Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
German
Size:
10K - 100K
License:
joelniklaus
commited on
Commit
•
45a0093
1
Parent(s):
5241c7a
added dataset files
Browse files- .gitattributes +4 -0
- README.md +260 -0
- convert_to_hf_dataset.py +138 -0
- meta.jsonl +3 -0
- test.jsonl +3 -0
- train.jsonl +3 -0
- validation.jsonl +3 -0
.gitattributes
CHANGED
@@ -35,3 +35,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
35 |
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
36 |
*.ogg filter=lfs diff=lfs merge=lfs -text
|
37 |
*.wav filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
35 |
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
36 |
*.ogg filter=lfs diff=lfs merge=lfs -text
|
37 |
*.wav filter=lfs diff=lfs merge=lfs -text
|
38 |
+
test.jsonl filter=lfs diff=lfs merge=lfs -text
|
39 |
+
train.jsonl filter=lfs diff=lfs merge=lfs -text
|
40 |
+
validation.jsonl filter=lfs diff=lfs merge=lfs -text
|
41 |
+
meta.jsonl filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,260 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
annotations_creators:
|
3 |
+
- expert-generated
|
4 |
+
- found
|
5 |
+
language_creators:
|
6 |
+
- found
|
7 |
+
languages:
|
8 |
+
- de
|
9 |
+
license:
|
10 |
+
- cc-by-4.0
|
11 |
+
multilinguality:
|
12 |
+
- monolingual
|
13 |
+
pretty_name: Annotated German Legal Decision Corpus
|
14 |
+
size_categories:
|
15 |
+
- 10K<n<100K
|
16 |
+
source_datasets:
|
17 |
+
- original
|
18 |
+
task_categories:
|
19 |
+
- text-classification
|
20 |
+
task_ids:
|
21 |
+
- multi-class-classification
|
22 |
+
---
|
23 |
+
|
24 |
+
# Dataset Card for Annotated German Legal Decision Corpus
|
25 |
+
|
26 |
+
## Table of Contents
|
27 |
+
|
28 |
+
- [Table of Contents](#table-of-contents)
|
29 |
+
- [Dataset Description](#dataset-description)
|
30 |
+
- [Dataset Summary](#dataset-summary)
|
31 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
32 |
+
- [Languages](#languages)
|
33 |
+
- [Dataset Structure](#dataset-structure)
|
34 |
+
- [Data Instances](#data-instances)
|
35 |
+
- [Data Fields](#data-fields)
|
36 |
+
- [Data Splits](#data-splits)
|
37 |
+
- [Dataset Creation](#dataset-creation)
|
38 |
+
- [Curation Rationale](#curation-rationale)
|
39 |
+
- [Source Data](#source-data)
|
40 |
+
- [Annotations](#annotations)
|
41 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
42 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
43 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
44 |
+
- [Discussion of Biases](#discussion-of-biases)
|
45 |
+
- [Other Known Limitations](#other-known-limitations)
|
46 |
+
- [Additional Information](#additional-information)
|
47 |
+
- [Dataset Curators](#dataset-curators)
|
48 |
+
- [Licensing Information](#licensing-information)
|
49 |
+
- [Citation Information](#citation-information)
|
50 |
+
- [Contributions](#contributions)
|
51 |
+
|
52 |
+
## Dataset Description
|
53 |
+
|
54 |
+
- **Homepage:**
|
55 |
+
- **Repository:** https://zenodo.org/record/3936490#.X1ed7ovgomK
|
56 |
+
- **Paper:** Urchs., S., Mitrović., J., & Granitzer., M. (2021). Design and Implementation of German Legal Decision
|
57 |
+
Corpora. Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
|
58 |
+
515–521. https://doi.org/10.5220/0010187305150521
|
59 |
+
- **Leaderboard:**
|
60 |
+
- **Point of Contact:** [Joel Niklaus](joel.niklaus.2@bfh.ch)
|
61 |
+
|
62 |
+
### Dataset Summary
|
63 |
+
|
64 |
+
This dataset consists of 200 randomly chosen judgments. In these judgments a legal expert annotated the components
|
65 |
+
conclusion, definition and subsumption of the German legal writing style Urteilsstil.
|
66 |
+
|
67 |
+
*"Overall 25,075 sentences are annotated. 5% (1,202) of these sentences are marked as conclusion, 21% (5,328) as
|
68 |
+
definition, 53% (13,322) are marked as subsumption and the remaining 21% (6,481) as other. The length of judgments in
|
69 |
+
sentences ranges from 38 to 862 sentences. The median of judgments have 97 sentences, the length of most judgments is on
|
70 |
+
the shorter side."* (Urchs. et al., 2021)
|
71 |
+
|
72 |
+
*"Judgments from 22 of the 131 courts are selected for the corpus. Most judgments originate from the VG Augsburg (59 /
|
73 |
+
30%) followed by the VG Ansbach (39 / 20%) and LSG Munich (33 / 17%)."* (Urchs. et al., 2021)
|
74 |
+
|
75 |
+
*"29% (58) of all selected judgments are issued in the year 2016, followed by 22% (44) from the year 2017 and 21% (41)
|
76 |
+
issued in the year 2015. [...] The percentages of selected judgments and decisions issued in 2018 and 2019 are roughly
|
77 |
+
the same. No judgments from 2020 are selected."* (Urchs. et al., 2021)
|
78 |
+
|
79 |
+
### Supported Tasks and Leaderboards
|
80 |
+
|
81 |
+
The dataset can be used for multi-class text classification tasks, more specifically, for argument mining.
|
82 |
+
|
83 |
+
### Languages
|
84 |
+
|
85 |
+
The language in the dataset is German as it is used in Bavarian courts in Germany.
|
86 |
+
|
87 |
+
## Dataset Structure
|
88 |
+
|
89 |
+
### Data Instances
|
90 |
+
|
91 |
+
Each sentence is saved as a json object on a line in one of the three files `train.jsonl`, `validation.jsonl`
|
92 |
+
or `test.jsonl`. The file `meta.jsonl` contains meta information for each court. The `file_number` is present in all
|
93 |
+
files for identification. Each sentence of the court decision was categorized according to its function.
|
94 |
+
|
95 |
+
### Data Fields
|
96 |
+
|
97 |
+
The file `meta.jsonl` contains for each row the following fields:
|
98 |
+
|
99 |
+
- `meta_title`: Title provided by the website, it is used for saving the decision
|
100 |
+
- `court`: Issuing court
|
101 |
+
- `decision_style`: Style of the decision; the corpus contains either *Urteil* (='judgment') or *Endurteil* (
|
102 |
+
='end-judgment')
|
103 |
+
- `date`: Date when the decision was issued by the court
|
104 |
+
- `file_number`: Identification number used for this decision by the court
|
105 |
+
- `title`: Title provided by the court
|
106 |
+
- `norm_chains`: Norms related to the decision
|
107 |
+
- `decision_guidelines`: Short summary of the decision
|
108 |
+
- `keywords`: Keywords associated with the decision
|
109 |
+
- `lower_court`: Court that decided on the decision before
|
110 |
+
- `additional_information`: Additional Information
|
111 |
+
- `decision_reference`: References to the location of the decision in beck-online
|
112 |
+
- `tenor`: Designation of the legal consequence ordered by the court (list of paragraphs)
|
113 |
+
- `legal_facts`: Facts that form the base for the decision (list of paragraphs)
|
114 |
+
|
115 |
+
The files `train.jsonl`, `validation.jsonl` and `test.jsonl` contain the following fields:
|
116 |
+
|
117 |
+
- `file_number`: Identification number for linkage with the file `meta.jsonl`
|
118 |
+
- `input_sentence`: The sentence to be classified
|
119 |
+
- `label`: In depth explanation of the court decision. Each sentence is assigned to one of the major components of
|
120 |
+
German *Urteilsstil* (Urchs. et al., 2021) (list of paragraphs, each paragraph containing list of sentences, each
|
121 |
+
sentence annotated with one of the following four labels):
|
122 |
+
- `conclusion`: Overall result
|
123 |
+
- `definition`: Abstract legal facts and consequences
|
124 |
+
- `subsumption`: Determination sentence / Concrete facts
|
125 |
+
- `other`: Anything else
|
126 |
+
- `context_before`: Context in the same paragraph before the input_sentence
|
127 |
+
- `context_after`: Context in the same paragraph after the input_sentence
|
128 |
+
|
129 |
+
### Data Splits
|
130 |
+
|
131 |
+
No split provided in the original release.
|
132 |
+
|
133 |
+
Splits created by Joel Niklaus. We randomly split the dataset into 80% (160 decisions, 19271 sentences) train, 10%
|
134 |
+
validation (20 decisions, 2726 sentences) and 10% test (20 decisions, 3078 sentences). We made sure, that a decision
|
135 |
+
only occurs in one split and is not dispersed over multiple splits.
|
136 |
+
|
137 |
+
Label Distribution
|
138 |
+
|
139 |
+
| label | train | validation | test |
|
140 |
+
|:---------------|-----------:|-------------:|----------:|
|
141 |
+
| conclusion | 975 | 115 | 112 |
|
142 |
+
| definition | 4105 | 614 | 609 |
|
143 |
+
| subsumption | 10034 | 1486 | 1802 |
|
144 |
+
| other | 4157 | 511 | 555 |
|
145 |
+
| total | **19271** | **2726** | **3078** |
|
146 |
+
|
147 |
+
## Dataset Creation
|
148 |
+
|
149 |
+
### Curation Rationale
|
150 |
+
|
151 |
+
Creating a publicly available German legal text corpus consisting of judgments that have been annotated by a legal
|
152 |
+
expert. The annotated components consist of *conclusion*, *definition* and *subsumption* of the German legal writing
|
153 |
+
style *Urteilsstil*.
|
154 |
+
|
155 |
+
### Source Data
|
156 |
+
|
157 |
+
#### Initial Data Collection and Normalization
|
158 |
+
|
159 |
+
*“The decision corpus is a collection of the decisions published on the website www.gesetze-bayern.de. At the time of
|
160 |
+
the crawling the website offered 32,748 decisions of 131 Bavarian courts, dating back to 2015. The decisions are
|
161 |
+
provided from the Bavarian state after the courts agreed to a publication. All decisions are processed by the publisher
|
162 |
+
C.H.BECK, commissioned by the Bavarian state. This processing includes anonymisation, key-wording, and adding of
|
163 |
+
editorial guidelines to the decisions.”* (Urchs. et al., 2021)
|
164 |
+
|
165 |
+
#### Who are the source language producers?
|
166 |
+
|
167 |
+
German courts from Bavaria
|
168 |
+
|
169 |
+
### Annotations
|
170 |
+
|
171 |
+
#### Annotation process
|
172 |
+
|
173 |
+
*“As stated above, the judgment corpus consist of 200 randomly chosen judgments that are annotated by a legal expert,
|
174 |
+
who holds a first legal state exam. Due to financial, staff and time reasons the presented iteration of the corpus was
|
175 |
+
only annotated by a single expert. In a future version several other experts will annotate the corpus and the
|
176 |
+
inter-annotator agreement will be calculated.”* (Urchs. et al., 2021)
|
177 |
+
|
178 |
+
#### Who are the annotators?
|
179 |
+
|
180 |
+
A legal expert, who holds a first legal state exam.
|
181 |
+
|
182 |
+
### Personal and Sensitive Information
|
183 |
+
|
184 |
+
*"All decisions are processed by the publisher C.H.BECK, commissioned by the Bavarian state. This processing includes **
|
185 |
+
anonymisation**, key-wording, and adding of editorial guidelines to the decisions.”* (Urchs. et al., 2021)
|
186 |
+
|
187 |
+
## Considerations for Using the Data
|
188 |
+
|
189 |
+
### Social Impact of Dataset
|
190 |
+
|
191 |
+
[More Information Needed]
|
192 |
+
|
193 |
+
### Discussion of Biases
|
194 |
+
|
195 |
+
[More Information Needed]
|
196 |
+
|
197 |
+
### Other Known Limitations
|
198 |
+
|
199 |
+
The SoMaJo Sentence Splitter has been used. Upon manual inspection of the dataset, we could see that the sentence
|
200 |
+
splitter had poor accuracy in some cases (see ```analyze_dataset()``` in ```convert_to_hf_dataset.py```). When creating
|
201 |
+
the splits, we thought about merging small sentences with their neighbors or removing them all together. However, since
|
202 |
+
we could not find an straightforward way to do this, we decided to leave the dataset content untouched.
|
203 |
+
|
204 |
+
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
|
205 |
+
Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
|
206 |
+
consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
|
207 |
+
dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
|
208 |
+
differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
|
209 |
+
have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
|
210 |
+
original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
|
211 |
+
the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
|
212 |
+
|
213 |
+
## Additional Information
|
214 |
+
|
215 |
+
### Dataset Curators
|
216 |
+
|
217 |
+
The names of the original dataset curators and creators can be found in references given below, in the section *Citation
|
218 |
+
Information*. Additional changes were made by Joel Niklaus ([Email](joel.niklaus.2@bfh.ch)
|
219 |
+
; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](veton.matoshi@bfh.ch)
|
220 |
+
; [Github](https://github.com/kapllan)).
|
221 |
+
|
222 |
+
### Licensing Information
|
223 |
+
|
224 |
+
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
|
225 |
+
|
226 |
+
### Citation Information
|
227 |
+
|
228 |
+
```
|
229 |
+
@dataset{urchs_stefanie_2020_3936490,
|
230 |
+
author = {Urchs, Stefanie and
|
231 |
+
Mitrović, Jelena},
|
232 |
+
title = {{German legal jugements annotated with judement
|
233 |
+
style components}},
|
234 |
+
month = jul,
|
235 |
+
year = 2020,
|
236 |
+
publisher = {Zenodo},
|
237 |
+
doi = {10.5281/zenodo.3936490},
|
238 |
+
url = {https://doi.org/10.5281/zenodo.3936490}
|
239 |
+
}
|
240 |
+
```
|
241 |
+
|
242 |
+
```
|
243 |
+
@conference{icaart21,
|
244 |
+
author = {Urchs., Stefanie and Mitrovi{\'{c}}., Jelena and Granitzer., Michael},
|
245 |
+
booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
|
246 |
+
doi = {10.5220/0010187305150521},
|
247 |
+
isbn = {978-989-758-484-8},
|
248 |
+
issn = {2184-433X},
|
249 |
+
organization = {INSTICC},
|
250 |
+
pages = {515--521},
|
251 |
+
publisher = {SciTePress},
|
252 |
+
title = {{Design and Implementation of German Legal Decision Corpora}},
|
253 |
+
year = {2021}
|
254 |
+
}
|
255 |
+
```
|
256 |
+
|
257 |
+
### Contributions
|
258 |
+
|
259 |
+
Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this
|
260 |
+
dataset.
|
convert_to_hf_dataset.py
ADDED
@@ -0,0 +1,138 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from glob import glob
|
2 |
+
from pathlib import Path
|
3 |
+
|
4 |
+
import json
|
5 |
+
import numpy as np
|
6 |
+
import pandas as pd
|
7 |
+
|
8 |
+
"""
|
9 |
+
Dataset url: https://zenodo.org/record/3936490/files/annotated_corpus.zip?download=1
|
10 |
+
Paper url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044329/
|
11 |
+
|
12 |
+
There are no splits available ==> Make random split ourselves
|
13 |
+
|
14 |
+
"""
|
15 |
+
|
16 |
+
pd.set_option('display.max_colwidth', None)
|
17 |
+
pd.set_option('display.max_columns', None)
|
18 |
+
|
19 |
+
|
20 |
+
def analyze_dataset(df, num_characters_for_short_sentence=25):
|
21 |
+
short_sentence = False
|
22 |
+
counter = 0
|
23 |
+
same_label_counter = 0
|
24 |
+
other_number = 0
|
25 |
+
num_one_paragraph_len = 0
|
26 |
+
for i in range(200):
|
27 |
+
for paragraph in df.iloc[i].decision_reasons:
|
28 |
+
for sentence in paragraph:
|
29 |
+
if short_sentence:
|
30 |
+
print("previous sentence was short: ", short_sentence)
|
31 |
+
print("current sentence label: ", sentence[1])
|
32 |
+
print("current paragraph: ", paragraph)
|
33 |
+
if sentence[1] == short_sentence[1]:
|
34 |
+
same_label_counter += 1
|
35 |
+
|
36 |
+
if len(sentence[0]) < num_characters_for_short_sentence:
|
37 |
+
counter += 1
|
38 |
+
short_sentence = sentence
|
39 |
+
print()
|
40 |
+
print("short sentence: ", sentence)
|
41 |
+
print("short paragraph: ", paragraph)
|
42 |
+
if sentence[1] == 'other':
|
43 |
+
other_number += 1
|
44 |
+
if len(paragraph) == 1:
|
45 |
+
num_one_paragraph_len += 1
|
46 |
+
else:
|
47 |
+
short_sentence = False
|
48 |
+
|
49 |
+
print("num short sentences: ", counter)
|
50 |
+
print("num short sentences containing the same label as the next one: ", same_label_counter)
|
51 |
+
print("num short sentences containing 'other' as label: ", other_number)
|
52 |
+
print("num short sentences where the paragraph contains only this one short sentence: ", num_one_paragraph_len)
|
53 |
+
# ==> the label is only the same in very few cases
|
54 |
+
# ==> the label is 'other' in the majority of cases, when it is not: it seems to be mislabeled
|
55 |
+
# ==> think about removing them entirely
|
56 |
+
# ==> we opted for not interfering in the content of the dataset
|
57 |
+
|
58 |
+
|
59 |
+
# create a summary jsonl file
|
60 |
+
dataset_filename = "dataset.jsonl"
|
61 |
+
if not Path(dataset_filename).exists():
|
62 |
+
with open(dataset_filename, "a") as dataset_file:
|
63 |
+
for filename in glob("annotated_corpus/*.json"):
|
64 |
+
# we need to do this charade, because some jsons are formatted differently than others
|
65 |
+
json_text = Path(filename).read_text()
|
66 |
+
json_obj = json.loads(json_text)
|
67 |
+
# make it less nested so that it is easier to read as df
|
68 |
+
new_dict = {}
|
69 |
+
new_dict.update(json_obj["meta"])
|
70 |
+
new_dict.update(json_obj["decision_text"])
|
71 |
+
dataset_file.write(json.dumps(new_dict) + "\n")
|
72 |
+
else:
|
73 |
+
print(f"{dataset_filename} already exists. Please delete it to re-aggregate it.")
|
74 |
+
|
75 |
+
df = pd.read_json(dataset_filename, lines=True)
|
76 |
+
|
77 |
+
# Do splits before expanding the df so that entire decisions are in the splits and not samples from one decision are spread across splits
|
78 |
+
# perform random split 80% train (160 decisions), 10% validation (20 decisions), 10% test (20 decisions)
|
79 |
+
train, validation, test = np.split(df.sample(frac=1, random_state=42), [int(.8 * len(df)), int(.9 * len(df))])
|
80 |
+
|
81 |
+
|
82 |
+
def expand_df(df):
|
83 |
+
"""
|
84 |
+
Expand the df so that each sentence has its own row and is its own sample
|
85 |
+
:param df:
|
86 |
+
:return:
|
87 |
+
"""
|
88 |
+
rows = []
|
89 |
+
for index, row in df.iterrows():
|
90 |
+
for paragraph in row.decision_reasons:
|
91 |
+
for sent_idx, sentence in enumerate(paragraph):
|
92 |
+
new_row = {'file_number': row['file_number'], 'input_sentence': sentence[0], 'label': sentence[1]}
|
93 |
+
# Discussion with lawyer yielded, that the paragraph as context is enough
|
94 |
+
# take the sentences before
|
95 |
+
new_row['context_before'] = paragraph[:sent_idx]
|
96 |
+
# take the remaining sentences afterwards
|
97 |
+
new_row['context_after'] = paragraph[sent_idx + 1:]
|
98 |
+
rows.append(new_row)
|
99 |
+
|
100 |
+
return pd.DataFrame.from_records(rows)
|
101 |
+
|
102 |
+
|
103 |
+
train = expand_df(train)
|
104 |
+
validation = expand_df(validation)
|
105 |
+
test = expand_df(test)
|
106 |
+
|
107 |
+
# Num samples for each split: train (19271), validation (2726), test (3078)
|
108 |
+
print(len(train.index), len(validation.index), len(test.index))
|
109 |
+
|
110 |
+
# save to jsonl files for huggingface
|
111 |
+
train.to_json("train.jsonl", lines=True, orient="records")
|
112 |
+
validation.to_json("validation.jsonl", lines=True, orient="records")
|
113 |
+
test.to_json("test.jsonl", lines=True, orient="records")
|
114 |
+
|
115 |
+
# save main df with meta information to file
|
116 |
+
# link to splits is given via file_number
|
117 |
+
df = df.drop(['decision_reasons'], axis=1)
|
118 |
+
df.to_json("meta.jsonl", lines=True, orient="records")
|
119 |
+
|
120 |
+
|
121 |
+
def print_split_table_single_label(train, validation, test, label_name):
|
122 |
+
train_counts = train[label_name].value_counts().to_frame().rename(columns={label_name: "train"})
|
123 |
+
validation_counts = validation[label_name].value_counts().to_frame().rename(columns={label_name: "validation"})
|
124 |
+
test_counts = test[label_name].value_counts().to_frame().rename(columns={label_name: "test"})
|
125 |
+
|
126 |
+
table = train_counts.join(validation_counts)
|
127 |
+
table = table.join(test_counts)
|
128 |
+
table[label_name] = table.index
|
129 |
+
total_row = {label_name: "total",
|
130 |
+
"train": len(train.index),
|
131 |
+
"validation": len(validation.index),
|
132 |
+
"test": len(test.index)}
|
133 |
+
table = table.append(total_row, ignore_index=True)
|
134 |
+
table = table[[label_name, "train", "validation", "test"]] # reorder columns
|
135 |
+
print(table.to_markdown(index=False))
|
136 |
+
|
137 |
+
|
138 |
+
print_split_table_single_label(train, validation, test, "label")
|
meta.jsonl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:83f23fed08311a81117bb12f60be1676313f7448ef297ef5fd34cf4641345e82
|
3 |
+
size 2828194
|
test.jsonl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:19595a310f39fd1daa89f834930923ed19e3b01d04806d189c382eca70ecbb08
|
3 |
+
size 4263762
|
train.jsonl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ed22caee3516ab681121b5896e74de3aceb771d3148587675cb04d4a9d4162d0
|
3 |
+
size 26669050
|
validation.jsonl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:cf7c5165d3c7d7a42d5d10be6a35391ffd72781dd4008bc6fa15a9daa983d850
|
3 |
+
size 3563814
|