for production or reuse without significant modernization.