📄 Unfair Tos¶

The UNFAIR-ToS dataset contains 50 Terms of Service (ToS) from on-line platforms (e.g., YouTube, Ebay, Facebook, etc.). The dataset has been annotated on the sentence-level with 8 types of unfair contractual terms (sentences), meaning terms that potentially violate user rights according to the European consumer law… See the full description on the dataset page: https://huggingface.co/datasets/coastalcph/lex_glue.

Tags: annotations_creators:found, arxiv:['2110.00976', '2109.00904', '1805.01217', '2104.08671'], language:en, language_creators:found, license:cc-by-4.0, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:extended, task_categories:['question-answering', 'text-classification'], task_ids:['multi-class-classification', 'multi-label-classification', 'multiple-choice-qa', 'topic-classification']

cards.unfair_tos

type: TaskCard
loader: 
  type: LoadHF
  path: lex_glue
  name: unfair_tos
preprocess_steps: 
  - type: MapInstanceValues
    mappers: 
      labels: 
        0: Limitation of liability
        1: Unilateral termination
        2: Unilateral change
        3: Content removal
        4: Contract by using
        5: Choice of law
        6: Jurisdiction
        7: Arbitration
    process_every_value: True
  - type: Set
    fields: 
      classes: 
        - Limitation of liability
        - Unilateral termination
        - Unilateral change
        - Content removal
        - Contract by using
        - Choice of law
        - Jurisdiction
        - Arbitration
      type_of_classes: contractual clauses
sampler: 
  type: DiverseLabelsSampler
  choices: classes
  labels: labels
task: tasks.classification.multi_label
templates: templates.classification.multi_label.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.

Explanation about DiverseLabelsSampler¶

Selects a balanced sample of instances based on an output field.

(used for selecting demonstrations in-context learning)

The field must contain list of values e.g [‘dog’], [‘cat’], [‘dog’,’cat’,’cow’]. The balancing is done such that each value or combination of values appears as equals as possible in the samples.

The choices param is required and determines which values should be considered.

Example:
If choices is [‘dog,’cat’] , then the following combinations will be considered. [‘’] [‘cat’] [‘dog’] [‘dog’,’cat’]

If the instance contains a value not in the ‘choice’ param, it is ignored. For example, if choices is [‘dog,’cat’] and the instance field is [‘dog’,’cat’,’cow’], then ‘cow’ is ignored then the instance is considered as [‘dog’,’cat’].

Args:
sample_size - number of samples to extract choices - name of input field that contains the list of values to balance on labels - name of output field with labels that must be balanced

Explanation about MapInstanceValues¶

A class used to map instance values into other values.

This class is a type of InstanceOperator, it maps values of instances in a stream using predefined mappers.

Attributes:

mappers (Dict[str, Dict[str, Any]]): The mappers to use for mapping instance values.
Keys are the names of the fields to undergo mapping, and values are dictionaries that define the mapping from old values to new values.

strict (bool): If True, the mapping is applied strictly. That means if a value
does not exist in the mapper, it will raise a KeyError. If False, values that are not present in the mapper are kept as they are.

process_every_value (bool): If True, all fields to be mapped should be lists, and the mapping
is to be applied to their individual elements. If False, mapping is only applied to a field containing a single value.

Examples:
MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}) replaces ‘1’ with ‘hi’ and ‘2’ with ‘bye’ in field ‘a’ in all instances of all streams: instance {“a”:”1”, “b”: 2} becomes {“a”:”hi”, “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, process_every_value=True) Assuming field ‘a’ is a list of values, potentially including “1”-s and “2”-s, this replaces each such “1” with “hi” and “2” – with “bye” in all instances of all streams: instance {“a”: [“1”, “2”], “b”: 2} becomes {“a”: [“hi”, “bye”], “b”: 2}.

MapInstanceValues(mappers={“a”: {“1”: “hi”, “2”: “bye”}}, strict=True) To ensure that all values of field ‘a’ are mapped in every instance, use strict=True. Input instance {“a”:”3”, “b”: 2} will raise an exception per the above call, because “3” is not a key in the mapper of “a”.

MapInstanceValues(mappers={“a”: {str([1,2,3,4]): ‘All’, str([]): ‘None’}}, strict=True) replaces a list [1,2,3,4] with the string ‘All’ and an empty list by string ‘None’. Note that mapped values are defined by their string representation, so mapped values must be converted to strings.

References: templates.classification.multi_label.all, tasks.classification.multi_label