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README.md ADDED
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+ ---
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+ tasks:
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+ - text2sql
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+ model-type:
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+ - glm
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+ domain:
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+ - nlp
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+ frameworks:
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+ - pytorch
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+ backbone:
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+ - transformer
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+ containers:
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+ - registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/pytorch:1.8-cpu-py36-ubuntu18.04
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+ - registry-vpc.cn-shanghai.aliyuncs.com/cloud-dsw/tensorflow:1.12-cpu-py36-ubuntu18.04
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+ customized-quickstart: False
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+ finetune-support: False
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+ license: Apache License 2.0
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+ language:
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+ - en
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+ - fr
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+ - cn
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+ tags:
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+ - transformer
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+ pre-train: False
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+ datasets:
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+ train:
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+ - spider dataset
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+ - bird dataset
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+ ---
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+ # sql_codellama 介绍
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+ SQL-Codellama是一个用于text2SQL的模型。
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+ ## 模型底座
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+ 它是基于codellama模型构建的,该模型通过使用qlora进行训练。
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+ ## 训练数据
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+ 训练数据包含了spider、starcode等数据集。这个模型的目标是将自然语言查询转换为SQL查询。
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+ ## 功能
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+ Text to SQL( 以下简称Text2SQL),是将自然语言文本(Text)转换成结构化查询语言SQL的过程,属于自然语言处理-语义分析(Semantic Parsing)领域中的子任务。
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+ 它的目的可以简单概括为:“打破人与结构化数据之间的壁垒”,即普通用户可以通过自然语言描述完成复杂数据库的查询工作,得到想要的结果。
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+ 它通过学习语法、语义和查询意图来理解用户的问题,并根据对应的数据库结构生成相应的SQL查询语句。SQL-Codellama的训练过程经过了大量的数据预处理、特征提取和模型训练,以提高其准确性和性能。
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+ 它可以应用于各种领域,如数据分析、数据库查询优化等。SQL-Codellama的设计和训练过程是为了使其能够处理复杂的查询,并产生高质量的SQL查询结果。它的目标是为用户提供准确、高效的文本到SQL转换,从而帮助用户更轻松地进行数据库查询和数据分析。
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+
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+ ```bash
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+ git clone https://www.modelscope.cn/tomatoModelScope/sql_codellama.git
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+ ```
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+ "▁<EOT>"
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+ "▁<EOT>"
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+ "eot_token": "▁<EOT>",
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