| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-retrieval |
| - text-generation |
| language: |
| - en |
| tags: |
| - finance |
| size_categories: |
| - 10K<n<100K |
| --- |
| # The ArGiMI Ardian datasets : text and images |
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|  |
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|
| The ArGiMi project is committed to open-source principles and data sharing. |
| Thanks to our generous partners, we are releasing several valuable datasets to the public. |
|
|
| ## Dataset description |
|
|
| This dataset comprises 34,000 financial annual reports, written in English, meticulously |
| extracted from their original PDF format to provide a valuable resource for researchers and developers in financial |
| analysis and natural language processing (NLP). These reports were published from the late 90s to 2023. |
|
|
| This dataset provides images of each document pages. A lighter, **text-only version**, is also available at |
| [`artefactory/Argimi-Ardian-Finance-10k-text`](https://huggingface.co/datasets/artefactory/Argimi-Ardian-Finance-10k-text). |
|
|
| You can load the dataset with: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("artefactory/Argimi-Ardian-Finance-10k-text-image", split="train") |
| |
| # Or you can stream the dataset to save memory space : |
| |
| ds = load_dataset("artefactory/Argimi-Ardian-Finance-10k-text-image", split="train", streaming=True) |
| ``` |
|
|
| ## Dataset composition: |
|
|
| * Each PDF was divided into **individual pages** to facilitate granular analysis. |
| * For each page, the following data points were extracted: |
| * **Raw Text:** The complete textual content of the page, capturing all textual information present. |
| * **Screenshot:** A high-resolution image of the page, preserving the visual layout and formatting. |
| * **Cells:** Each cell within tables was identified and represented as a `Cell` object within the `docling` framework. Each `Cell` object encapsulates: |
| * `id`: A unique identifier assigned to each cell, ensuring unambiguous referencing. |
| * `text`: The textual content contained within the cell. |
| * `bbox`: The precise bounding box coordinates of the cell, defining its location and dimensions on the page. |
| * When OCR is employed, cells are further represented as `OcrCell` objects, which include an additional `confidence` attribute. This attribute quantifies the confidence level of the OCR process in accurately recognizing the cell's textual content. |
| * **Segments:** Beyond individual cells, `docling` segments each page into distinct content units, each represented as a `Segment` object. These segments provide a structured representation of the document's layout and content, encompassing elements such as tables, headers, paragraphs, and other structural components. Each `Segment` object contains: |
| * `text`: The textual content of the segment. |
| * `bbox`: The bounding box coordinates, specifying the segment's position and size on the page. |
| * `label`: A categorical label indicating the type of content the segment represents (e.g., "table," "header," "paragraph"). |
|
|
| * To guarantee unique identification, each document is assigned a distinct identifier derived from the hash of its content. |
|
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|
|
| ## Parsing description: |
|
|
| The dataset creation involved a systematic process using the `docling` library ([Documentation](https://ds4sd.github.io/docling/)). |
|
|
| * PDFs were processed using the `DocumentConverter` class, employing the `PyPdfiumDocumentBackend` for handling of the PDF format. |
| * To ensure high-quality extraction, the following `PdfPipelineOptions` were configured: |
| ```python |
| pipeline_options = PdfPipelineOptions(ocr_options=EasyOcrOptions(use_gpu=True)) |
| pipeline_options.images_scale = 2.0 # Scale image resolution by a factor of 2 |
| pipeline_options.generate_page_images = True # Generate page images |
| pipeline_options.do_ocr = True # Perform OCR |
| pipeline_options.do_table_structure = True # Extract table structure |
| pipeline_options.table_structure_options.do_cell_matching = True # Perform cell matching in tables |
| pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE # Use accurate mode for table structure extraction |
| ``` |
| * These options collectively enable: |
| * GPU-accelerated Optical Character Recognition (OCR) via `EasyOcr`. |
| * Upscaling of image resolution by a factor of 2, enhancing the clarity of visual elements. |
| * Generation of page images, providing a visual representation of each page within the document. |
| * Comprehensive table structure extraction, including cell matching, to accurately capture tabular data within the reports. |
| * The "accurate" mode for table structure extraction, prioritizing precision in identifying and delineating tables. |
| |
| ## Disclaimer: |
|
|
| This dataset, made available for experimental purposes as part of the ArGiMi research project, is provided "as is" |
| for informational purposes only. The original publicly available data was provided by Ardian. |
| Artefact has processed this dataset and now publicly releases it through Ardian, with Ardian's agreement. |
| None of ArGiMi, Artefact, or Ardian make any representations or warranties of any kind (express or implied) regarding the completeness, |
| accuracy, reliability, suitability, or availability of the dataset or its contents. |
| Any reliance you place on such information is strictly at your own risk. |
| In no event shall ArGiMi, Artefact, or Ardian be liable for any loss or damage, including without limitation, |
| indirect or consequential loss or damage, or any loss or damage whatsoever arising from loss of data or profits arising out of, |
| or in connection with, the use of this dataset. This disclaimer includes, but is not limited to, |
| claims relating to intellectual property infringement, negligence, breach of contract, and defamation. |
|
|
| ## Acknowledgement: |
|
|
| The ArGiMi consortium gratefully acknowledges Ardian for their invaluable contribution in gathering the documents that |
| comprise this dataset. Their effort and collaboration were essential in enabling the creation and release of this dataset for public use. |
| The ArGiMi project is a collaborative project with Giskard, Mistral, INA and BnF, and is sponsored by the |
| France 2030 program of the French Government. |
|
|
| ## Citation: |
|
|
| If you find our datasets useful for your research, consider citing us in your works: |
|
|
| ```latex |
| @misc{argimi2024Datasets, |
| title={The ArGiMi datasets}, |
| author={Hicham Randrianarivo, Charles Moslonka, Arthur Garnier and Emmanuel Malherbe}, |
| year={2024}, |
| } |
| ``` |
