File size: 2,544 Bytes
763e3eb 241f322 763e3eb 241f322 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 116696879
num_examples: 130316
- name: validation
num_bytes: 11660319
num_examples: 11873
download_size: 17698683
dataset_size: 128357198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
task_categories:
- question-answering
language:
- en
size_categories:
- 100K<n<1M
---
## Clean SQuAD Classic v2
This is a refined version of the [SQuAD v2](https://huggingface.co/datasets/rajpurkar/squad_v2) dataset. It has been preprocessed to ensure higher data quality and usability for NLP tasks such as Question Answering.
## Description
The **Clean SQuAD Classic v2** dataset was created by applying preprocessing steps to the original SQuAD v2 dataset, including:
- **Trimming whitespace**: All leading and trailing spaces have been removed from the `question` field.
- **Minimum question length**: Questions with fewer than 12 characters were filtered out to remove overly short or uninformative entries.
Unlike the [Clean SQuAD v2](https://huggingface.co/datasets/decodingchris/clean_squad_v2) dataset, this dataset does not contain a separate test split. It retains the classic two-way split of **train** and **validation**, following the traditional structure of the original SQuAD v2 dataset.
## Dataset Structure
The dataset is divided into two subsets:
1. **Train**: The primary dataset for model training.
2. **Validation**: A dataset for hyperparameter tuning and model validation.
## Data Fields
Each subset contains the following fields:
- `id`: Unique identifier for each question-context pair.
- `title`: Title of the article the context is derived from.
- `context`: Paragraph from which the answer is extracted.
- `question`: Preprocessed question string.
- `answers`: Dictionary containing:
- `text`: The text of the correct answer(s), if available. Empty for unanswerable questions.
- `answer_start`: Character-level start position of the answer in the context, if available.
## Usage
The dataset is hosted on the Hugging Face Hub and can be loaded with the following code:
```python
from datasets import load_dataset
dataset = load_dataset("decodingchris/clean_squad_classic_v2")
``` |