GLiNER 项目下载及安装教程

GLiNER 项目下载及安装教程

GLiNER Generalist model for NER (Extract any entity types from texts) 项目地址: https://gitcode.com/gh_mirrors/gl/GLiNER

1. 项目介绍

GLiNER 是一个用于命名实体识别（Named Entity Recognition, NER）的通用且轻量级的模型。它能够识别文本中的任何实体类型，使用双向变换器编码器（如BERT），为资源受限场景提供了一个实用的替代方案。GLiNER 不仅灵活，而且成本较低，适用于各种NER任务。

2. 项目下载位置

GLiNER 项目托管在 GitHub 上，可以通过以下链接进行下载：

GLiNER GitHub 仓库

你可以使用以下命令克隆项目到本地：

git clone https://github.com/urchade/GLiNER.git

3. 项目安装环境配置

在安装 GLiNER 之前，你需要确保你的系统满足以下环境要求：

• Python 3.7 或更高版本
• pip 包管理工具
• 其他依赖项（将在安装过程中自动安装）

环境配置示例

以下是一个简单的环境配置示例，假设你已经安装了 Python 和 pip：

# 创建虚拟环境（可选）
python3 -m venv gliner_env
source gliner_env/bin/activate

# 更新 pip
pip install --upgrade pip

4. 项目安装方式

GLiNER 可以通过 pip 进行安装。进入项目目录后，运行以下命令：

cd GLiNER
pip install .

或者，你也可以直接使用以下命令进行安装：

pip install gliner

5. 项目处理脚本

安装完成后，你可以使用 GLiNER 提供的脚本来处理文本数据。以下是一个简单的示例脚本：

from gliner import GLiNER

# 初始化 GLiNER 模型
model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")

# 示例文本
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu], born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards, a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""

# 实体标签
labels = ["Person", "Award", "Date", "Competitions", "Teams"]

# 预测实体
entities = model.predict_entities(text, labels, threshold=0.5)

# 显示预测结果
for entity in entities:
    print(f"{entity['text']} => {entity['label']}")

运行上述脚本后，你将看到文本中识别出的实体及其对应的标签。

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通过以上步骤，你可以成功下载、安装并使用 GLiNER 项目进行命名实体识别任务。希望这篇教程对你有所帮助！

GLiNER Generalist model for NER (Extract any entity types from texts) 项目地址: https://gitcode.com/gh_mirrors/gl/GLiNER