📄 Abercrombie¶

LegalBench is a collection of benchmark tasks for evaluating legal reasoning in large language models… See the full description on the dataset page: https://huggingface.co/datasets/nguha/legalbench

Tags: arxiv:2308.11462, flags:['finance', 'law', 'legal'], language:en, license:other, region:us, size_categories:10K<n<100K, task_categories:['text-classification', 'question-answering', 'text-generation']

cards.legalbench.abercrombie

type: TaskCard
loader: 
  type: LoadHF
  path: nguha/legalbench
  name: abercrombie
preprocess_steps: 
  - type: Shuffle
    page_size: 9223372036854775807
  - type: Rename
    field_to_field: 
      text: text
      answer: label
  - type: Set
    fields: 
      text_type: products
      classes: 
        - generic
        - descriptive
        - suggestive
        - arbitrary
        - fanciful
      type_of_class: type of mark
      classes_descriptions: A mark is generic if it is the common name for the product. A mark is descriptive if it describes a purpose, nature, or attribute of the product. A mark is suggestive if it suggests or implies a quality or characteristic of the product. A mark is arbitrary if it is a real English word that has no relation to the product. A mark is fanciful if it is an invented word.
task: tasks.classification.multi_class.with_classes_descriptions
templates: 
  default: 
    type: InputOutputTemplate
    input_format: Q: {text} What is the {type_of_class}?
    output_format: {label}
    instruction: "{classes_descriptions}\n\nLabel the {type_of_class} for the following {text_type}:\n"
    target_prefix: A: 
    title_fields: []
    postprocessors: 
      - processors.take_first_non_empty_line
      - processors.lower_case_till_punc
[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 Shuffle¶

Shuffles the order of instances in each page of a stream.

Args (of superclass):
page_size (int): The size of each page in the stream. Defaults to 1000.

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 InputOutputTemplate¶

Generate field ‘source’ from fields designated as input, and fields ‘target’ and ‘references’ from fields designated as output, of the processed instance.

Args specify the formatting strings with which to glue together the input and reference fields of the processed instance into one string (‘source’ and ‘target’), and into a list of strings (‘references’).

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 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}}]

References: tasks.classification.multi_class.with_classes_descriptions, processors.take_first_non_empty_line, processors.lower_case_till_punc