📄 Pop Qa Robust¶
PopQA is a large-scale open-domain question answering (QA) dataset, consisting of 14k entity-centric QA pairs. Each question is created by converting a knowledge tuple retrieved from Wikidata using a template. Each question come with the original subject_entitiey, object_entity, and relationship_type annotation, as well as Wikipedia monthly page views. Languages The dataset contains samples in English only.… See the full description on the dataset page: https://huggingface.co/datasets/akariasai/PopQA.
Tags: region:us, category:dataset
cards.pop_qa_robust
TaskCard(
loader=LoadHF(
path="akariasai/PopQA",
),
preprocess_steps=[
Apply(
function="json.loads",
to_field="possible_answers",
_argv=[
"possible_answers",
],
),
Copy(
field_to_field=[
[
"prop_id",
"group_id",
],
],
),
Rename(
field="obj",
to_field="variant_id",
),
Rename(
field="prop",
to_field="variant_type",
),
],
task=Task(
inputs=[
"group_id",
"id",
"question",
"variant_id",
"variant_type",
],
outputs=[
"possible_answers",
],
metrics=[
"metrics.robustness.fixed_group_mean_string_containment",
],
),
templates=TemplatesList(
items=[
MultiReferenceTemplate(
input_format="Question: {question}
Answer:",
references_field="possible_answers",
postprocessors=[
"processors.take_first_non_empty_line",
"processors.lower_case_till_punc",
"processors.to_string_stripped",
],
),
MultiReferenceTemplate(
input_format="Question: {question}
I'm not certain, I think the answer is:",
references_field="possible_answers",
postprocessors=[
"processors.take_first_non_empty_line",
"processors.lower_case_till_punc",
"processors.to_string_stripped",
],
),
MultiReferenceTemplate(
input_format="Question: {question}
I'm absolutely sure the answer is:",
references_field="possible_answers",
postprocessors=[
"processors.take_first_non_empty_line",
"processors.lower_case_till_punc",
"processors.to_string_stripped",
],
),
],
),
)
[source]from unitxt.loaders import LoadHF
from unitxt.operators import Apply, Copy, Rename
from unitxt.task import Task
from unitxt.templates import MultiReferenceTemplate, TemplatesList
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 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 byCopy(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 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 Apply¶
A class used to apply a python function and store the result in a field.
- Args:
function (str): name of function. to_field (str): the field to store the result
any additional arguments are field names whose values will be passed directly to the function specified
Examples: Store in field “b” the uppercase string of the value in field “a”:
Apply("a", function=str.upper, to_field="b")Dump the json representation of field “t” and store back in the same field:
Apply("t", function=json.dumps, to_field="t")Set the time in a field ‘b’:
Apply(function=time.time, to_field="b")
Explanation about Rename¶
Renames fields.
Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.
- Examples:
Rename(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]
Rename(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]
Rename(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]
Rename(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]
Explanation about Task¶
Task packs the different instance fields into dictionaries by their roles in the task.
- Args:
- input_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance input fields and types of respective values. In case a list is passed, each type will be assumed to be Any.
- reference_fields (Union[Dict[str, str], List[str]]):
Dictionary with string names of instance output fields and types of respective values. In case a list is passed, each type will be assumed to be Any.
- metrics (List[str]):
List of names of metrics to be used in the task.
- prediction_type (Optional[str]):
Need to be consistent with all used metrics. Defaults to None, which means that it will be set to Any.
- defaults (Optional[Dict[str, Any]]):
An optional dictionary with default values for chosen input/output keys. Needs to be consistent with names and types provided in ‘input_fields’ and/or ‘output_fields’ arguments. Will not overwrite values if already provided in a given instance.
- The output instance contains three fields:
“input_fields” whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg ‘input_fields’.
“reference_fields” – for the fields listed in Arg “reference_fields”.
“metrics” – to contain the value of Arg ‘metrics’
References: metrics.robustness.fixed_group_mean_string_containment, processors.take_first_non_empty_line, processors.lower_case_till_punc, processors.to_string_stripped
Read more about catalog usage here.