AndersGiovanni/10-dim

---
language:
- en
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-classification
pretty_name: 10 Social Dimensions
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2237355.6300445576
num_examples: 5498
- name: validation
num_bytes: 479375.2150222788
num_examples: 1178
- name: test
num_bytes: 479782.1549331636
num_examples: 1179
download_size: 1723668
dataset_size: 3196513.0
---
# Dataset Card for "10-dim"

### Map labels to strings

```python
# Here's the list of labels and mappings between id and label.

labels = [
"social_support",
"conflict",
"trust",
"fun",
"similarity",
"identity",
"respect",
"romance",
"knowledge",
"power",
]
id2label = {i: label for i, label in enumerate(labels)}
label2id = {label: i for i, label in enumerate(labels)}

# Given an examples, this is how you map
sample = {
"text": "This is just a made up text"
"labels": [0, 0, 0, 1, 0, 0, 0, 0, 0, 1]
}

labels_str = [id2label[i] for i, label in enumerate(sample['labels']) if label == 1]
```

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

原始信息汇总

数据集概述

基本信息

• 语言: 英语
• 许可证: MIT
• 数据集大小: 1K<n<10K
• 任务类别: 文本分类
• 数据集名称: 10 Social Dimensions

配置

• 配置名称: default
  • 数据文件:
    • 训练集: data/train-*
    • 验证集: data/validation-*
    • 测试集: data/test-*

数据集信息

• 特征:
  • 文本: 字符串类型
  • 标签: 整数序列类型
• 分割:
  • 训练集:
    • 字节数: 2237355.6300445576
    • 样本数: 5498
  • 验证集:
    • 字节数: 479375.2150222788
    • 样本数: 1178
  • 测试集:
    • 字节数: 479782.1549331636
    • 样本数: 1179
• 下载大小: 1723668
• 数据集大小: 3196513.0

标签映射

• 标签列表:

  • "social_support"
  • "conflict"
  • "trust"
  • "fun"
  • "similarity"
  • "identity"
  • "respect"
  • "romance"
  • "knowledge"
  • "power"

• id到标签的映射: python id2label = {i: label for i, label in enumerate(labels)}

• 标签到id的映射: python label2id = {label: i for i, label in enumerate(labels)}

• 示例映射: python sample = { "text": "This is just a made up text", "labels": [0, 0, 0, 1, 0, 0, 0, 0, 0, 1] } labels_str = [id2label[i] for i, label in enumerate(sample[labels]) if label == 1]