metadata

language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - feature-extraction
  - text-classification
  - sentence-similarity
dataset_info:
  features:
    - name: sentence
      dtype: string
    - name: cluster
      dtype: int64
  splits:
    - name: train
      num_bytes: 1820698240
      num_examples: 30472041
  download_size: 695740262
  dataset_size: 1820698240
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for "WikiAnswers Small"

Dataset Summary

nikhilchigali/wikianswers_small is a subset of the embedding-data/WikiAnswers dataset (Link).
As opposed to the original dataset with 3,386,256 rows, this dataset contains only 4% of the total rows(sets). The sets of sentences have been unraveled into individual items with corresponding cluster IDs to identify sentences from the same set.

Languages

English.

Dataset Structure

Each example in the dataset contains a sentence and its cluster id of other equivalent sentences. The sentences in the same cluster are paraphrases of each other.

{"sentence": [sentence], "cluster": [cluster_id]}
{"sentence": [sentence], "cluster": [cluster_id]}
{"sentence": [sentence], "cluster": [cluster_id]}
...
{"sentence": [sentence], "cluster": [cluster_id]}

Usage Example

Install the 🤗 Datasets library with pip install datasets and load the dataset from the Hub with:

from datasets import load_dataset
dataset = load_dataset("nikhilchigali/wikianswers_small")

The dataset is loaded as a DatasetDict and has the format for N examples:

DatasetDict({
    train: Dataset({
        features: ['sentence', "cluster"],
        num_rows: N
    })
})

Review an example i with:

dataset["train"][i]

Source Data

embedding-data/WikiAnswers on HuggingFace (Link)

Datasets:

nikhilchigali
/

wikianswers_small