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  ---
 
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  language:
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  - fr
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- license: apache-2.0
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  multilinguality:
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  - monolingual
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- size_categories:
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- - 1K<n<10K
 
 
 
 
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  source_datasets:
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  - original
 
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  task_categories:
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  - text-generation
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  - table-question-answering
@@ -15,40 +20,10 @@ task_categories:
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  - text-retrieval
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  - question-answering
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  - text-classification
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- pretty_name: Code des transports
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- tags:
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- - finetuning
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- - legal
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- - french law
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- - droit français
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- - Code des transports
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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: 7580830
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- num_examples: 8655
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- download_size: 2685625
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- dataset_size: 7580830
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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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  ---
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- # Code des transports, non-instruct (2024-03-27)
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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 +39,120 @@ Instruction-based fine-tuning significantly enhances the performance of LLMs in
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  - Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
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  - Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
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  ## Dataset generation
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  This JSON file is a list of dictionaries, each dictionary contains the following fields:
 
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  ---
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+ license: apache-2.0
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  language:
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  - fr
 
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  multilinguality:
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  - monolingual
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+ tags:
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+ - finetuning
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+ - legal
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+ - french law
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+ - droit français
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+ - Code des transports
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  source_datasets:
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  - original
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+ pretty_name: Code des transports
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  task_categories:
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  - text-generation
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  - table-question-answering
 
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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 (2024-04-01)
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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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  - Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
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  - Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
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+ ## Concurrent reading of the LegalKit
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+
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+ To use all the legal data published on LegalKit, you can use this code snippet:
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+ ```python
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+ # -*- coding: utf-8 -*-
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+ import concurrent.futures
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+ import os
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+
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+ import datasets
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+ from tqdm.notebook import tqdm
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+
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+ def dataset_loader(
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+ name:str,
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+ streaming:bool=True
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+ ) -> datasets.Dataset:
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+ """
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+ Helper function to load a single dataset in parallel.
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+
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+ Parameters
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+ ----------
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+ name : str
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+ Name of the dataset to be loaded.
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+
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+ streaming : bool, optional
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+ Determines if datasets are streamed. Default is True.
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+
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+ Returns
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+ -------
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+ dataset : datasets.Dataset
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+ Loaded dataset object.
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+
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+ Raises
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+ ------
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+ Exception
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+ If an error occurs during dataset loading.
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+ """
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+ try:
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+ return datasets.load_dataset(
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+ name,
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+ split="train",
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+ streaming=streaming
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+ )
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+
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+ except Exception as exc:
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+ logging.error(f"Error loading dataset {name}: {exc}")
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+
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+ return None
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+
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+
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+ def load_datasets(
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+ req:list,
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+ streaming:bool=True
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+ ) -> list:
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+ """
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+ Downloads datasets specified in a list and creates a list of loaded datasets.
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+
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+ Parameters
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+ ----------
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+ req : list
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+ A list containing the names of datasets to be downloaded.
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+
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+ streaming : bool, optional
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+ Determines if datasets are streamed. Default is True.
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+
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+ Returns
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+ -------
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+ datasets_list : list
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+ A list containing loaded datasets as per the requested names provided in 'req'.
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+
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+ Raises
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+ ------
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+ Exception
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+ If an error occurs during dataset loading or processing.
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+
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+ Examples
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+ --------
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+ >>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
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+ """
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+ datasets_list = []
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+
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+ with concurrent.futures.ThreadPoolExecutor() as executor:
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+ future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
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+
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+ for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
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+ name = future_to_dataset[future]
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+
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+ try:
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+ dataset = future.result()
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+
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+ if dataset:
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+ datasets_list.append(dataset)
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+
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+ except Exception as exc:
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+ logging.error(f"Error processing dataset {name}: {exc}")
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+
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+ return datasets_list
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+
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+
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+ req = [
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+ "louisbrulenaudet/code-artisanat",
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+ "louisbrulenaudet/code-action-sociale-familles",
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+ # ...
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+ ]
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+
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+ datasets_list = load_datasets(
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+ req=req,
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+ streaming=True
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+ )
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+
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+ dataset = datasets.concatenate_datasets(
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+ datasets_list
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+ )
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+ ```
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+
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  ## Dataset generation
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  This JSON file is a list of dictionaries, each dictionary contains the following fields: