Update parquet files

README.md

@@ -1,200 +0,0 @@
----
-annotations_creators:
-- machine-generated
-language:
-- de
-- fr
-- it
-language_creators:
-- expert-generated
-license: []
-multilinguality:
-- multilingual
-pretty_name: Legal Criticality Prediction
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-tags: []
-task_categories:
-- text-classification
----
-# Dataset Card for [legal criticality prediction]
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:**
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-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 i 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.
-
-### Supported Tasks and Leaderboards
-
-LCP can be used as text classification task
-
-### Languages
-
-Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
-German (80k), French (40k), Italian (10k)
-
-## Dataset Structure
-
-```
-{
-  "decision_id": ,
-  "language": de,
-  "year": 2018,
-  "chamber": ,
-  "court": ,
-  "canton": ,
-  "region": ,
-  "origin_chamber": ,
-  "origin_court": ,
-  "origin_canton": ,
-  "law_area": ,
-  "law_sub_area": ,
-  "bge_label": ,
-  "citation_label": ,
-  "facts": ,
-  "considerations": ,
-  "rulings": ,
-  "origin_facts": ,
-  "origin_considerations": ,
-}
-```
-
-### Data Fields
-
-```
-decision_id: (str) a unique identifier of the for the document
-language: (str) one of (de, fr, it)
-year: (int) the publication year
-chamber: (str) the chamber of the case
-court: (str) the court of the case
-canton: (str) the canton
-region: (str) the region of the case
-origin_chamber: (str) the chamber of the origin case
-origin_court: (str) the court of the origin case
-origin_canton:  (str) the canton of the origin case
-law_area: (str) the law area of the case
-law_sub_area:(str) the law sub area of the case
-bge_label: (str) critical or non-critical
-citation_label: (str) critical-1, critical-2, critical-3, critical-4, non-critical
-facts: (str) the facts of the case
-considerations: (str) the considerations of the case
-rulings: (str) the rulings of the case
-origin_facts: (str) the facts of the origin case
-origin_considerations: (str) the considerations of the origin case
-```
-
-### Data Instances
-[More Information Needed]
-### Data Fields
-[More Information Needed]
-### Data Splits
-
-The dataset was split date-stratisfied
-- Train: 2002-2015
-- Validation: 2016-2017
-- Test: 2018-2022
-
-| Language   | Subset     | Number of Documents (Training/Validation/Test) | 
-|------------|------------|--------------------------------------------|  
-| German     | **de**     |  /  /                          |
-| French     | **fr**     |  /  /                       |
-| Italian    | **it**     |  /  /                           |
-
-## Dataset Creation
-### Curation Rationale
-
-The dataset was curated by Stern et al. (2023).
-
-### Source Data
-#### Initial Data Collection and Normalization
-
-The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. 
-
-#### Who are the source language producers?
-
-The decisions are written by the judges and clerks in the language of the proceedings.
-
-### Annotations
-#### Annotation process
-
-bge_label:
-1. all bger_references in the bge header were extracted
-2. bger file_names are compared with the found references
-
-citation_label:
-1. count all citations for all bger cases and weight citations
-2. divide cited cases in four different classes, depending on amount of citations
-
-#### Who are the annotators?
-
-Ronja Stern annotated the citations.
-Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
-
-### Personal and Sensitive Information
-
-The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
-
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed]
-### Discussion of Biases
-[More Information Needed]
-### Other Known Limitations
-[More Information Needed]
-## Additional Information
-### Dataset Curators
-[More Information Needed]
-### Licensing Information
-
-We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
-© Swiss Federal Supreme Court, 2002-2022
-
-The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
-Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
-
-### Citation Information
-
-*Visu, Ronja, Joel*
-*Title: Blabliblablu*
-*Name of conference*
-```
-cit
-```
-
-### Contributions
-
-Thanks to [@Stern5497](https://github.com/stern5497) for adding this dataset.

data/test.jsonl.xz → full/swiss_criticality_prediction-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0c28da5a9f31dd774dcd1bf31b4b1a57f17b41a9832ff6bc205c0d4692167d57
-size 87979896
+oid sha256:584014372fd54c1cfd2e381c4a48ffa36f5cb8e4346e141719b1cad40bea4b86
+size 242060693

data/train.jsonl.xz → full/swiss_criticality_prediction-train-00000-of-00003.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9eccc885fb41f5121a2e98ad87e4128551159606d622b223d34271b25b1ffb01
-size 230247948
+oid sha256:0b1631aaaae8db85b5e7b957e5e7954e2f5bb59860b7eeac493c5be6f822f755
+size 249733456

data/validation.jsonl.xz → full/swiss_criticality_prediction-train-00001-of-00003.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dc7799bbd942bc69085c032271e124393968a61d76304e879325ee19ed2c7ff8
-size 36331956
+oid sha256:a97e5ce44d04f0069addb3a34aafa065f7fd7166614afcd95866a9ffd9b5fc55
+size 247125765

full/swiss_criticality_prediction-train-00002-of-00003.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0cadc46bf297deaabda2e95b03d67b128442b2c06e9aa41e268ac1924a99669c
+size 127054618

full/swiss_criticality_prediction-validation.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b39b87c1a033ca51209e07afab71c4dec1c64fa5bd13a5fe53d18ee8351d9f3a
+size 100420329

swiss_criticality_prediction.py

@@ -1,166 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Dataset for the Legal Criticality Prediction task."""
-
-import json
-import lzma
-import os
-
-import datasets
-try:
-    import lzma as xz
-except ImportError:
-    import pylzma as xz
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{huggingface:dataset,
-title = {A great new dataset},
-author={huggingface, Inc.
-},
-year={2020}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-This dataset contains Swiss federal court decisions for the legal criticality prediction task
-"""
-
-_URLS = {
-    "full": "https://huggingface.co/datasets/rcds/swiss_criticality_prediction/resolve/main/data",
-}
-
-
-class SwissCriticalityPrediction(datasets.GeneratorBasedBuilder):
-    """This dataset contains court decision for court view generation task."""
-
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="full", description="This part covers the whole dataset"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "full"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        if self.config.name == "full":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    # Todo check if these are all
-                    "decision_id": datasets.Value("string"),
-                    "language": datasets.Value("string"),
-                    "year": datasets.Value("int32"),
-                    "chamber": datasets.Value("string"),
-                    "region": datasets.Value("string"),
-                    "origin_chamber": datasets.Value("string"),
-                    "origin_court": datasets.Value("string"),
-                    "origin_canton": datasets.Value("string"),
-                    "law_area": datasets.Value("string"),
-                    "law_sub_area": datasets.Value("string"),
-                    "bge_label": datasets.Value("string"),
-                    "citation_label": datasets.Value("string"),
-                    "facts": datasets.Value("string"),
-                    "considerations": datasets.Value("string"),
-                    "rulings": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            # homepage=_HOMEPAGE,
-            # License for the dataset if available
-            # license=_LICENSE,
-            # Citation for the dataset
-            # citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        filepath_train = dl_manager.download(os.path.join(urls, "train.jsonl.xz"))
-        filepath_validation = dl_manager.download(os.path.join(urls, "validation.jsonl.xz"))
-        filepath_test = dl_manager.download(os.path.join(urls, "test.jsonl.xz"))
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepath_train,
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepath_validation,
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": filepath_test,
-                    "split": "test"
-                },
-            )
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        line_counter = 0
-        try:
-            with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
-                for id, line in enumerate(f):
-                    line_counter += 1
-                    if line:
-                        data = json.loads(line)
-                        if self.config.name == "full":
-                            yield id, {
-                                "decision_id": data["decision_id"],
-                                "language": data["language"],
-                                "year": data["year"],
-                                "chamber": data["chamber"],
-                                "region": data["region"],
-                                "origin_chamber": data["origin_chamber"],
-                                "origin_court": data["origin_court"],
-                                "origin_canton": data["origin_canton"],
-                                "law_area": data["law_area"],
-                                "law_sub_area": data["law_sub_area"],
-                                "citation_label": data["citation_label"],
-                                "bge_label": data["bge_label"],
-                                "facts": data["facts"],
-                                "considerations": data["considerations"],
-                                "rulings": data["rulings"],
-                            }
-        except lzma.LZMAError as e:
-            print(split, e)
-            if line_counter == 0:
-                raise e