📄 En¶

Note

ID: cards.rag.benchmark.clap_nq.en | Type: TaskCard

{
    "__type__": "task_card",
    "loader": {
        "__type__": "load_csv",
        "files": {
            "test": "https://raw.githubusercontent.com/primeqa/clapnq/main/retrieval/dev/question_dev_answerable.tsv",
            "train": "https://raw.githubusercontent.com/primeqa/clapnq/main/retrieval/train/question_train_answerable.tsv"
        },
        "sep": "\t"
    },
    "preprocess_steps": [
        {
            "__type__": "copy",
            "field_to_field": {
                "id": "question_id",
                "question": "question"
            }
        },
        {
            "__type__": "set",
            "fields": {
                "is_answerable_label": true,
                "metadata_field": "",
                "reference_contexts": []
            }
        },
        {
            "__type__": "list_field_values",
            "fields": [
                "doc-id-list"
            ],
            "to_field": "reference_context_ids"
        },
        {
            "__type__": "list_field_values",
            "fields": [
                "answers"
            ],
            "to_field": "reference_answers"
        }
    ],
    "task": "tasks.rag.end_to_end",
    "templates": {
        "default": "templates.rag.end_to_end.json_predictions"
    }
}

Explanation about TaskCard¶

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

Attributes:
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 a 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 LoadCSV¶

Loads data from CSV files.

Supports streaming and can handle large files by loading them in chunks.
Args:
files (Dict[str, str]): A dictionary mapping names to file paths. chunksize : Size of the chunks to load at a time. loader_limit: Optional integer to specify a limit on the number of records to load. streaming: Bool indicating if streaming should be used. sep: String specifying the separator used in the CSV files.

Example:
Loading csv
load_csv = LoadCSV(files={'train': 'path/to/train.csv'}, chunksize=100)

Explanation about ListFieldValues¶

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

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 Set¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams Set(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams Set(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream Set(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. Set(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

References: tasks.rag.end_to_end, templates.rag.end_to_end.json_predictions