Duplicate from llamaindex/vdr-multilingual-train

Co-authored-by: Marco Cimolai <marco@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- de
+- it
+- fr
+- es
+- en
+multilinguality:
+- multilingual
+size_categories:
+- 100K<n<1M
+pretty_name: Multilingual Visual Document Retrieval
+dataset_info:
+- config_name: en
+  features:
+  - name: id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: image
+    dtype: image
+  - name: negatives
+    sequence:
+      dtype: string
+  - name: language
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 19695589638
+    num_examples: 94225
+  download_size: 19695589638
+  dataset_size: 19695589638
+- config_name: es
+  features:
+  - name: id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: image
+    dtype: image
+  - name: negatives
+    sequence:
+      dtype: string
+  - name: language
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 19881676198
+    num_examples: 102685
+  download_size: 19881676198
+  dataset_size: 19881676198
+- config_name: it
+  features:
+  - name: id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: image
+    dtype: image
+  - name: negatives
+    sequence:
+      dtype: string
+  - name: language
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 20278641470
+    num_examples: 98747
+  download_size: 20278641470
+  dataset_size: 20278641470
+- config_name: de
+  features:
+  - name: id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: image
+    dtype: image
+  - name: negatives
+    sequence:
+      dtype: string
+  - name: language
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 19629975126
+    num_examples: 100713
+  download_size: 19629975126
+  dataset_size: 19629975126
+- config_name: fr
+  features:
+  - name: id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: image
+    dtype: image
+  - name: negatives
+    sequence:
+      dtype: string
+  - name: language
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 20825335207
+    num_examples: 99797
+  download_size: 20825335207
+  dataset_size: 20825335207
+configs:
+- config_name: en
+  data_files:
+  - split: train
+    path: en/train-*
+- config_name: it
+  data_files:
+  - split: train
+    path: it/train-*
+- config_name: fr
+  data_files:
+  - split: train
+    path: fr/train-*
+- config_name: es
+  data_files:
+  - split: train
+    path: es/train-*
+- config_name: de
+  data_files:
+  - split: train
+    path: de/train-*
+license: apache-2.0
+---
+
+# Multilingual Visual Document Retrieval Dataset
+
+![](training_cover.png)
+
+> This dataset consists of **500k multilingual query image samples**, collected and generated from scratch using public internet pdfs. The queries are synthetic and generated using VLMs (gemini-1.5-pro and Qwen2-VL-72B).
+
+It was used to train the [vdr-2b-multi-v1](https://huggingface.co/llamaindex/vdr-2b-multi-v1) retrieval multimodal, multilingual embedding model.
+
+## How it was created
+
+This is the entire data pipeline used to create the Italian subset of this dataset. Each step of the process is explained in detail below.
+
+![](datapipeline.png)
+
+#### Data gathering
+
+For each language, we generate a long list of search queries covering many different topics, which are then used to search for PDFs. We use the language filtering capabilities of the search engine to scrape documents that are only in the specified language. This "search by topic" technique ensures that the model has seen a lot of diverse topics and domains, and that it performs well in real life scenarios.
+
+The scraping process produced ~50k multilingual documents. Contrary to the method used in the previous [`mcdse-2b-v1`](https://huggingface.co/marco/mcdse-2b-v1) model, pages were not extracted randomly. Instead, each page of each PDF was run through a document layout analysis model to determine whether the page contained more textual or visual elements. The result is a number that classifies the page as text-only, visual-only or mixed. This labelling step was then used to sample ~100k pages, ensuring they were evenly distributed by page type.
+
+#### Synthetic generation
+The queries were then generated using gemini-1.5-pro and Qwen2-VL-72B. They were tasked to come up with a specific and a general question. Only the specific question is then used to train the model, but forcing the LLM to distinguish between the two often resulted in stronger specific questions for information retrieval training.
+
+After generation, a further cleaning step ensures that the questions are good enough for training. This includes:
+
+- Ensuring the language is correct
+- Fix formatting problems
+- Remove markdown
+- Ensuring that only one question is posed
+- Removing grounding phrases (e.g. "according to Figure 1", "this document", ...)
+
+
+#### Filtering and hard-negative mining
+
+This cleaning step ensures that the queries are syntactically correct and follow some strict guidelines. But it still doesn't ensure that the queries are good enough for information retrieval.
+
+To filter out bad questions, we have embedded and indexed each broad query with the voyage-3 embedding model. For each specific question, we search the index. The query is marked as 'good' if its associated broad question appears in the top 100 results. This method removes low entropy, duplicate or too similar questions. On average, 40% of queries were removed from each language dataset.
+
+Hard negatives were then mined using voyage-3 only on specific questions with a fixed threshold of 0.75. Experiments were also carried out using positive aware negative mining as used by [nvidia/NV-Retriever-v1](https://huggingface.co/nvidia/NV-Retriever-v1), but on this dataset it seems to produce too easy/distant negatives.
+
+# Info and usage
+
+The training dataset consists of 496,167 PDF pages, of which only 280,679 are associated with the filtered queries (using the method described above). The images that remain without a query are still used as hard negatives.
+
+|  Language | # filtered queries | # unfiltered queries |
+|----------:|-------------------:|---------------------:|
+|   English |             53,512 |               94,225 |
+|   Spanish |             58,738 |              102,685 |
+|   Italian |             54,942 |               98,747 |
+|    German |             58,217 |              100,713 |
+|    French |             55,270 |               99,797 |
+| **TOTAL** |        **280,679** |          **496,167** |
+
+### Schema
+
+| **Column** |      **Type** |
+|-----------:|--------------:|
+|         id |           str |
+|      query |           str |
+|      image |         image |
+|  negatives | array[string] |
+|   language |        string |
+
+The `id` column represents the identification number of the positive image. The `negatives` column contains all the ids of the associated negatives, sorted in ascending order by their distance from the positive.
+The last rows do not contain any negatives or queries, as their queries have been filtered out by the data curation process. The images are still being used as negatives for other earlier queries.
+
+The dataset consists of 5 different subsets for each language. You can download languages individually by specifying the language subset in [`load_dataset`](https://huggingface.co/docs/datasets/main/en/package_reference/loading_methods#datasets.load_dataset):
+
+```python
+from datasets import load_dataset
+
+italian_dataset = load_dataset("llamaindex/vdr-multilingual-train", "it", split="train")
+
+english_dataset = load_dataset("llamaindex/vdr-multilingual-train", "en", split="train")
+
+french_dataset = load_dataset("llamaindex/vdr-multilingual-train", "fr", split="train")
+
+german_dataset = load_dataset("llamaindex/vdr-multilingual-train", "de", split="train")
+
+spanish_dataset = load_dataset("llamaindex/vdr-multilingual-train", "es", split="train")
+```

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