README.md

---
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](https://huggingface.co/datasets/embedding-data/WikiAnswers)). This dataset is for the owner's personal use and claims no rights whatsoever. 
As opposed to the original dataset with `3,386,256` rows, this dataset contains only 4% of the total rows (1,095,326).

## Languages
English.

## Dataset Structure
Each example in the dataset contains 25 equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
```
{"set": [sentence_1, sentence_2, ..., sentence_25]}
{"set": [sentence_1, sentence_2, ..., sentence_25]}
...
{"set": [sentence_1, sentence_2, ..., sentence_25]}
```
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/WikiAnswers")
```
The dataset is loaded as a `DatasetDict` and has the format for `N` examples:
```python
DatasetDict({
    train: Dataset({
        features: ['set'],
        num_rows: N
    })
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Source Data
* `embedding-data/WikiAnswers` on HuggingFace ([Link](https://huggingface.co/datasets/embedding-data/WikiAnswers))