README.md

---
license: apache-2.0
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- legal
- legal-reasoning
- multiple-choice
- regression
pretty_name: LegalBench Processed by DatologyAI
size_categories:
- 1K<n<10K
configs:
- config_name: canada_tax_court_outcomes
  data_files:
  - split: train
    path: canada_tax_court_outcomes/train-*
  - split: test
    path: canada_tax_court_outcomes/test-*
- config_name: citation_prediction_classification
  data_files:
  - split: train
    path: citation_prediction_classification/train-*
  - split: test
    path: citation_prediction_classification/test-*
- config_name: diversity_3
  data_files:
  - split: train
    path: diversity_3/train-*
  - split: test
    path: diversity_3/test-*
- config_name: diversity_5
  data_files:
  - split: train
    path: diversity_5/train-*
  - split: test
    path: diversity_5/test-*
- config_name: diversity_6
  data_files:
  - split: train
    path: diversity_6/train-*
  - split: test
    path: diversity_6/test-*
- config_name: jcrew_blocker
  data_files:
  - split: train
    path: jcrew_blocker/train-*
  - split: test
    path: jcrew_blocker/test-*
- config_name: learned_hands_benefits
  data_files:
  - split: train
    path: learned_hands_benefits/train-*
  - split: test
    path: learned_hands_benefits/test-*
- config_name: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts
  data_files:
  - split: train
    path: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts/train-*
  - split: test
    path: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts/test-*
- config_name: maud_additional_matching_rights_period_for_modifications_cor
  data_files:
  - split: train
    path: maud_additional_matching_rights_period_for_modifications_cor/train-*
  - split: test
    path: maud_additional_matching_rights_period_for_modifications_cor/test-*
- config_name: maud_change_in_law_subject_to_disproportionate_impact_modifier
  data_files:
  - split: train
    path: maud_change_in_law_subject_to_disproportionate_impact_modifier/train-*
  - split: test
    path: maud_change_in_law_subject_to_disproportionate_impact_modifier/test-*
- config_name: maud_changes_in_gaap_or_other_accounting_principles_subject_to_disproportionate_impact_modifier
  data_files:
  - split: train
    path: maud_changes_in_gaap_or_other_accounting_principles_subject_to_disproportionate_impact_modifier/train-*
  - split: test
    path: maud_changes_in_gaap_or_other_accounting_principles_subject_to_disproportionate_impact_modifier/test-*
- config_name: maud_cor_permitted_in_response_to_intervening_event
  data_files:
  - split: train
    path: maud_cor_permitted_in_response_to_intervening_event/train-*
  - split: test
    path: maud_cor_permitted_in_response_to_intervening_event/test-*
- config_name: maud_fls_mae_standard
  data_files:
  - split: train
    path: maud_fls_mae_standard/train-*
  - split: test
    path: maud_fls_mae_standard/test-*
- config_name: maud_includes_consistent_with_past_practice
  data_files:
  - split: train
    path: maud_includes_consistent_with_past_practice/train-*
  - split: test
    path: maud_includes_consistent_with_past_practice/test-*
- config_name: maud_initial_matching_rights_period_cor
  data_files:
  - split: train
    path: maud_initial_matching_rights_period_cor/train-*
  - split: test
    path: maud_initial_matching_rights_period_cor/test-*
- config_name: maud_ordinary_course_efforts_standard
  data_files:
  - split: train
    path: maud_ordinary_course_efforts_standard/train-*
  - split: test
    path: maud_ordinary_course_efforts_standard/test-*
- config_name: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic_related_governmental_responses_or_measures
  data_files:
  - split: train
    path: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic_related_governmental_responses_or_measures/train-*
  - split: test
    path: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic_related_governmental_responses_or_measures/test-*
- config_name: maud_pandemic_or_other_public_health_event_subject_to_disproportionate_impact_modifier
  data_files:
  - split: train
    path: maud_pandemic_or_other_public_health_event_subject_to_disproportionate_impact_modifier/train-*
  - split: test
    path: maud_pandemic_or_other_public_health_event_subject_to_disproportionate_impact_modifier/test-*
- config_name: maud_type_of_consideration
  data_files:
  - split: train
    path: maud_type_of_consideration/train-*
  - split: test
    path: maud_type_of_consideration/test-*
- config_name: personal_jurisdiction
  data_files:
  - split: train
    path: personal_jurisdiction/train-*
  - split: test
    path: personal_jurisdiction/test-*
- config_name: sara_entailment
  data_files:
  - split: train
    path: sara_entailment/train-*
  - split: test
    path: sara_entailment/test-*
- config_name: sara_numeric
  data_files:
  - split: train
    path: sara_numeric/train-*
  - split: test
    path: sara_numeric/test-*
- config_name: supply_chain_disclosure_best_practice_accountability
  data_files:
  - split: train
    path: supply_chain_disclosure_best_practice_accountability/train-*
  - split: test
    path: supply_chain_disclosure_best_practice_accountability/test-*
- config_name: supply_chain_disclosure_best_practice_certification
  data_files:
  - split: train
    path: supply_chain_disclosure_best_practice_certification/train-*
  - split: test
    path: supply_chain_disclosure_best_practice_certification/test-*
- config_name: supply_chain_disclosure_best_practice_training
  data_files:
  - split: train
    path: supply_chain_disclosure_best_practice_training/train-*
  - split: test
    path: supply_chain_disclosure_best_practice_training/test-*
- config_name: telemarketing_sales_rule
  data_files:
  - split: train
    path: telemarketing_sales_rule/train-*
  - split: test
    path: telemarketing_sales_rule/test-*
dataset_info:
- config_name: canada_tax_court_outcomes
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 7864
    num_examples: 6
  - name: test
    num_bytes: 392042
    num_examples: 244
  download_size: 161532
  dataset_size: 399906
- config_name: citation_prediction_classification
  features:
  - name: answer
    dtype: string
  - name: citation
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 1471
    num_examples: 2
  - name: test
    num_bytes: 60272
    num_examples: 108
  download_size: 30302
  dataset_size: 61743
- config_name: diversity_3
  features:
  - name: aic_is_met
    dtype: string
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: parties_are_diverse
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 3040
    num_examples: 6
  - name: test
    num_bytes: 153782
    num_examples: 300
  download_size: 38926
  dataset_size: 156822
- config_name: diversity_5
  features:
  - name: aic_is_met
    dtype: string
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: parties_are_diverse
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 3520
    num_examples: 6
  - name: test
    num_bytes: 177382
    num_examples: 300
  download_size: 45990
  dataset_size: 180902
- config_name: diversity_6
  features:
  - name: aic_is_met
    dtype: string
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: parties_are_diverse
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 5087
    num_examples: 6
  - name: test
    num_bytes: 253115
    num_examples: 300
  download_size: 66869
  dataset_size: 258202
- config_name: jcrew_blocker
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 16657
    num_examples: 6
  - name: test
    num_bytes: 137273
    num_examples: 54
  download_size: 79424
  dataset_size: 153930
- config_name: learned_hands_benefits
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 17791
    num_examples: 6
  - name: test
    num_bytes: 188795
    num_examples: 66
  download_size: 134816
  dataset_size: 206586
- config_name: maud_ability_to_consummate_concept_is_subject_to_mae_carveouts
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 10943
    num_examples: 1
  - name: test
    num_bytes: 628674
    num_examples: 69
  download_size: 226362
  dataset_size: 639617
- config_name: maud_additional_matching_rights_period_for_modifications_cor
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
    num_bytes: 4807
    num_examples: 1
  - name: test
    num_bytes: 698844
    num_examples: 158
  download_size: 242597
  dataset_size: 703651
- config_name: maud_change_in_law_subject_to_disproportionate_impact_modifier
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
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  splits:
  - name: train
    num_bytes: 12303
    num_examples: 1
  - name: test
    num_bytes: 927240
    num_examples: 99
  download_size: 323047
  dataset_size: 939543
- config_name: maud_changes_in_gaap_or_other_accounting_principles_subject_to_disproportionate_impact_modifier
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 921626
    num_examples: 98
  download_size: 314921
  dataset_size: 933957
- config_name: maud_cor_permitted_in_response_to_intervening_event
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 419104
    num_examples: 100
  download_size: 149850
  dataset_size: 424649
- config_name: maud_fls_mae_standard
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
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  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 733140
    num_examples: 77
  download_size: 237744
  dataset_size: 742913
- config_name: maud_includes_consistent_with_past_practice
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
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  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 333827
    num_examples: 181
  download_size: 108295
  dataset_size: 336378
- config_name: maud_initial_matching_rights_period_cor
  features:
  - name: answer
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  - name: index
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  - name: text
    dtype: string
  - name: input
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  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 698682
    num_examples: 158
  download_size: 249943
  dataset_size: 705241
- config_name: maud_ordinary_course_efforts_standard
  features:
  - name: answer
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  - name: index
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  - name: text
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  - name: input
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  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 340523
    num_examples: 181
  download_size: 111087
  dataset_size: 342930
- config_name: maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic_related_governmental_responses_or_measures
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
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  - name: input
    dtype: string
  splits:
  - name: train
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    num_examples: 1
  - name: test
    num_bytes: 932728
    num_examples: 98
  download_size: 331916
  dataset_size: 940579
- config_name: maud_pandemic_or_other_public_health_event_subject_to_disproportionate_impact_modifier
  features:
  - name: answer
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  - name: index
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  - name: text
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  splits:
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    num_examples: 1
  - name: test
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    num_examples: 98
  download_size: 344444
  dataset_size: 934540
- config_name: maud_type_of_consideration
  features:
  - name: answer
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  - name: index
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  - name: text
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    num_examples: 1
  - name: test
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    num_examples: 172
  download_size: 100428
  dataset_size: 338161
- config_name: personal_jurisdiction
  features:
  - name: answer
    dtype: string
  - name: index
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  - name: slice
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  - name: text
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  - name: test
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    num_examples: 50
  download_size: 25202
  dataset_size: 62553
- config_name: sara_entailment
  features:
  - name: answer
    dtype: string
  - name: case id
    dtype: string
  - name: description
    dtype: string
  - name: index
    dtype: string
  - name: question
    dtype: string
  - name: statute
    dtype: string
  - name: text
    dtype: string
  - name: input
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- config_name: sara_numeric
  features:
  - name: answer
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  - name: case id
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  - name: description
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  - name: index
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  - name: statute
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  - name: test
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  download_size: 1133960
  dataset_size: 8953110
- config_name: supply_chain_disclosure_best_practice_accountability
  features:
  - name: answer
    dtype: string
  - name: index
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  - name: text
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  - name: input
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  - name: train
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  - name: test
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    num_examples: 379
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  dataset_size: 2912547
- config_name: supply_chain_disclosure_best_practice_certification
  features:
  - name: answer
    dtype: string
  - name: index
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  - name: text
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  - name: train
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    num_examples: 8
  - name: test
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    num_examples: 378
  download_size: 1303303
  dataset_size: 2816328
- config_name: supply_chain_disclosure_best_practice_training
  features:
  - name: answer
    dtype: string
  - name: index
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  - name: text
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    num_examples: 8
  - name: test
    num_bytes: 2883341
    num_examples: 379
  download_size: 1325423
  dataset_size: 2935711
- config_name: telemarketing_sales_rule
  features:
  - name: answer
    dtype: string
  - name: index
    dtype: string
  - name: text
    dtype: string
  - name: input
    dtype: string
  splits:
  - name: train
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    num_examples: 4
  - name: test
    num_bytes: 39244
    num_examples: 47
  download_size: 19388
  dataset_size: 42130
---

# DatologyAI/legalbench

## Overview
This dataset contains 26 legal reasoning tasks from [LegalBench](https://github.com/HazyResearch/legalbench), processed for easy use in language model evaluation. Each task preserves its original data and includes an additional `input` column with a formatted prompt, generated using the LegalBench registry, ready to be fed directly into language models.

## Task Categories

- **Basic Legal**: `canada_tax_court_outcomes`, `jcrew_blocker`, `learned_hands_benefits`, `telemarketing_sales_rule`
- **Citation**: `citation_prediction_classification`
- **Diversity Analysis**: `diversity_3`, `diversity_5`, `diversity_6`
- **Jurisdiction**: `personal_jurisdiction`
- **SARA Analysis**: `sara_entailment`, `sara_numeric`
- **Supply Chain Disclosure**: `supply_chain_disclosure_best_practice_accountability`, `supply_chain_disclosure_best_practice_certification`, `supply_chain_disclosure_best_practice_training`
- **MAUD Contract Analysis**: `maud_ability_to_consummate_concept_is_subject_to_mae_carveouts`, `maud_additional_matching_rights_period_for_modifications_cor`, `maud_change_in_law_subject_to_disproportionate_impact_modifier`, `maud_changes_in_gaap_or_other_accounting_principles_subject_to_disproportionate_impact_modifier`, `maud_cor_permitted_in_response_to_intervening_event`, `maud_fls_mae_standard`, `maud_includes_consistent_with_past_practice`, `maud_initial_matching_rights_period_cor`, `maud_ordinary_course_efforts_standard`, `maud_pandemic_or_other_public_health_event_subject_to_disproportionate_impact_modifier`, `maud_pandemic_or_other_public_health_event_specific_reference_to_pandemic_related_governmental_responses_or_measures`, `maud_type_of_consideration`

## Task Details

<div style="overflow-x: auto; max-height: 400px; border: 1px solid #ddd;">
<table>
<thead>
<tr>
<th>Task Name</th>
<th>Type</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr><td>canada\_tax\_court\_outcomes</td><td>multiple_choice</td><td>INSTRUCTIONS: Indicate whether the following judgment excerpt from a Tax Court of Canada decision allows the appeal or dismisses the appeal. Where the result is mixed, indicate that the appeal was allowed. Ignore costs orders. Where the outcome is unclear indicate other.<br>Options: allowed, dismissed, other</td></tr>
<tr><td>citation\_prediction\_classification</td><td>multiple_choice</td><td>Can the case can be used as a citation for the provided text?</td></tr>
<tr><td>diversity\_3</td><td>multiple_choice</td><td>Diversity jurisdiction exists when there is (1) complete diversity between plaintiffs and defendants, and (2) the amount-in-controversy (AiC) is greater than $75k.</td></tr>
<tr><td>diversity\_5</td><td>multiple_choice</td><td>Diversity jurisdiction exists when there is (1) complete diversity between plaintiffs and defendants, and (2) the amount-in-controversy (AiC) is greater than $75k.</td></tr>
<tr><td>diversity\_6</td><td>multiple_choice</td><td>Diversity jurisdiction exists when there is (1) complete diversity between plaintiffs and defendants, and (2) the amount-in-controversy (AiC) is greater than $75k.</td></tr>
<tr><td>jcrew\_blocker</td><td>multiple_choice</td><td>The JCrew Blocker is a provision that typically includes (1) a prohibition on the borrower from transferring IP to an unrestricted subsidiary, and (2) a requirement that the borrower obtains the consent of its agent/lenders before transferring IP to any subsidiary. Do the following provisions contain JCrew Blockers?</td></tr>
<tr><td>learned\_hands\_benefits</td><td>multiple_choice</td><td>Does the post discuss public benefits and social services that people can get from the government, like for food, disability, old age, housing, medical help, unemployment, child care, or other social needs?</td></tr>
<tr><td>maud\_ability\_to\_consummate\_concept\_is\_subject\_to\_mae\_carveouts</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Is the 'ability to consummate' concept subject to Material Adverse Effect (MAE) carveouts?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_additional\_matching\_rights\_period\_for\_modifications\_cor</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: How long is the additional matching rights period for modifications in case the board changes its recommendation?<br>Option A: 2 business days or less<br>Option B: 3 business days<br>Option C: 3 days<br>Option D: 4 business days<br>Option E: 5 business days<br>Option F: &gt; 5 business days<br>Option G: None</td></tr>
<tr><td>maud\_change\_in\_law\_subject\_to\_disproportionate\_impact\_modifier</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Do changes in law that have disproportionate impact qualify for Material Adverse Effect (MAE)?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_changes\_in\_gaap\_or\_other\_accounting\_principles\_subject\_to\_disproportionate\_impact\_modifier</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Do changes in GAAP or other accounting principles that have disproportionate impact qualify for Material Adverse Effect (MAE)?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_cor\_permitted\_in\_response\_to\_intervening\_event</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Is Change of Recommendation permitted in response to an intervening event?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_fls\_mae\_standard</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: What is the Forward Looking Standard (FLS) with respect to Material Adverse Effect (MAE)?<br>Option A: "Could" (reasonably) be expected to<br>Option B: "Would"<br>Option C: "Would" (reasonably) be expected to<br>Option D: No<br>Option E: Other forward-looking standard</td></tr>
<tr><td>maud\_includes\_consistent\_with\_past\_practice</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Does the wording of the Efforts Covenant clause include 'consistent with past practice'?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_initial\_matching\_rights\_period\_cor</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: How long is the initial matching rights period in case the board changes its recommendation?<br>Option A: 2 business days or less<br>Option B: 3 business days<br>Option C: 3 calendar days<br>Option D: 4 business days<br>Option E: 4 calendar days<br>Option F: 5 business days<br>Option G: Greater than 5 business days</td></tr>
<tr><td>maud\_ordinary\_course\_efforts\_standard</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: What is the efforts standard?<br>Option A: Commercially reasonable efforts<br>Option B: Flat covenant (no efforts standard)<br>Option C: Reasonable best efforts</td></tr>
<tr><td>maud\_pandemic\_or\_other\_public\_health\_event\_subject\_to\_disproportionate\_impact\_modifier</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Do pandemics or other public health events have to have disproportionate impact to qualify for Material Adverse Effect (MAE)?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_pandemic\_or\_other\_public\_health\_event\_specific\_reference\_to\_pandemic\_related\_governmental\_responses\_or\_measures</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: Is there specific reference to pandemic-related governmental responses or measures in the clause that qualifies pandemics or other public health events for Material Adverse Effect (MAE)?<br>Option A: No<br>Option B: Yes</td></tr>
<tr><td>maud\_type\_of\_consideration</td><td>multiple_choice</td><td>Instruction: Read the segment of a merger agreement and answer the multiple-choice question by choosing the option that best characterizes the agreement.<br>Question: What type of consideration is specified in this agreement?<br>Option A: All Cash<br>Option B: All Stock<br>Option C: Mixed Cash/Stock<br>Option D: Mixed Cash/Stock: Election</td></tr>
<tr><td>personal\_jurisdiction</td><td>multiple_choice</td><td>There is personal jurisdiction over a defendant in the state where the defendant is domiciled, or when (1) the defendant has sufficient contacts with the state, such that they have availed itself of the privileges of the state and (2) the claim arises out of the nexus of the defendant's contacts with the state.</td></tr>
<tr><td>sara\_entailment</td><td>multiple_choice</td><td>Determine whether the following statements are entailed under the statute.</td></tr>
<tr><td>sara\_numeric</td><td>regression</td><td>Answer the following questions.</td></tr>
<tr><td>supply\_chain\_disclosure\_best\_practice\_accountability</td><td>multiple_choice</td><td>Evaluates supply chain disclosure practices</td></tr>
<tr><td>supply\_chain\_disclosure\_best\_practice\_certification</td><td>multiple_choice</td><td>Evaluates supply chain disclosure practices</td></tr>
<tr><td>supply\_chain\_disclosure\_best\_practice\_training</td><td>multiple_choice</td><td>Evaluates supply chain disclosure practices</td></tr>
<tr><td>telemarketing\_sales\_rule</td><td>multiple_choice</td><td>The Telemarketing Sales Rule is provided by 16 C.F.R. § 310.3(a)(1) and 16 C.F.R. § 310.3(a)(2).</td></tr>
</tbody>
</table>
</div>

## Data Format

Each dataset retains its original columns from LegalBench and adds an `input` column containing a pre-formatted prompt based on the task's instructions and template from the LegalBench registry. This `input` column is designed for direct use with language models. The column structure varies by task; common examples include:

- **Basic Legal**: `answer`, `index`, `text`, `input`
- **Citation**: `answer`, `citation`, `index`, `text`, `input`
- **Diversity Analysis**: `aic_is_met`, `answer`, `index`, `parties_are_diverse`, `text`, `input`
- **Jurisdiction**: `answer`, `index`, `slice`, `text`, `input`
- **SARA Analysis**: `answer`, `case id`, `description`, `index`, `question`, `statute`, `text`, `input`
- **Supply Chain Disclosure**: `answer`, `index`, `text`, `input`
- **MAUD Contract Analysis**: `answer`, `index`, `text`, `input`

## Usage

Load and use a task dataset as follows:

```python
from datasets import load_dataset

# Load a specific task
dataset = load_dataset("DatologyAI/legalbench", "canada_tax_court_outcomes")

# Access the formatted input and answer
example = dataset["test"][0]
print("Input:", example["input"])
print("Answer:", example["answer"])
```

## Model Evaluation Example

Evaluate a language model on a task:

```python
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_name = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load a task
dataset = load_dataset("DatologyAI/legalbench", "personal_jurisdiction")
example = dataset["test"][0]

# Generate response
inputs = tokenizer(example["input"], return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_new_tokens=10, temperature=0.0)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

print(f"Gold answer: {example['answer']}")
print(f"Model response: {response}")
```