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--- |
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language: |
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- fr |
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- en |
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license: cc-by-sa-3.0 |
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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: instruction |
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dtype: string |
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- name: context |
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dtype: string |
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- name: category |
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dtype: string |
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- name: fr_context |
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dtype: string |
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- name: fr_response |
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dtype: string |
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- name: fr_instruction |
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dtype: string |
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- name: response |
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dtype: string |
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- name: qid |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 9785049.205202311 |
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num_examples: 3300 |
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- name: eval |
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num_bytes: 1340255.2244701348 |
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num_examples: 452 |
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- name: test |
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num_bytes: 1186066.570327553 |
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num_examples: 400 |
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download_size: 7746263 |
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dataset_size: 12311371.0 |
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--- |
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# Dataset Card for "dolly_context_enfr" |
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This is a filtered version of [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k), |
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then traduced to french with Deepl pro API, the best translation solution available on the market. |
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Our goal is to gather french data on question answering on context, where the model should not bring new information |
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not present in the context given. Our goal is to limit hallucination. |
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The filtering have been done in three parts: |
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- We keep only the data with a not empty context (we are not interested in random chat or not sourced information) |
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- We don't take data where the answer is more than 1,5 times longer than the context, our study of the data showed |
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that in those cases the information come from other sources than the context, and/or concist of a copy past of the context |
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- For long context data (>1000 characters), we don't take data where the answer is longer than context (character wize) |
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- We also filter around 30 data with too long context (10k character), answer (5k character) and instruction (5k character) |
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as ther were showed to have a wrong format |
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Our filtered version of dolly dataset only contain 3 of the 7 categories, |
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the annotation guidelines for each of the categories were as follows: |
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- **Closed QA**: Write a question or instruction that requires factually correct response based on a passage of text from Wikipedia. The question can be complex and can involve human-level reasoning capabilities, but should not require special knowledge. To create a question for this task include both the text of the question as well as the reference text in the form. |
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- **Summarization**: Give a summary of a paragraph from Wikipedia. Please don't ask questions that will require more than 3-5 minutes to answer. To create a question for this task include both the text of the question as well as the reference text in the form. |
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- **Information Extraction**: These questions involve reading a paragraph from Wikipedia and extracting information from the passage. Everything required to produce an answer (e.g. a list, keywords etc) should be included in the passages. To create a question for this task include both the text of the question as well as the reference text in the form. |
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| Category | Samples | |
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| - | - | |
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| closed_qa | 1711 | |
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| information_extraction | 1377 | |
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| summarization | 1064 | |
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Note that we considered 'brainstorming' and 'classification' data, |
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but there are not suited for our LLM project, and very subjective (as not based on a context), so we decided to not use them. |
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