📄 Function Of Decision Section¶

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'], category:dataset

cards.legalbench.function_of_decision_section

type: TaskCard
loader: 
  type: LoadHF
  path: nguha/legalbench
  name: function_of_decision_section
preprocess_steps: 
  - type: Shuffle
    page_size: 9223372036854775807
  - type: Rename
    field_to_field: 
      Paragraph: text
      answer: label
  - type: Set
    fields: 
      text_type: text
      classes: 
        - Facts
        - Procedural History
        - Issue
        - Rule
        - Analysis
        - Conclusion
        - Decree
      type_of_class: 
      classes_descriptions: "- Facts: The paragraph describes the factual background that led up to the present lawsuit.\n- Procedural History: The paragraph describes the course of litigation that led to the current proceeding before the court.\n- Issue: The paragraph describes the legal or factual issue that must be resolved by the court.\n- Rule: The paragraph describes a rule of law relevant to resolving the issue.\n- Analysis: The paragraph analyzes the legal issue by applying the relevant legal principles to the facts of the present dispute.\n- Conclusion: The paragraph presents a conclusion of the court.\n- Decree: The paragraph constitutes a decree resolving the dispute"
task: tasks.classification.multi_class.with_classes_descriptions
templates: 
  default: 
    type: InputOutputTemplate
    input_format: {text_type}: {text}
    output_format: {label}
    instruction: "Classify the following {text_type} using the following definitions.\n\n{classes_descriptions}.\n\n"
    target_prefix: Label: 
    title_fields: 
      - text_type
    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.

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.

default_template:
a default template for tasks with very specific task dataset specific template

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 Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds 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:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing 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}}]

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 (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 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’).

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