metadata

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

Dataset Card for "no_robots_enfr"

This is a filtered version of 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