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@@ -17,31 +17,13 @@ task_categories:
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  - text-generation
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  - table-question-answering
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  - summarization
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- - conversational
 
 
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  size_categories:
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  - 1K<n<10K
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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: input
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- dtype: string
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- - name: output
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- dtype: string
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- - name: start
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- dtype: string
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- - name: expiration
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- dtype: string
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- - name: num
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 7581160
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- num_examples: 8655
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- download_size: 2685851
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- dataset_size: 7581160
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  ---
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- # Code des transports, non-instruct (11-12-2023)
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  This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
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@@ -64,6 +46,9 @@ This JSON file is a list of dictionaries, each dictionary contains the following
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  - `instruction`: `string`, presenting the instruction linked to the element.
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  - `input`: `string`, signifying the input details for the element.
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  - `output`: `string`, indicating the output information for the element.
 
 
 
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  We used the following list of instructions for generating the dataset:
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  ```python
@@ -109,19 +94,6 @@ instructions = [
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  ]
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  ```
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- ## Citing this project
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-
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- If you use this code in your research, please use the following BibTeX entry.
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-
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- ```BibTeX
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- @misc{louisbrulenaudet2023,
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- author = {Louis Brulé Naudet},
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- title = {Code des transports, non-instruct (11-12-2023)},
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- howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/code-transports}},
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- year = {2023}
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- }
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- ```
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-
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  ## Feedback
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  If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).
 
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  - text-generation
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  - table-question-answering
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  - summarization
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+ - text-retrieval
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+ - question-answering
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+ - text-classification
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  size_categories:
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  - 1K<n<10K
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ # Code des transports, non-instruct (2024-03-26)
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  This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
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  - `instruction`: `string`, presenting the instruction linked to the element.
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  - `input`: `string`, signifying the input details for the element.
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  - `output`: `string`, indicating the output information for the element.
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+ - `start`: `string`, the date of entry into force of the article.
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+ - `expiration`: `string`, the date of expiration of the article.
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+ - `num`: `string`, the id of the article.
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  We used the following list of instructions for generating the dataset:
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  ```python
 
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  ]
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  ```
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  ## Feedback
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  If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com).