Duplicate from vidore/vidore_v3_finance_en

Co-authored-by: Quentin Macé <QuentinJG@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- visual-document-retrieval
+language:
+- en
+tags:
+- Visual Retrieving
+- Industrial RAG
+- datadesigner
+- vidore
+- multimodal-rag
+arxiv: 2601.08620
+dataset_info:
+- config_name: corpus
+  features:
+  - name: corpus_id
+    dtype: int64
+  - name: image
+    dtype: image
+  - name: doc_id
+    dtype: string
+  - name: markdown
+    dtype: string
+  - name: page_number_in_doc
+    dtype: int64
+  splits:
+  - name: test
+    num_bytes: 1287722952
+    num_examples: 2942
+  download_size: 1265344133
+  dataset_size: 1287722952
+- config_name: documents_metadata
+  features:
+  - name: file_name
+    dtype: string
+  - name: doc_id
+    dtype: string
+  - name: url
+    dtype: string
+  - name: doc_type
+    dtype: string
+  - name: doc_language
+    dtype: string
+  - name: doc_year
+    dtype: int64
+  - name: visual_types
+    list: string
+  - name: page_number
+    dtype: int64
+  - name: license
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1820
+    num_examples: 6
+  download_size: 5710
+  dataset_size: 1820
+- config_name: qrels
+  features:
+  - name: query_id
+    dtype: int64
+  - name: corpus_id
+    dtype: int64
+  - name: score
+    dtype: int64
+  - name: content_type
+    list: string
+  - name: bounding_boxes
+    list:
+    - name: annotator
+      dtype: int64
+    - name: x1
+      dtype: int64
+    - name: x2
+      dtype: int64
+    - name: y1
+      dtype: int64
+    - name: y2
+      dtype: int64
+  splits:
+  - name: test
+    num_bytes: 1084608
+    num_examples: 8766
+  download_size: 145165
+  dataset_size: 1084608
+- config_name: queries
+  features:
+  - name: query_id
+    dtype: int64
+  - name: query
+    dtype: string
+  - name: language
+    dtype: string
+  - name: query_types
+    list: string
+  - name: query_format
+    dtype: string
+  - name: content_type
+    list: string
+  - name: raw_answers
+    list: string
+  - name: query_generator
+    dtype: string
+  - name: query_generation_pipeline
+    dtype: string
+  - name: source_type
+    dtype: string
+  - name: query_type_for_generation
+    dtype: string
+  - name: answer
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 1707613
+    num_examples: 1854
+  download_size: 505684
+  dataset_size: 1707613
+configs:
+- config_name: corpus
+  data_files:
+  - split: test
+    path: corpus/test-*
+- config_name: documents_metadata
+  data_files:
+  - split: test
+    path: documents_metadata/test-*
+- config_name: qrels
+  data_files:
+  - split: test
+    path: qrels/test-*
+- config_name: queries
+  data_files:
+  - split: test
+    path: queries/test-*
+---
+
+<center><h1>ViDoRe V3 : Finance - EN</h1></center>
+
+This dataset, `Financial_Bank_Reports`, is a corpus of `annual reports` from the `banking` sector, intended for long-document understanding tasks. It is one of the 10 corpora comprising the **ViDoRe v3 Benchmark**.
+
+## About ViDoRe v3
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.08620-b31b1b.svg?style=for-the-badge)](https://arxiv.org/abs/2601.08620)
+
+ViDoRe V3 is our latest benchmark for RAG evaluation on visually-rich documents from real-world applications. It features 10 datasets with, in total, 26,000 pages and 3099 queries, translated into 6 languages. Each query comes with human-verified relevant pages, bounding box annotations for key elements, and a comprehensive combined answer from human annotations.
+
+**Authors:** António Loison, Quentin Macé, Antoine Edy, Victor Xing, Tom Balough, Gabriel Moreira, Bo Liu, Manuel Faysse, Céline Hudelot, and Gautier Viaud.
+
+## Links
+
+* **Homepage:** [https://huggingface.co/vidore](https://huggingface.co/vidore)
+* **Collection:** [https://hf.co/collections/vidore/vidore-benchmark-v3](https://hf.co/collections/vidore/vidore-benchmark-v3)
+* **Preprint:** [https://arxiv.org/abs/2601.08620](https://arxiv.org/abs/2601.08620)
+* **Blogpost:** [https://huggingface.co/blog/QuentinJG/introducing-vidore-v3](https://huggingface.co/blog/QuentinJG/introducing-vidore-v3)
+* **Leaderboard:** [https://huggingface.co/spaces/vidore/vidore-leaderboard](https://huggingface.co/spaces/vidore/vidore-leaderboard)
+
+### Dataset Summary
+
+Summary of the specific dataset (`Financial - EN`):
+
+- Description: Consists of six 10-K annual reports from major U.S. financial institutions for the fiscal year ended December 31, 2024.
+
+- Language: en
+
+- Domain: Finance
+
+- Document Types: Reports
+
+> [!NOTE]
+> Commit used for end-to-end evaluation: `0fe6508053e8aa31b1f1eaec4553f79e3974f59a`
+
+### Dataset Statistics
+
+- Total Documents : 6
+- Total Pages : 2942
+- Total Queries : 1854
+- Queries without counting translations : 309
+- Average number of pages per query : 4.7
+
+### Languages
+
+The documents in this dataset are in `english`.
+
+### Queries type
+
+![finance_bank_reports_en_query_types](https://cdn-uploads.huggingface.co/production/uploads/66e16a677c2eb2da5109fb5c/2g31_jEyaevHc9qTcVI3z.png)
+
+### Queries format
+
+![finance_bank_reports_en_query_formats](https://cdn-uploads.huggingface.co/production/uploads/66e16a677c2eb2da5109fb5c/5HK-NS65sVe8gfqweNPeB.png)
+
+### Content types
+
+![finance_bank_reports_en_content_types](https://cdn-uploads.huggingface.co/production/uploads/66e16a677c2eb2da5109fb5c/8Gvct6HoUH32YSozY4bvo.png)
+
+## Dataset Structure
+
+### 1. Corpus
+
+Contains the full collection of documents to be searched.
+
+Data instance of a single item from the corpus subset:
+
+```json
+{
+  "corpus_id": <int>,
+  "image": <PIL.Image>,
+  "doc_id": <str>,
+  "markdown": <str>,
+  "page_number_in_doc": <int>
+}
+```
+
+- **corpus_id** <int> : A unique numerical identifier for the corresponding corpus document.
+- **image** <PIL.Image> : The page
+- **doc_id** <str> : name of the document from where the image was extracted
+- **markdown** <str> : Extracted text from the Image using an OCR pipeline
+- **page_number_in_doc** <int> : Original page number inside the document
+
+### 2. Queries
+Contains set of questions or search queries.
+
+Data Instance of a single item from the queries subset:
+
+
+```json
+{
+  "query_id": <int>,
+  "query": <str>,
+  "language": <str>,
+  "query_types": <List[str]>,
+  "query_format": <str>,
+  "content_type": <str>,
+  "raw_answers": <List[str]>,
+  "query_generator": <str>,
+  "query_generation_pipeline": <str>,
+  "source_type": <str>,
+  "query_type_for_generation": <str>,
+  "answer": <str>
+}
+```
+
+- **query_id** <int> : A unique numerical identifier for the query.
+- **query** <str> : The actual text of the search question or statement used for retrieval.
+- **language** <str> : The language of the query text.
+- **query_types** <List[str]> : A list of categories or labels describing the query's intent.
+- **query_format** <str> : The syntactic format of the query ("intruction", "keyword" or "question").
+- **content_type** <str> : The type of visual content present images relevant for the query.
+- **raw_answers** <List[str]> : A list of reference answers written by human annotators.
+- **query_generator** <str> : The source or method used to create the query ("human" or "sdg").
+- **query_generation_pipeline** <str> : Type of SDG pipeline used to create the query (if it was not written by humans)
+- **source_type** <str> : "summary" or "image", metadata about the type of information used by the annotation pipeline to create the query
+- **query_type_for_generation** <str> : The specific type requested when the query was generated
+- **answer** <str> : The answer extracted from the source documents, merged from human annotations using an LLM.
+
+### 3. Qrels
+Maps queries to their corresponding relevant documents.
+
+Data Instance of a single item for the qrels subset:
+```json
+{
+  "query_id": <int>,
+  "corpus_id": <int>,
+  "score": <int>,
+  "content_type": <str>,
+  "bounding_boxes": <List[Tuple[int]]>
+}
+```
+
+- **query_id** <int> : A unique numerical identifier for the query.
+- **corpus_id** <int> : A unique numerical identifier for the corresponding corpus document.
+- **score** <int> : Relevance score for the pair `<query, corpus>`. Can be either 1 (Critically Relevant) or 2 (Fully Relevant):
+  - Fully Relevant (2) - The page contains the complete answer.
+  - Critically Relevant (1) - The page contains facts or information that are required to answer the query, though additional information is required.
+- **content_type** <str> : The type of visual content present images relevant for the query.
+- **bounding_boxes** <List[Tuple[int]]> : Bounding boxes annotated by humans that indicate which part of the image is relevant to the query.
+
+### 4. Original PDFs
+All the original pdfs used to build the corpus are distributed in the "pdfs" folder of this directory.
+
+## License information
+
+All annotations, query-document relevance judgments (qrels), and related metadata generated for this corpus are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+The licensing status of the original source documents (the corpus) and any parsed text (`markdown` column in the corpus) are inherited from their respective publishers. The specific license governing each original document is provided in the `documents_metadata["license"]` field of that document's entry.
+
+## Data Privacy and Removal Requests
+
+While this dataset is released under open licenses, we respect the privacy of individuals and the ownership of source content. If you are a data subject, author, or publisher and are uncomfortable with the inclusion of your data or documents in this release, please contact us at gautier.viaud@illuin.tech and quentin.mace@illuin.tech. We will promptly review your request.

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