README.md

---
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: category
    dtype: string
  - name: prompt_id
    dtype: string
  splits:
  - name: train
    num_bytes: 222353
    num_examples: 400
  download_size: 139530
  dataset_size: 222353
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Dataset Card for "no_robots_test400"

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

This is a subset of "no_robots", selecting 400 questions from the test set.

| category   |   messages |
|:-----------|-----------:|
| Brainstorm |         36 |
| Chat       |        101 |
| Classify   |         16 |
| Closed QA  |         15 |
| Coding     |         16 |
| Extract    |          7 |
| Generation |        129 |
| Open QA    |         34 |
| Rewrite    |         21 |
| Summarize  |         25 |


Code:

```python
import pandas as pd
import numpy as np
import numpy.random
from datasets import load_dataset, Dataset
from copy import deepcopy

def get_norobot_dataset():
    ds = load_dataset('HuggingFaceH4/no_robots')
    all_test_data = []
    for sample in ds['test_sft']:
        sample: dict
        for i, message in enumerate(sample['messages']):
            if message['role'] == 'user':
                item = dict(
                    messages=deepcopy(sample['messages'][:i + 1]),
                    category=sample['category'],
                    prompt_id=sample['prompt_id'],
                )
                all_test_data.append(item)
    return Dataset.from_list(all_test_data)


dataset = get_norobot_dataset().to_pandas()
dataset.groupby('category').count()
dataset['_sort_key'] = dataset['messages'].map(str)
dataset = dataset.sort_values(['_sort_key'])

subset = []
for category, group_df in sorted(dataset.groupby('category')):
    n = int(len(group_df) * 0.603)
    if n <= 20:
        n = len(group_df)
    indices = np.random.default_rng(seed=42).choice(len(group_df), size=n, replace=False)
    subset.append(group_df.iloc[indices])

df = pd.concat(subset)
df = df.drop(columns=['_sort_key'])
df = df.reset_index(drop=True)
print(len(df))
print(df.groupby('category').count().to_string())
Dataset.from_pandas(df).push_to_hub('yujiepan/no_robots_test400')
```