DeepKE-LLM 的安装 方法一:In-Context Learning 上下文学习 使用方法 方法二:Lora微调 数据 训练 KnowLM 预测 方法三:P-Tuning微调 训练 预测 参考 简介 DeepKE 作为一个全面的知识提取工具包,不仅在构建知识图谱方面展现出卓越性能,还针对多种场景(如cnSchema、低资源环境、文档级处理和多模态分析)提供了强大支持...
创建并配置好虚拟环境后,请通过以下命令激活名为 deepke-llm 的环境: conda activate deepke-llm mkdir results mkdir lora mkdir data 数据放在目录 ./data 中。 🐐 3.2模型 以下是本仓库代码支持的一些基础模型:[llama, alpaca, vicuna, zhixi, falcon, baichuan, chatglm, qwen, moss, openba] 🌰 ...
DeepKE-LLM In the era of large models, DeepKE-LLM utilizes a completely new environment dependency. conda create -n deepke-llm python=3.9 conda activate deepke-llm cd example/llm pip install -r requirements.txt Please note that therequirements.txtfile is located in theexample/llmfolder. ...
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Please refer to DeepKE/example/llm/README.md to create a Python virtual environment, and activate the deepke-llm environment: conda activate deepke-llm mkdir results mkdir lora mkdir data Place the data in the directory ./data 🐐 3.2Model Here are some of the models supported by the code ...
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[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction - DeepKE/example/llm/InstructKGC/ft_scripts/fine_moss.bash at main · zjunlp/DeepKE
转换前: 数据的格式需要符合DeepKE/example/llm/InstructKGC/data目录下为各项任务(如NER、RE、EE等)规定的结构。以NER任务为例,输入文本应标记为input字段,而标注数据则应标记为entity字段,它是一个包含多个entity和entity_type键值对的字典列表。 转换后: 将得到包含input文本、instruction指令(详细说明了候选标签列表...
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction - DeepKE/example/llm/InstructKGC/ft_scripts/fine_chatglm.bash at main · zjunlp/DeepKE