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This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of large language models. It serves as a structured natural language processing resource, offering a collection of text data that includes dialogue, customer service interactions, and creative writing.
The main features of plexpt/chatgpt-corpus are: Language Corpora, Training Datasets, Pre-training Datasets, Large Language Model Training Resources, Natural Language Processing Resources, Prompt Engineering, Instruction Datasets, Learning and Research.
Projects with overlapping indexed features include: d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… brightmart/nlp_chinese_corpus — This is a large-scale collection of curated Chinese text corpora designed for training natural language processing… thunlp/ultrachat — UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and… esbatmop/mnbvc — MNBVC is a dataset pipeline and toolkit designed for the collection, cleaning, and normalization of massive text and… wdndev/llm_interview_note — This project is a comprehensive technical reference and educational resource focused on the lifecycle of large… zai-org/chatglm3 — ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a…
This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
This is a large-scale collection of curated Chinese text corpora designed for training natural language processing models. The project provides a variety of datasets, including a deduplicated archive of millions of news articles with titles and keywords, high-quality categorized question-and-answer pairs, and parallel translation corpora. The collection includes millions of aligned Chinese and English sentence pairs used for cross-lingual model training and machine translation development. It also contains filtered question-and-answer data organized by label for the construction of knowledge-
UltraChat is a collection of large-scale conversational datasets and instruction-tuning data designed for training and evaluating generative AI models. It provides structured JSON data consisting of complex, multi-round dialogue sequences intended to refine the performance of large language models in chat tasks. The project focuses on improving reasoning and response quality through a diverse set of interactions across multiple sectors. These datasets are used for supervised fine-tuning and instruction tuning workflows to improve how models follow complex directions and maintain context acros
MNBVC is a dataset pipeline and toolkit designed for the collection, cleaning, and normalization of massive text and code corpora used to train large language models. It provides specialized tools for harvesting source code, commit histories, and repository metadata from version control platforms, alongside a multilingual text corpus collector for gathering parallel text and academic papers. The project distinguishes itself through comprehensive capabilities for processing diverse document types, including a PDF-to-text converter that transforms complex layouts and formulas into structured JS