📄 4K¶

The WikiQA task is the task of answering a question based on the information given in a Wikipedia document. We have built upon the short answer format data in Google Natural Questions to construct our QA task. It is formatted as a document and a question. We ensure the answer to the question is a short answer which is either a single word or a small sentence directly cut pasted from the document. Having the task structured as such, we can… See the full description on the dataset page: https://huggingface.co/datasets/abacusai/WikiQA-Free_Form_QA.

Tags: region:us

cards.ffqa_filtered.4k

type: TaskCard
loader: 
  type: LoadHF
  path: abacusai/WikiQA-Free_Form_QA
preprocess_steps: 
  - type: Copy
    field_to_field: 
      conversations/0/value: inputs
      conversations/0/tok_len: inputs_len
      conversations/1/value: answer
  - type: ListFieldValues
    fields: 
      - answer
    to_field: answers
  - type: FilterByCondition
    values: 
      inputs_len: 4096
    condition: lt
  - type: ExecuteExpression
    expression: "re.search(r\"Document:\s(.*)(\n\n|$)\", inputs).group(1)"
    imports_list: 
      - re
    to_field: context
  - type: ExecuteExpression
    expression: "re.search(r\"Question:\s(.*)(\n\n|$)\", inputs).group(1)"
    imports_list: 
      - re
    to_field: question
  - type: Set
    fields: 
      context_type: document
  - type: SplitRandomMix
    mix: 
      train: 4k[80%]
      validation: 4k[10%]
      test: 4k[10%]
task: tasks.qa.with_context.extractive
templates: templates.qa.with_context.all
[source]

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into 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 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 SplitRandomMix¶

Splits a multistream into new streams (splits), whose names, source input stream, and amount of instances, are specified by arg ‘mix’.

The keys of arg ‘mix’, are the names of the new streams, the values are of the form: ‘name-of-source-stream[percentage-of-source-stream]’ Each input instance, of any input stream, is selected exactly once for inclusion in any of the output streams.

Examples: When processing a multistream made of two streams whose names are ‘train’ and ‘test’, by SplitRandomMix(mix = { “train”: “train[99%]”, “validation”: “train[1%]”, “test”: “test” }) the output is a multistream, whose three streams are named ‘train’, ‘validation’, and ‘test’. Output stream ‘train’ is made of randomly selected 99% of the instances of input stream ‘train’, output stream ‘validation’ is made of the remaining 1% instances of input ‘train’, and output stream ‘test’ is made of the whole of input stream ‘test’.

When processing the above input multistream by SplitRandomMix(mix = { “train”: “train[50%]+test[0.1]”, “validation”: “train[50%]+test[0.2]”, “test”: “test[0.7]” }) the output is a multistream, whose three streams are named ‘train’, ‘validation’, and ‘test’. Output stream ‘train’ is made of randomly selected 50% of the instances of input stream ‘train’ + randomly selected 0.1 (i.e., 10%) of the instances of input stream ‘test’. Output stream ‘validation’ is made of the remaining 50% instances of input ‘train’+ randomly selected 0.2 (i.e., 20%) of the original instances of input ‘test’, that were not selected for output ‘train’, and output stream ‘test’ is made of the remaining instances of input ‘test’.

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. 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: A lambda function for filtering the data after loading. num_proc: Optional integer to specify 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 ExecuteExpression¶

Compute an expression, specified as a string to be eval-uated, over the instance’s fields, and store the result in field to_field.

Raises an error if a field mentioned in the query is missing from the instance.

Args:
expression (str): an expression to be evaluated over the fields of the instance to_field (str): the field where the result is to be stored into imports_list (List[str]): names of imports needed for the eval of the query (e.g. ‘re’, ‘json’)

Examples:
When instance {“a”: 2, “b”: 3} is process-ed by operator ExecuteExpression(expression=”a+b”, to_field = “c”) the result is {“a”: 2, “b”: 3, “c”: 5}

When instance {“a”: “hello”, “b”: “world”} is process-ed by operator ExecuteExpression(expression = “a+’ ‘+b”, to_field = “c”) the result is {“a”: “hello”, “b”: “world”, “c”: “hello world”}

Explanation about FilterByCondition¶

Filters a stream, yielding only instances in which the values in required fields follow the required condition operator.

Raises an error if a required field name is missing from the input instance.

Args:
values (Dict[str, Any]): Field names and respective Values that instances must match according the condition, to be included in the output. condition: the name of the desired condition operator between the specified (sub) field’s value and the provided constant value. Supported conditions are (“gt”, “ge”, “lt”, “le”, “ne”, “eq”, “in”,”not in”) error_on_filtered_all (bool, optional): If True, raises an error if all instances are filtered out. Defaults to True.

Examples:
FilterByCondition(values = {“a”:4}, condition = “gt”) will yield only instances where field “a” contains a value > 4 FilterByCondition(values = {“a”:4}, condition = “le”) will yield only instances where “a”<=4 FilterByCondition(values = {“a”:[4,8]}, condition = “in”) will yield only instances where “a” is 4 or 8 FilterByCondition(values = {“a”:[4,8]}, condition = “not in”) will yield only instances where “a” different from 4 or 8 FilterByCondition(values = {“a/b”:[4,8]}, condition = “not in”) will yield only instances where “a” is

a dict in which key “b” is mapped to a value that is neither 4 nor 8

FilterByCondition(values = {“a[2]”:4}, condition = “le”) will yield only instances where “a” is a list whose 3-rd
element is <= 4

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.

Explanation about ListFieldValues¶

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

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