📄 Qa¶

CoQA is a large-scale dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. Supported Tasks and Leaderboards More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/coqa.

Tags: annotations_creators:crowdsourced, arxiv:['1808.07042', '1704.04683', '1506.03340'], flags:['conversational-qa'], language:en, language_creators:found, license:other, multilinguality:monolingual, region:us, size_categories:1K<n<10K, source_datasets:['extended|race', 'extended|cnn_dailymail', 'extended|wikipedia', 'extended|other'], task_categories:question-answering, task_ids:extractive-qa, category:dataset

cards.coqa.qa

TaskCard(
    loader=LoadHF(
        path="stanfordnlp/coqa",
    ),
    preprocess_steps=[
        "splitters.small_no_test",
        Set(
            fields={
                "context_type": "story",
            },
        ),
        ZipFieldValues(
            fields=[
                "questions",
                "answers/input_text",
            ],
            to_field="dialog",
        ),
        Dictify(
            field="dialog",
            with_keys=[
                "user",
                "system",
            ],
            process_every_value=True,
        ),
        DuplicateBySubLists(
            field="dialog",
        ),
        Get(
            field="dialog",
            item=-1,
            to_field="last_turn",
        ),
        Copy(
            field_to_field={
                "last_turn/user": "question",
                "last_turn/system": "answer",
            },
        ),
        Wrap(
            field="answer",
            inside="list",
            to_field="answers",
        ),
        SerializeDialog(
            field="dialog",
            to_field="context",
            context_field="story",
        ),
    ],
    task="tasks.qa.extractive",
    templates="templates.qa.with_context.all",
)

[source]

from unitxt.collections_operators import Dictify, DuplicateBySubLists, Get, Wrap
from unitxt.dialog_operators import SerializeDialog
from unitxt.loaders import LoadHF
from unitxt.operators import Copy, Set, ZipFieldValues

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 Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds 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:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing 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.

Explanation about ZipFieldValues¶

Zips values of multiple fields in a given instance, similar to list(zip(*fields)).

The value in each of the specified ‘fields’ is assumed to be a list. The lists from all ‘fields’ are zipped, and stored into ‘to_field’.

If ‘longest’=False, the length of the zipped result is determined by the shortest input value.

If ‘longest’=True, the length of the zipped result is determined by the longest input, padding shorter inputs with None-s.

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 SerializeDialog¶

Serializes dialog data for feeding into a model.

This class takes structured dialog data and converts it into a text format according to a specified template. It allows for the inclusion or exclusion of system responses and can operate on a per-turn basis or aggregate the entire dialog.

Args:

field (str):
The field in the input data that contains the dialog.

to_field (Optional[str]):
The field in the output data where the serialized dialog will be stored.

last_user_turn_to_field (Optional[str]):
Field to store the last user turn.

last_system_turn_to_field (Optional[str]):
Field to store the last system turn.

context_field (Optional[str]):
Field that contains additional context to be prepended to the dialog.

References: templates.qa.with_context.all, splitters.small_no_test, tasks.qa.extractive