📄 Websrc¶

WebSRC v1.0 is a dataset for reading comprehension on structural web pages. The task is to answer questions about web pages, which requires a system to have a comprehensive understanding of the spatial structure and logical structure. WebSRC consists of 6.4K web pages and 400K question-answer pairs about web pages. This cached copy of the dataset is focused on Q&A using the web screenshots (HTML and other metadata are omitted). Questions in WebSRC were created for each segment. Answers are either text spans from web pages or yes/no.

Tags: license:Unknown, multilinguality:monolingual, modalities:['image', 'text'], size_categories:10K<n<100K, task_categories:question-answering, task_ids:extractive-qa

cards.websrc

type: TaskCard
loader: 
  type: LoadHF
  path: rootsautomation/websrc
preprocess_steps: 
  - type: RenameSplits
    mapper: 
      train: train
      dev: test
  - splitters.small_no_dev
  - type: Wrap
    field: answer
    inside: list
    to_field: answers
  - type: DecodeImage
    field: image
    to_field: context
  - type: ToImage
    field: context
  - type: Set
    fields: 
      context_type: image
task: tasks.qa.with_context.abstractive
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 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 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.qa.with_context.abstractive, templates.qa.with_context.all, splitters.small_no_dev