Add criticality prediction configs

#8
by Stern5497 - opened
Files changed (1) hide show
  1. lextreme.py +40 -0
lextreme.py CHANGED
@@ -161,6 +161,41 @@ _SWISS_JUDGMENT_PREDICTION = {
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  ,
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  "label_classes": ["dismissal", "approval"],
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  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  _GERMAN_ARGUMENT_MINING = {
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  "task_type": TaskType.SLTC,
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  "hf_hub_name": "joelito/german_argument_mining",
@@ -4053,6 +4088,11 @@ class LEXTREME(datasets.GeneratorBasedBuilder):
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  LextremeConfig(name="greek_legal_code_subject", **_GREEK_LEGAL_CODE_SUBJECT),
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  LextremeConfig(name="online_terms_of_service_unfairness_levels", **_ONLINE_TERMS_OF_SERVICE_UNFAIRNESS_LEVELS),
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  LextremeConfig(name="turkish_constitutional_court_decisions_judgment", **_TURKISH_CONSTITUTIONAL_COURT_DECISIONS_JUDGMENT),
 
 
 
 
 
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  # MLTC tasks
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  LextremeConfig(name="online_terms_of_service_clause_topics", **_ONLINE_TERMS_OF_SERVICE_CLAUSE_TOPICS),
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  LextremeConfig(name="covid19_emergency_event", **_COVID19_EMERGENCY_EVENT),
 
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  ,
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  "label_classes": ["dismissal", "approval"],
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  }
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+
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+ _SWISS_CRITICLALITY_PREDICTION_BGE_FACTS = {
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+ "task_type": TaskType.SLTC,
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+ "hf_hub_name": "rcds/legal_criticaliy_prediction",
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+ "url": "https://huggingface.co/datasets/rcds/legal_criticality_prediction",
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+ "input_col": "facts",
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+ "label_col": "bge_label",
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+ "description":
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+ """
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+ Legal Criticality Prediction (LCP) is a multilingual, diachronic dataset of 130K Swiss Federal Supreme Court (FSCS) cases annotated with two criticality labels. The bge_label is a binary label (critical, non-critical), while the citation label has 5 classes (critical-1, critical-2, critical-3, critical-4, non-critical). Critical classes of the citation_label are distinct subsets of the critical class of the bge_label. This dataset creates a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
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+ """
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+ ,
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+ # TODO add citation
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+ "label_classes": ["critical", "non-critical"],
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+ }
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+
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+ _SWISS_CRITICLALITY_PREDICTION_BGE_CONSIDERATIONS = {
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+ **_SWISS_CRITICLALITY_PREDICTION_BGE_FACTS,
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+ "input_col": "considerations",
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+ "label_col": "bge_label",
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+ "label_classes": ["critical", "non-critical"],
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+ }
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+ _SWISS_CRITICLALITY_PREDICTION_CITATION_FACTS = {
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+ **_SWISS_CRITICLALITY_PREDICTION_BGE_FACTS,
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+ "input_col": "facts",
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+ "label_col": "citation_label",
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+ "label_classes": ["critical-1", "critical-2", "critical-3", "critical-4", "non-critical"],
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+ }
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+ _SWISS_CRITICLALITY_PREDICTION_CITATION_CONSIDERATIONS = {
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+ **_SWISS_CRITICLALITY_PREDICTION_BGE_FACTS,
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+ "input_col": "considerations",
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+ "label_col": "citation_label",
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+ "label_classes": ["critical-1", "critical-2", "critical-3", "critical-4", "non-critical"],
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+ }
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+
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  _GERMAN_ARGUMENT_MINING = {
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  "task_type": TaskType.SLTC,
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  "hf_hub_name": "joelito/german_argument_mining",
 
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  LextremeConfig(name="greek_legal_code_subject", **_GREEK_LEGAL_CODE_SUBJECT),
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  LextremeConfig(name="online_terms_of_service_unfairness_levels", **_ONLINE_TERMS_OF_SERVICE_UNFAIRNESS_LEVELS),
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  LextremeConfig(name="turkish_constitutional_court_decisions_judgment", **_TURKISH_CONSTITUTIONAL_COURT_DECISIONS_JUDGMENT),
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+ LextremeConfig(name="swiss_criticality_prediction_bge_facts", **_SWISS_CRITICLALITY_PREDICTION_BGE_FACTS),
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+ LextremeConfig(name="swiss_criticality_prediction_bge_considerations", **_SWISS_CRITICLALITY_PREDICTION_BGE_CONSIDERATIONS),
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+ LextremeConfig(name="swiss_criticality_prediction_citation_facts", **_SWISS_CRITICLALITY_PREDICTION_CITATION_FACTS),
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+ LextremeConfig(name="swiss_criticality_prediction_citation_considerations", **_SWISS_CRITICLALITY_PREDICTION_CITATION_CONSIDERATIONS),
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+
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  # MLTC tasks
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  LextremeConfig(name="online_terms_of_service_clause_topics", **_ONLINE_TERMS_OF_SERVICE_CLAUSE_TOPICS),
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  LextremeConfig(name="covid19_emergency_event", **_COVID19_EMERGENCY_EVENT),