标注示例

数据集获取（Figshare）

• 源站标题：Examples of annotation.
• DOI：10.1371/journal.pone.0305496.t003
• 发布日期：2024-09-06
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Examples_of_annotation_/26960533

命令行下载

curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/49059610"

文件清单

• Table 3.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Narratives posted on the internet by patients contain a vast amount of information about various concerns. This study aimed to extract multiple concerns from interviews with breast cancer patients using the natural language processing (NLP) model bidirectional encoder representations from transformers (BERT). A total of 508 interview transcriptions of breast cancer patients written in Japanese were labeled with five types of concern labels: “treatment,” “physical,” “psychological,” “work/financial,” and “family/friends.” The labeled texts were used to create a multi-label classifier by fine-tuning a pre-trained BERT model. Prior to fine-tuning, we also created several classifiers with domain adaptation using (1) breast cancer patients’ blog articles and (2) breast cancer patients’ interview transcriptions. The performance of the classifiers was evaluated in terms of precision through 5-fold cross-validation. The multi-label classifiers with only fine-tuning had precision values of over 0.80 for “physical” and “work/financial” out of the five concerns. On the other hand, precision for “treatment” was low at approximately 0.25. However, for the classifiers using domain adaptation, the precision of this label took a range of 0.40–0.51, with some cases improving by more than 0.2. This study showed combining domain adaptation with a multi-label classifier on target data made it possible to efficiently extract multiple concerns from interviews.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0305496.t003（source: https://plos.figshare.com/articles/dataset/Examples_of_annotation_/26960533）。