📄 REALMMRAG: FinReport¶

We introduced REAL-MM-RAG-Bench, a real-world multi-modal retrieval benchmark designed to evaluate retrieval models in reliable, challenging, and realistic settings. The benchmark was constructed using an automated pipeline, where queries were generated by a vision-language model (VLM), filtered by a large language model (LLM), and rephrased by an LLM to ensure high-quality retrieval evaluation. To simulate real-world retrieval challenges, we introduce multi-level query rephrasing, modifying queries at three distinct levels—from minor wording adjustments to significant structural changes—ensuring models are tested on their true semantic understanding rather than simple keyword matching.

Tags: license:cdla-permissive-2.0, url:https://huggingface.co/datasets//ibm-research/REAL-MM-RAG_FinReport, category:dataset

cards.rag.documents.real_mm_rag_fin_report.en

TaskCard(
    loader=LoadHF(
        path="ibm-research/REAL-MM-RAG_FinReport",
        name="default",
        split="test",
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        RenameSplits(
            mapper={
                "test": "train",
            },
        ),
        HashImage(
            field="image",
            to_field="document_id",
        ),
        Deduplicate(
            by=[
                "document_id",
            ],
        ),
        ToImage(
            field="image",
        ),
        Wrap(
            field="image",
            inside="list",
            to_field="passages",
        ),
    ],
    task="tasks.rag.corpora",
    templates={
        "empty": InputOutputTemplate(
            input_format="",
            output_format="",
        ),
    },
    __title__="REALMMRAG: FinReport",
)

[source]

from unitxt.collections_operators import Wrap
from unitxt.image_operators import HashImage, ToImage
from unitxt.loaders import LoadHF
from unitxt.operators import Deduplicate
from unitxt.splitters import RenameSplits
from unitxt.templates import InputOutputTemplate

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into model input, and specifies the metrics for evaluation of model output.

Args:

loader:
specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps:
list of unitxt operators to process the data source into model input.

task:
specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates:
format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about LoadHF¶

Loads datasets from the HuggingFace Hub.

It supports loading with or without streaming, and it can filter datasets upon loading.
Args:

path:
The path or identifier of the dataset on the HuggingFace Hub.

name:
An optional dataset name.

data_dir:
Optional directory to store downloaded data.

split:
Optional specification of which split to load.

data_files:
Optional specification of particular data files to load. When you provide a list of data_files to Hugging Face’s load_dataset function without explicitly specifying the split argument, these files are automatically placed into the train split.

revision:
Optional. The revision of the dataset. Often the commit id. Use in case you want to set the dataset version.

streaming (bool):
indicating if streaming should be used.

filtering_lambda (str, optional):
A lambda function for filtering the data after loading.

num_proc (int, optional):
Specifies the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

Explanation about Deduplicate¶

Deduplicate the stream based on the given fields.

Args:
by (List[str]): A list of field names to deduplicate by. The combination of these fields’ values will be used to determine uniqueness.

Examples:
>>> dedup = Deduplicate(by=["field1", "field2"])

Explanation about InputOutputTemplate¶

Generate field ‘source’ from fields designated as input, and fields ‘target’ and ‘references’ from fields designated as output, of the processed instance.

Args specify the formatting strings with which to glue together the input and reference fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

References: tasks.rag.corpora