Gliner Biomed Base V1.0

🚀 GLiNER-BioMed

GLiNER-BioMed 是一套专门用于生物医学领域的高效命名实体识别（NER）模型。它基于 GLiNER 框架，借助从大型生成式生物医学语言模型中提炼的合成注释，在生物医学实体识别任务中实现了零样本和少样本学习的先进性能，为传统 NER 模型和大语言模型提供了实用的替代方案。

🚀 快速开始

安装

安装官方的 GLiNER 库：

pip install gliner -U

使用

安装 GLiNER 库后，你可以轻松加载 GLiNER-biomed 模型并进行命名实体识别：

from gliner import GLiNER

model = GLiNER.from_pretrained("Ihor/gliner-biomed-base-v1.0")

text = """
The patient, a 45-year-old male, was diagnosed with type 2 diabetes mellitus and hypertension.
He was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. 
A recent lab test showed elevated HbA1c levels at 8.2%.
"""

labels = ["Disease", "Drug", "Drug dosage", "Drug frequency", "Lab test", "Lab test value", "Demographic information"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

预期输出：

45-year-old male => Demographic information
type 2 diabetes mellitus => Disease
hypertension => Disease
Metformin => Drug
500mg => Drug dosage
twice daily => Drug frequency
Lisinopril => Drug
10mg => Drug dosage
once daily => Drug frequency
HbA1c levels => Lab test
8.2% => Lab test value

✨ 主要特性

• 广泛的实体识别能力：GLiNER 是一种命名实体识别（NER）模型，能够使用双向变压器编码器（类似 BERT）识别任何实体类型。
• 高效的生物医学模型：GLiNER-biomed 与日内瓦大学的 DS4DH 合作开发，基于 GLiNER 框架引入了一套专门的高效开放生物医学 NER 模型。
• 零样本和少样本学习：利用从大型生成式生物医学语言模型中提炼的合成注释，在生物医学实体识别任务中实现了零样本和少样本学习的先进性能。

📦 安装指南

使用 pip 安装官方的 GLiNER 库：

pip install gliner -U

💻 使用示例

基础用法

from gliner import GLiNER

model = GLiNER.from_pretrained("Ihor/gliner-biomed-base-v1.0")

text = """
The patient, a 45-year-old male, was diagnosed with type 2 diabetes mellitus and hypertension.
He was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. 
A recent lab test showed elevated HbA1c levels at 8.2%.
"""

labels = ["Disease", "Drug", "Drug dosage", "Drug frequency", "Lab test", "Lab test value", "Demographic information"]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

📚 详细文档

模型信息

基准测试

我们在 8 个复杂的真实世界数据集上对模型进行了测试，并与其他 GLiNER 模型进行了比较：

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📄 许可证

本项目采用 apache-2.0 许可证。

🔗 引用

本工作

如果在你的工作中使用了 GLiNER-biomed 模型，请引用：

@misc{yazdani2025glinerbiomedsuiteefficientmodels,
      title={GLiNER-biomed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition},
      author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
      year={2025},
      eprint={2504.00676},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.00676},
}

先前工作

@misc{zaratiana2023gliner,
      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
      year={2023},
      eprint={2311.08526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{stepanov2024gliner,
      title={GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks},
      author={Ihor Stepanov and Mykhailo Shtopko},
      year={2024},
      eprint={2406.12925},
      archivePrefix={arXiv},
      primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'}
}