metadata
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
Dataset Card for "10-dim"
Map labels to strings
# 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]