📄 En¶

CLAP NQ is created from the subset of Natural Questions (NQ) that have a long answer but no short answer. NQ consists of ~380k examples. There are ~30k questions that are long answers without short answers excluding tables and lists. To increases the likelihood of longer answers we only explored ones that have more than 5 sentences in the passage. The subset that was annotated consists of ~12k examples. All examples where cohesion of non-consecutive sentences was required for the answer were annotated a second time. The final dataset is made up of all data that went through two rounds of annotation. (We provide the single round annotations as well - it is only training data) An equal amount of unanswerable questions have also been added from the original NQ train/dev sets. Details about the annotation task and unanswerables can be found at https://github.com/primeqa/clapnq/blob/main/annotated_data.

Tags: license:Apache License 2.0, url:https://huggingface.co/datasets/PrimeQA/clapnq, category:dataset

cards.rag.documents.clap_nq.en

TaskCard(
    loader=LoadHF(
        path="PrimeQA/clapnq_passages",
        data_classification_policy=[
            "public",
        ],
    ),
    preprocess_steps=[
        Copy(
            field_to_field={
                "id": "document_id",
                "title": "title",
            },
        ),
        ListFieldValues(
            fields=[
                "text",
            ],
            to_field="passages",
        ),
    ],
    task="tasks.rag.corpora",
    templates={
        "empty": InputOutputTemplate(
            input_format="",
            output_format="",
        ),
    },
)

[source]

from unitxt.loaders import LoadHF
from unitxt.operators import Copy, ListFieldValues
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 Copy¶

Copies values from specified fields to specified fields.

Args (of parent class):
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.

Examples:
An input instance {“a”: 2, “b”: 3}, when processed by Copy(field_to_field={"a": "b"}) would yield {“a”: 2, “b”: 2}, and when processed by Copy(field_to_field={"a": "c"}) would yield {“a”: 2, “b”: 3, “c”: 2}

with field names containing / , we can also copy inside the field: Copy(field="a/0",to_field="a") would process instance {“a”: [1, 3]} into {“a”: 1}

Explanation about ListFieldValues¶

Concatenates values of multiple fields into a list, and assigns it to a new field.

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