模型介绍
内容详情
替代品
模型简介
GLiNER 为传统 NER 模型提供了实用替代方案，后者仅限于预定义实体，而大型语言模型（LLMs）虽然灵活，但在资源受限的场景下成本高昂且体积庞大。
模型特点
灵活实体识别
能够识别任何用户定义的实体类型，而不仅限于预定义实体。
高效性能
相比大型语言模型，在资源受限的场景下更高效且体积更小。
多语言支持
支持多种语言的命名实体识别任务。
模型能力
命名实体识别
多语言文本处理
自定义实体类型识别
使用案例
信息提取
人物信息提取
从文本中识别和提取人物姓名及相关信息。
如识别 'Cristiano Ronaldo dos Santos Aveiro' 为 'person' 实体
奖项信息提取
从文本中识别和提取奖项名称。
如识别 'Ballon d'Or' 为 'award' 实体
日期信息提取
从文本中识别和提取日期信息。
如识别 '5 February 1985' 为 'date' 实体
体育新闻分析
球队信息提取
从体育新闻中识别和提取球队名称。
如识别 'Al Nassr' 和 'Portugal national team' 为 'teams' 实体
比赛信息提取
从体育新闻中识别和提取比赛名称。
如识别 'UEFA Champions Leagues' 和 'UEFA European Championship' 为 'competitions' 实体
license: apache-2.0
language:
- multilingual
library_name: gliner
datasets: - urchade/pile-mistral-v0.1
pipeline_tag: token-classification
关于
GLiNER 是一个命名实体识别（NER）模型，能够通过双向 Transformer 编码器（类似 BERT）识别任何实体类型。它为传统 NER 模型提供了实用替代方案，后者仅限于预定义实体，而大型语言模型（LLMs）虽然灵活，但在资源受限的场景下成本高昂且体积庞大。
链接
- 论文: https://arxiv.org/abs/2311.08526
- 代码库: https://github.com/urchade/GLiNER
安装
要使用此模型，必须安装 GLiNER Python 库：
!pip install gliner -U
使用
下载 GLiNER 库后，可以导入 GLiNER 类。然后使用 GLiNER.from_pretrained 加载此模型，并通过 predict_entities 预测实体。
from gliner import GLiNER
model = GLiNER.from_pretrained("gliner-community/gliner_small-v2.5", load_tokenizer=True)
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,[note 3] 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.
"""
labels = ["person", "award", "date", "competitions", "teams"]
entities = model.predict_entities(text, labels)
for entity in entities:
print(entity["text"], "=>", entity["label"])
Cristiano Ronaldo dos Santos Aveiro => person
5 February 1985 => date
Al Nassr => teams
Portugal national team => teams
Ballon d'Or => award
UEFA Men's Player of the Year Awards => award
European Golden Shoes => award
UEFA Champions Leagues => competitions
UEFA European Championship => competitions
UEFA Nations League => competitions
Champions League => competitions
European Championship => competitions
命名实体识别基准测试结果
以下是模型当前版本与之前版本的性能对比：
其他数据集上的结果
| 模型 | 数据集 | 精确率 | 召回率 | F1 分数 |
|---|---|---|---|---|
| gliner-community/gliner_small-v2.5 | ACE 2004 | 35.18% | 22.81% | 27.67% |
| ACE 2005 | 35.89% | 22.39% | 27.58% | |
| AnatEM | 49.12% | 31.31% | 38.24% | |
| Broad Tweet Corpus | 59.51% | 77.85% | 67.46% | |
| CoNLL 2003 | 63.16% | 70.43% | 66.60% | |
| FabNER | 23.78% | 22.55% | 23.15% | |
| FindVehicle | 37.46% | 40.06% | 38.72% | |
| GENIA_NER | 45.90% | 54.11% | 49.67% | |
| HarveyNER | 13.20% | 32.58% | 18.78% | |
| MultiNERD | 45.87% | 87.01% | 60.07% | |
| Ontonotes | 23.05% | 41.16% | 29.55% | |
| PolyglotNER | 31.88% | 67.22% | 43.25% | |
| TweetNER7 | 40.98% | 39.91% | 40.44% | |
| WikiANN en | 55.35% | 60.06% | 57.61% | |
| WikiNeural | 64.52% | 86.24% | 73.81% | |
| bc2gm | 51.70% | 49.99% | 50.83% | |
| bc4chemd | 30.78% | 57.56% | 40.11% | |
| bc5cdr | 63.48% | 69.65% | 66.42% | |
| ncbi | 63.36% | 66.67% | 64.97% | |
| 平均 | 46.58% | |||
| ------------------------------------ | --------------------- | ----------- | -------- | ---------- |
| urchade/gliner_small-v2.1 | ACE 2004 | 38.89% | 23.53% | 29.32% |
| ACE 2005 | 42.09% | 26.82% | 32.76% | |
| AnatEM | 63.71% | 19.45% | 29.80% | |
| Broad Tweet Corpus | 57.01% | 70.49% | 63.04% | |
| CoNLL 2003 | 57.11% | 62.66% | 59.76% | |
| FabNER | 32.41% | 12.33% | 17.87% | |
| FindVehicle | 43.47% | 33.02% | 37.53% | |
| GENIA_NER | 61.03% | 37.25% | 46.26% | |
| HarveyNER | 23.12% | 15.16% | 18.32% | |
| MultiNERD | 43.63% | 83.60% | 57.34% | |
| Ontonotes | 23.25% | 35.41% | 28.07% | |
| PolyglotNER | 29.47% | 64.41% | 40.44% | |
| TweetNER7 | 44.78% | 30.83% | 36.52% | |
| WikiANN en | 52.58% | 58.31% | 55.30% | |
| WikiNeural | 53.38% | 82.19% | 64.72% | |
| bc2gm | 66.64% | 30.56% | 41.90% | |
| bc4chemd | 42.01% | 56.03% | 48.02% | |
| bc5cdr | 72.03% | 58.58% | 64.61% | |
| ncbi | 68.88% | 46.71% | 55.67% | |
| 平均 | 43.54% | |||
| ------------------------------------ | --------------------- | ----------- | -------- | ---------- |
| EmergentMethods/gliner_small-v2.1 | ACE 2004 | 39.92% | 17.50% | 24.34% |
| ACE 2005 | 38.53% | 16.58% | 23.18% | |
| AnatEM | 55.95% | 25.69% | 35.22% | |
| Broad Tweet Corpus | 66.63% | 72.00% | 69.21% | |
| CoNLL 2003 | 62.89% | 58.96% | 60.86% | |
| FabNER | 32.76% | 13.33% | 18.95% | |
| FindVehicle | 42.93% | 43.20% | 43.06% | |
| GENIA_NER | 51.28% | 43.75% | 47.22% | |
| HarveyNER | 24.82% | 21.52% | 23.05% | |
| MultiNERD | 59.27% | 80.69% | 68.34% | |
| Ontonotes | 32.97% | 37.59% | 35.13% | |
| PolyglotNER | 33.60% | 63.30% | 43.90% | |
| TweetNER7 | 46.90% | 28.66% | 35.58% | |
| WikiANN en | 51.91% | 55.43% | 53.61% | |
| WikiNeural | 70.65% | 82.21% | 75.99% | |
| bc2gm | 49.95% | 43.13% | 46.29% | |
| bc4chemd | 35.88% | 71.64% | 47.81% | |
| bc5cdr | 68.41% | 68.90% | 68.65% | |
| ncbi | 55.31% | 59.87% | 57.50% | |
| 平均 | 46.20% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| gliner-community/gliner_medium-v2.5 | ACE 2004 | 33.06% | 20.96% | 25.66% |
| ACE 2005 | 33.65% | 19.65% | 24.81% | |
| AnatEM | 52.03% | 35.28% | 42.05% | |
| Broad Tweet Corpus | 60.57% | 79.09% | 68.60% | |
| CoNLL 2003 | 63.80% | 68.31% | 65.98% | |
| FabNER | 26.20% | 22.26% | 24.07% | |
| FindVehicle | 41.95% | 40.68% | 41.30% | |
| GENIA_NER | 51.83% | 62.34% | 56.60% | |
| HarveyNER | 14.04% | 32.17% | 19.55% | |
| MultiNERD | 47.63% | 88.78% | 62.00% | |
| Ontonotes | 21.68% | 38.41% | 27.71% | |
| PolyglotNER | 32.73% | 68.27% | 44.24% | |
| TweetNER7 | 40.39% | 37.64% | 38.97% | |
| WikiANN en | 56.41% | 59.90% | 58.10% | |
| WikiNeural | 65.61% | 86.28% | 74.54% | |
| bc2gm | 55.20% | 56.71% | 55.95% | |
| bc4chemd | 35.94% | 63.67% | 45.94% | |
| bc5cdr | 63.50% | 70.09% | 66.63% | |
| ncbi | 62.96% | 68.55% | 65.63% | |
| 平均 | 47.81% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| urchade/gliner_medium-v2.1 | ACE 2004 | 36.33% | 22.74% | 27.97% |
| ACE 2005 | 40.49% | 25.46% | 31.27% | |
| AnatEM | 59.75% | 16.87% | 26.31% | |
| Broad Tweet Corpus | 60.89% | 67.25% | 63.91% | |
| CoNLL 2003 | 60.62% | 62.39% | 61.50% | |
| FabNER | 27.72% | 12.24% | 16.98% | |
| FindVehicle | 41.55% | 31.31% | 35.71% | |
| GENIA_NER | 60.86% | 43.93% | 51.03% | |
| HarveyNER | 23.20% | 23.16% | 23.18% | |
| MultiNERD | 41.25% | 83.74% | 55.27% | |
| Ontonotes | 20.58% | 34.11% | 25.67% | |
| PolyglotNER | 31.32% | 64.22% | 42.11% | |
| TweetNER7 | 44.52% | 33.42% | 38.18% | |
| WikiANN en | 54.57% | 56.47% | 55.51% | |
| WikiNeural | 57.60% | 81.57% | 67.52% | |
| bc2gm | 67.98% | 33.45% | 44.84% | |
| bc4chemd | 45.66% | 52.00% | 48.62% | |
| bc5cdr | 72.20% | 58.12% | 64.40% | |
| ncbi | 73.12% | 49.74% | 59.20% | |
| 平均 | 44.17% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| EmergentMethods/gliner_news_medium-v2.1 | ACE 2004 | 39.21% | 17.24% | 23.95% |
| ACE 2005 | 39.82% | 16.48% | 23.31% | |
| AnatEM | 57.67% | 23.57% | 33.46% | |
| Broad Tweet Corpus | 69.52% | 65.94% | 67.69% | |
| CoNLL 2003 | 68.26% | 58.45% | 62.97% | |
| FabNER | 30.74% | 15.51% | 20.62% | |
| FindVehicle | 40.33% | 37.37% | 38.79% | |
| GENIA_NER | 53.70% | 47.73% | 50.54% | |
| HarveyNER | 26.29% | 27.05% | 26.67% | |
| MultiNERD | 56.78% | 81.96% | 67.08% | |
| Ontonotes | 30.90% | 35.86% | 33.19% | |
| PolyglotNER | 35.98% | 60.96% | 45.25% | |
| TweetNER7 | 52.37% | 30.50% | 38.55% | |
| WikiANN en | 53.81% | 52.29% | 53.04% | |
| WikiNeural | 76.84% | 78.92% | 77.86% | |
| bc2gm | 62.97% | 44.24% | 51.96% | |
| bc4chemd | 44.90% | 65.56% | 53.30% | |
| bc5cdr | 73.93% | 67.03% | 70.31% | |
| ncbi | 69.53% | 60.82% | 64.88% | |
| 平均 | 47.55% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| gliner-community/gliner_large-v2.5 | ACE 2004 | 31.64% | 22.81% | 26.51% |
| ACE 2005 | 32.10% | 22.56% | 26.49% | |
| AnatEM | 53.64% | 27.82% | 36.64% | |
| Broad Tweet Corpus | 61.93% | 76.85% | 68.59% | |
| CoNLL 2003 | 62.83% | 67.71% | 65.18% | |
| FabNER | 24.54% | 27.03% | 25.73% | |
| FindVehicle | 40.71% | 56.24% | 47.23% | |
| GENIA_NER | 43.56% | 52.56% | 47.64% | |
| HarveyNER | 14.85% | 27.05% | 19.17% | |
| MultiNERD | 38.04% | 89.17% | 53.33% | |
| Ontonotes | 17.28% | 40.16% | 24.16% | |
| PolyglotNER | 32.88% | 63.31% | 43.28% | |
| TweetNER7 | 38.03% | 41.43% | 39.66% | |
| WikiANN en | 57.80% | 60.54% | 59.14% | |
| WikiNeural | 67.72% | 83.94% | 74.96% | |
| bc2gm | 54.74% | 48.54% | 51.45% | |
| bc4chemd | 40.20% | 58.66% | 47.71% | |
| bc5cdr | 66.27% | 71.95% | 69.00% | |
| ncbi | 68.09% | 61.55% | 64.65% | |
| 平均 | 46.87% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| urchade/gliner_large-v2.1 | ACE 2004 | 37.52% | 25.38% | 30.28% |
| ACE 2005 | 39.02% | 29.00% | 33.27% | |
| AnatEM | 52.86% | 13.64% | 21.68% | |
| Broad Tweet Corpus | 51.44% | 71.73% | 59.91% | |
| CoNLL 2003 | 54.86% | 64.98% | 59.49% | |
| FabNER | 23.98% | 16.00% | 19.19% | |
| FindVehicle | 47.04% | 57.53% | 51.76% | |
| GENIA_NER | 58.10% | 49.98% | 53.74% | |
| HarveyNER | 16.29% | 21.93% | 18.69% | |
| MultiNERD | 34.09% | 85.43% | 48.74% | |
| Ontonotes | 14.02% | 32.01% | 19.50% | |
| PolyglotNER | 28.53% | 64.92% | 39.64% | |
| TweetNER7 | 38.00% | 34.34% | 36.08% | |
| WikiANN en | 51.69% | 59.92% | 55.50% | |
| WikiNeural | 50.94% | 82.08% | 62.87% | |
| bc2gm | 64.48% | 32.47% | 43.19% | |
| bc4chemd | 48.66% | 57.52% | 52.72% | |
| bc5cdr | 72.19% | 64.27% | 68.00% | |
| ncbi | 69.54% | 52.25% | 59.67% | |
| 平均 | 43.89% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
| EmergenMethods/fliner_news_large-v2.1 | ACE 2004 | 43.19% | 18.39% | 25.80% |
| ACE 2005 | 45.24% | 21.20% | 28.87% | |
| AnatEM | 61.51% | 21.66% | 32.04% | |
| Broad Tweet Corpus | 69.38% | 68.99% | 69.18% | |
| CoNLL 2003 | 61.47% | 52.18% | 56.45% | |
| FabNER | 27.42% | 19.11% | 22.52% | |
| FindVehicle | 46.30% | 62.48% | 53.19% | |
| GENIA_NER | 54.13% | 54.02% | 54.07% | |
| HarveyNER | 15.91% | 15.78% | 15.84% | |
| MultiNERD | 53.73% | 79.07% | 63.98% | |
| Ontonotes | 26.78% | 39.77% | 32.01% | |
| PolyglotNER | 34.28% | 55.87% | 42.49% | |
| TweetNER7 | 48.06% | 28.18% | 35.53% | |
| WikiANN en | 53.66% | 51.34% | 52.47% | |
| WikiNeural | 69.81% | 70.75% | 70.28% | |
| bc2gm | 59.83% | 37.62% | 46.20% | |
| bc4chemd | 46.24% | 69.15% | 55.42% | |
| bc5cdr | 71.94% | 70.37% | 71.15% | |
| ncbi | 70.17% | 61.44% | 65.52% | |
| 平均 | 47.00% | |||
| ----------------------------------------- | --------------------- | ----------- | -------- | ---------- |
其他可用模型
| 版本 | 模型名称 | 参数量 | 语言 | 许可证 |
|---|---|---|---|---|
| v0 | urchade/gliner_base urchade/gliner_multi |
209M 209M |
英语 多语言 |
cc-by-nc-4.0 |
| v1 | urchade/gliner_small-v1 urchade/gliner_medium-v1 urchade/gliner_large-v1 |
166M 209M 459M |
英语 英语 英语 |
cc-by-nc-4.0 |
| v2 | urchade/gliner_small-v2 urchade/gliner_medium-v2 urchade/gliner_large-v2 |
166M 209M 459M |
英语 英语 英语 |
apache-2.0 |
| v2.1 | urchade/gliner_small-v2.1 urchade/gliner_medium-v2.1 urchade/gliner_large-v2.1 urchade/gliner_multi-v2.1 |
166M 209M 459M 209M |
英语 英语 英语 多语言 |
apache-2.0 |
模型作者
模型作者包括：
- Urchade Zaratiana
- Ihor Stepanov
- Nadi Tomeh
- Pierre Holat
- Thierry Charnois
引用
@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}
}
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