Datasets:

Modalities:
Text
Formats:
parquet
ArXiv:
Libraries:
Datasets
Dask
License:
srehaag commited on
Commit
523fb4b
1 Parent(s): 41afe78

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +18 -23
README.md CHANGED
@@ -25,7 +25,7 @@ legal datasets for profit -- a particular concern in the border control setting.
25
 
26
  This dataset includes the unofficial full text of thousdands of court and tribunal decisions at the federal level.
27
  It can be used for legal analytics (e.g. identifying patterns in legal decision-making), to test ML and NLP tools
28
- on a bilingual (French and English) dataset of Canadian legal materials, and to pretrain language models.
29
 
30
  ### Languages
31
 
@@ -78,31 +78,21 @@ English and French versions of the same documents are put in the same split.
78
 
79
  ### Curation Rationale
80
 
81
- [More Information Needed]
 
 
 
82
 
83
  ### Source Data
84
 
85
  #### Initial Data Collection and Normalization
86
 
87
- [More Information Needed]
88
-
89
- #### Who are the source language producers?
90
-
91
- [More Information Needed]
92
-
93
- ### Annotations
94
-
95
- #### Annotation process
96
-
97
- [More Information Needed]
98
-
99
- #### Who are the annotators?
100
-
101
- [More Information Needed]
102
 
103
  ### Personal and Sensitive Information
104
 
105
- [More Information Needed]
 
106
 
107
  ## Considerations for Using the Data
108
 
@@ -122,16 +112,21 @@ English and French versions of the same documents are put in the same split.
122
 
123
  ### Dataset Curators
124
 
125
- [More Information Needed]
126
 
127
  ### Licensing Information
128
 
129
- [More Information Needed]
 
 
130
 
131
  ### Citation Information
132
 
133
- [More Information Needed]
 
 
134
 
135
- ### Contributions
136
 
137
- [More Information Needed]
 
 
25
 
26
  This dataset includes the unofficial full text of thousdands of court and tribunal decisions at the federal level.
27
  It can be used for legal analytics (e.g. identifying patterns in legal decision-making), to test ML and NLP tools
28
+ on a bilingual dataset of Canadian legal materials, and to pretrain language models.
29
 
30
  ### Languages
31
 
 
78
 
79
  ### Curation Rationale
80
 
81
+ The dataset includes all the Bulk Legal Data made publicly available by the Refugee Law Lab. The Lab has
82
+ focused on federal courts (e.g. Supreme Court of Canada, Federal Court of Appeal, Federal Court) as well as
83
+ federal administrative tribunals (e.g. Immigration and Refugee Board) because immigration and refugee law,
84
+ which is the main area of interest of the Lab, operates mostly at a federal level.
85
 
86
  ### Source Data
87
 
88
  #### Initial Data Collection and Normalization
89
 
90
+ Details (including links to github repos with code) are available via links for the Bulk Legal Data page
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91
 
92
  ### Personal and Sensitive Information
93
 
94
+ Documents may include personal and sensitive information. All documents have been published or released
95
+ publicly by the relevant court or tribunal.
96
 
97
  ## Considerations for Using the Data
98
 
 
112
 
113
  ### Dataset Curators
114
 
115
+ Sean Rehaag, Osgoode Hall Law School Professor & Director of the Refugee Law Lab
116
 
117
  ### Licensing Information
118
 
119
+ Creative Commons Attribution 4.0 International (CC BY 4.0)
120
+
121
+ NOTE: Users must also comply with upstream licensing for the SCC, FCA & FC data instances.
122
 
123
  ### Citation Information
124
 
125
+ Sean Rehaag, "Refugee Law Lab: Canadian Legal Data" (2023) online: Huggingface: <https://huggingface.co/datasets/refugee-law-lab/canadian-legal-data>.
126
+
127
+ ### Acknowledgements
128
 
129
+ This project draws on research supported by the Social Sciences and Humanities Research Council and the Law Foundation of Ontario.
130
 
131
+ The project was inspired in part by the excellent prior work by pile-of-law (Peter Henderson et al, "Pile of Law: Learning
132
+ Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset" (2022), online: arXiv: https://arxiv.org/abs/2207.00220)