Bert Portuguese Ner

🚀 bert-portuguese-ner

本模型是基於 neuralmind/bert-base-portuguese-cased 的微調版本，可用於解決葡萄牙語檔案文檔中的命名實體識別（NER）問題，在評估集上有出色的表現。

🚀 快速開始

本模型在評估集上取得了以下結果：

• 損失值：0.1140
• 精確率：0.9147
• 召回率：0.9483
• F1值：0.9312
• 準確率：0.9700

✨ 主要特性

• 基於預訓練的葡萄牙語 BERT 模型進行微調，適用於葡萄牙語檔案文檔的命名實體識別任務。
• 標註的標籤包括：日期、職業、人物、地點、組織，能滿足多種信息抽取需求。

📚 詳細文檔

模型描述

該模型在葡萄牙語檔案文檔的標記分類任務（NER）上進行了微調。標註的標籤有：日期、職業、人物、地點、組織。

數據集

所有訓練和評估數據可在以下鏈接獲取：http://ner.epl.di.uminho.pt/

訓練超參數

訓練過程中使用了以下超參數：

• 學習率：2e-05
• 訓練批次大小：16
• 評估批次大小：16
• 隨機種子：42
• 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
• 學習率調度器類型：線性
• 訓練輪數：4

訓練結果

框架版本

• Transformers 4.10.0.dev0
• Pytorch 1.9.0+cu111
• Datasets 1.10.2
• Tokenizers 0.10.3

引用信息

@Article{make4010003,
AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos},
TITLE = {NER in Archival Finding Aids: Extended},
JOURNAL = {Machine Learning and Knowledge Extraction},
VOLUME = {4},
YEAR = {2022},
NUMBER = {1},
PAGES = {42--65},
URL = {https://www.mdpi.com/2504-4990/4/1/3},
ISSN = {2504-4990},
ABSTRACT = {The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.},
DOI = {10.3390/make4010003}
}

📄 許可證

本項目採用 MIT 許可證。