---
language:
- fr
- en
size_categories:
- 1K<n<10K
task_categories:
- text-generation
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: eval
    path: data/eval-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: category
    dtype: string
  - name: output
    dtype: string
  - name: query
    dtype: string
  - name: qid
    dtype: int64
  - name: fr_query
    dtype: string
  - name: fr_output
    dtype: string
  splits:
  - name: train
    num_bytes: 22352415.03147541
    num_examples: 7866
  - name: eval
    num_bytes: 1810130.7367213115
    num_examples: 637
  - name: test
    num_bytes: 1838547.2318032787
    num_examples: 647
  download_size: 16266132
  dataset_size: 26001093.0
---
# Dataset Card for "no_robots_enfr"

This is a filtered version of [HuggingFaceH4/no_robots](https://huggingface.co./datasets/HuggingFaceH4/no_robots),
then traduced to french with Deepl pro API, the best translation solution available on the market.

Our goal is to gather french data for one turn chatbot, on general subjects.
We filtered few data from the original dataset:
- We kept only the one turn questions
- We took out any data where a system role is settle at the beginning, as our LLM will have a unique role that we don't have to define before a query.
- We kept the category information from the original dataset

| Category       | Number of Data | Mean Words (Query) | Mean Words (Output) |
|----------------|----------------|--------------------|---------------------|
| Brainstorm     | 1120           | 35                 | 217                 |
| Generation     | 4560           | 35                 | 177                 |
| Rewrite        | 660            | 258                | 206                 |
| Open QA        | 1240           | 12                 | 73                  |
| Classify       | 350            | 121                | 29                  |
| Summarize      | 420            | 238                | 64                  |
| Coding         | 350            | 55                 | 124                 |
| Extract        | 190            | 270                | 36                  |
| Closed QA      | 260            | 217                | 22                  |
|----------------|----------------|--------------------|---------------------|
| General Dataset| 9150           | 71                 | 150                 |

Depending on our need we will filter those data by category to not inject hallicination in our fine-tuning.

The splits are made as each split have the same proportion of each categories:
Train dataset size: 7866
Eval dataset size: 637
Test dataset size: 647