|
--- |
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language: |
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- fr |
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- en |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- text-generation |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: eval |
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path: data/eval-* |
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- split: test |
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path: data/test-* |
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dataset_info: |
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features: |
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- name: category |
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dtype: string |
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- name: output |
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dtype: string |
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- name: query |
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dtype: string |
|
- name: qid |
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dtype: int64 |
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- name: fr_query |
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dtype: string |
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- name: fr_output |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 22352415.03147541 |
|
num_examples: 7866 |
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- name: eval |
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num_bytes: 1810130.7367213115 |
|
num_examples: 637 |
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- name: test |
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num_bytes: 1838547.2318032787 |
|
num_examples: 647 |
|
download_size: 16266132 |
|
dataset_size: 26001093.0 |
|
--- |
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# Dataset Card for "no_robots_enfr" |
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|
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This is a filtered version of [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots), |
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then traduced to french with Deepl pro API, the best translation solution available on the market. |
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|
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Our goal is to gather french data for one turn chatbot, on general subjects. |
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We filtered few data from the original dataset: |
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- We kept only the one turn questions |
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- 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. |
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- We kept the category information from the original dataset |
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|
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| Category | Number of Data | Mean Words (Query) | Mean Words (Output) | |
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|----------------|----------------|--------------------|---------------------| |
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| Brainstorm | 1120 | 35 | 217 | |
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| Generation | 4560 | 35 | 177 | |
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| Rewrite | 660 | 258 | 206 | |
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| Open QA | 1240 | 12 | 73 | |
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| Classify | 350 | 121 | 29 | |
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| Summarize | 420 | 238 | 64 | |
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| Coding | 350 | 55 | 124 | |
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| Extract | 190 | 270 | 36 | |
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| Closed QA | 260 | 217 | 22 | |
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|----------------|----------------|--------------------|---------------------| |
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| General Dataset| 9150 | 71 | 150 | |
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|
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Depending on our need we will filter those data by category to not inject hallicination in our fine-tuning. |
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|
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The splits are made as each split have the same proportion of each categories: |
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Train dataset size: 7866 |
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Eval dataset size: 637 |
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Test dataset size: 647 |
