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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
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-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
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-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
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-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: cc-by-nc-sa-4.0
----
-
-# ECHR Cases
-
-The original data from [Chalkidis et al.](https://arxiv.org/abs/1906.02059), sourced from [archive.org](https://archive.org/details/ECHR-ACL2019).
-
-## Preprocessing
-
-* Order is shuffled
-* Fact numbers preceeding each fact are removed (using the python regex `^[0-9]+\. `), as some cases didn't have fact numbers to begin with
-* Everything else is the same

data_anon/test.jsonl → anon/echr-test.parquet

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data/train.jsonl

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data_anon/train.jsonl

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echr.py

@@ -1,146 +0,0 @@
-import datasets
-import json
-import os
-from datasets import Value, Sequence
-
-_CITATION = """\
-@inproceedings{chalkidis-etal-2019-neural,
-    title = "Neural Legal Judgment Prediction in {E}nglish",
-    author = "Chalkidis, Ilias  and
-      Androutsopoulos, Ion  and
-      Aletras, Nikolaos",
-    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2019",
-    address = "Florence, Italy",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/P19-1424",
-    doi = "10.18653/v1/P19-1424",
-    pages = "4317--4323",
-}
-"""
-
-_HOMEPAGE = "https://archive.org/details/ECHR-ACL2019"
-_DESCRIPTION = """\
-The ECHR Cases dataset is designed for experimentation of neural judgment prediction, as in the original 2019 ACL paper "Neural Legal Judgment Prediction in English".
-"""
-
-
-ARTICLES = {
-    "2": "Right to life",
-    "3": "Prohibition of torture",
-    "4": "Prohibition of slavery and forced labour",
-    "5": "Right to liberty and security",
-    "6": "Right to a fair trial",
-    "7": "No punishment without law",
-    "8": "Right to respect for private and family life",
-    "9": "Freedom of thought, conscience and religion",
-    "10": "Freedom of expression",
-    "11": "Freedom of assembly and association",
-    "12": "Right to marry",
-    "13": "Right to an effective remedy",
-    "14": "Prohibition of discrimination",
-    "15": "Derogation in time of emergency",
-    "16": "Restrictions on political activity of aliens",
-    "17": "Prohibition of abuse of rights",
-    "18": "Limitation on use of restrictions on rights",
-    "34": "Individual applications",
-    "38": "Examination of the case",
-    "39": "Friendly settlements",
-    "46": "Binding force and execution of judgments",
-    "P1-1": "Protection of property",
-    "P1-2": "Right to education",
-    "P1-3": "Right to free elections",
-    "P3-1": "Right to free elections",
-    "P4-1": "Prohibition of imprisonment for debt",
-    "P4-2": "Freedom of movement",
-    "P4-3": "Prohibition of expulsion of nationals",
-    "P4-4": "Prohibition of collective expulsion of aliens",
-    "P6-1": "Abolition of the death penalty",
-    "P6-2": "Death penalty in time of war",
-    "P6-3": "Prohibition of derogations",
-    "P7-1": "Procedural safeguards relating to expulsion of aliens",
-    "P7-2": "Right of appeal in criminal matters",
-    "P7-3": "Compensation for wrongful conviction",
-    "P7-4": "Right not to be tried or punished twice",
-    "P7-5": "Equality between spouses",
-    "P12-1": "General prohibition of discrimination",
-    "P13-1": "Abolition of the death penalty",
-    "P13-2": "Prohibition of derogations",
-    "P13-3": "Prohibition of reservations",
-}
-
-
-class Echr(datasets.GeneratorBasedBuilder):
-    """ECHR dataset."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="non-anon", data_dir="data"),
-        datasets.BuilderConfig(name="anon", data_dir="data_anon"),
-    ]
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "itemid": Value(dtype="string"),
-                "languageisocode": Value(dtype="string"),
-                "respondent": Value(dtype="string"),
-                "branch": Value(dtype="string"),
-                "date": Value(dtype="int64"),
-                "docname": Value(dtype="string"),
-                "importance": Value(dtype="int64"),
-                "conclusion": Value(dtype="string"),
-                "judges": Value(dtype="string"),
-                "text": Sequence(feature=Value(dtype="string")),
-                "violated_articles": Sequence(feature=Value(dtype="string")),
-                "violated_paragraphs": Sequence(feature=Value(dtype="string")),
-                "violated_bulletpoints": Sequence(feature=Value(dtype="string")),
-                "non_violated_articles": Sequence(feature=Value(dtype="string")),
-                "non_violated_paragraphs": Sequence(feature=Value(dtype="string")),
-                "non_violated_bulletpoints": Sequence(feature=Value(dtype="string")),
-                "violated": Value(dtype="bool"),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            features=features,
-            homepage=_HOMEPAGE,
-            description=_DESCRIPTION,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        path_prefix = self.config.data_dir
-        data_dir = dl_manager.download([os.path.join(path_prefix, f"{f}.jsonl") for f in ["train", "test", "dev"]])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir[0],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir[1],
-                    "split": "test",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir[2],
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                yield id_, data

jsonlify.py

@@ -1,12 +0,0 @@
-# Script used to put files into jsonl format (original downloaded from web archive link, in readme)
-import glob
-
-for folder in glob.glob("*/"):
-    a = []
-    for file in glob.glob(f"./{folder}*.json"):
-        contents = open(file, "r").read()
-        a.append(contents)
-
-    with open(f"{folder[:-1]}.jsonl", "w+") as f:
-        f.write("\n".join(a))
-    a.clear()

data/test.jsonl → non-anon/echr-test.parquet

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+size 17060492

non-anon/echr-train.parquet

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non-anon/echr-validation.parquet

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process.ipynb

@@ -1,134 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_dev = pd.read_json(\"EN_dev_Anon.jsonl\", lines=True)\n",
-    "_test = pd.read_json(\"EN_test_Anon.jsonl\", lines=True)\n",
-    "_train = pd.read_json(\"EN_train_Anon.jsonl\", lines=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def process(df):\n",
-    "    df = df.copy()\n",
-    "    df.columns = df.columns.str.lower()\n",
-    "    df[\"violated\"] = df.violated_articles.str.len() != 0\n",
-    "    return df\n",
-    "\n",
-    "dev = process(_dev)\n",
-    "test = process(_test)\n",
-    "train = process(_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "d = {k: list(v) for k, v in train.groupby(\"violated\").indices.items()}\n",
-    "dist = dict(dev.violated.value_counts().items())\n",
-    "d_train = {k: v[0:dist[k]] for k, v in d.items()}\n",
-    "d_remaining = {k: v[dist[k]:] for k, v in d.items()}\n",
-    "\n",
-    "new_rows = []\n",
-    "for i in range(len(dev[\"violated\"])):\n",
-    "    label = i % 2 == 0\n",
-    "    new_rows.append(train.iloc[d_train[label].pop()])\n",
-    "\n",
-    "new_train = pd.concat([pd.DataFrame(new_rows), pd.DataFrame(train.iloc[i] for l in d_remaining.values() for i in l).sample(frac=1, random_state=42)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_train.to_json(\"train.jsonl\", lines=True, orient=\"records\")\n",
-    "test.to_json(\"test.jsonl\", lines=True, orient=\"records\")\n",
-    "dev.to_json(\"dev.jsonl\", lines=True, orient=\"records\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train = pd.read_json(\"data/train.jsonl\", lines=True, orient=\"records\")\n",
-    "test = pd.read_json(\"data/test.jsonl\", lines=True, orient=\"records\")\n",
-    "dev = pd.read_json(\"data/dev.jsonl\", lines=True, orient=\"records\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "\n",
-    "number = re.compile(\"^([0-9]+|CARDINAL)\\s?\\. \")\n",
-    "train[\"text\"] = train[\"text\"].map(lambda r: [re.sub(number, \"\", line) for line in r])\n",
-    "test[\"text\"] = test[\"text\"].map(lambda r: [re.sub(number, \"\", line) for line in r])\n",
-    "dev[\"text\"] = dev[\"text\"].map(lambda r: [re.sub(number, \"\", line) for line in r])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train.to_json(\"data/train.jsonl\", lines=True, orient=\"records\")\n",
-    "test.to_json(\"data/test.jsonl\", lines=True, orient=\"records\")\n",
-    "dev.to_json(\"data/dev.jsonl\", lines=True, orient=\"records\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

tests.ipynb

@@ -1,112 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using custom data configuration jonathanli--echr-8f6bd4e68e0f7714\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading and preparing dataset json/jonathanli--echr to /home/jonathan/.cache/huggingface/datasets/jonathanli___json/jonathanli--echr-8f6bd4e68e0f7714/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Downloading data: 100%|██████████| 106M/106M [00:01<00:00, 66.6MB/s]\n",
-      "Downloading data: 100%|██████████| 37.9M/37.9M [00:00<00:00, 56.8MB/s]\n",
-      "Downloading data: 100%|██████████| 21.9M/21.9M [00:00<00:00, 42.3MB/s]\n",
-      "Downloading data files: 100%|██████████| 3/3 [00:05<00:00,  1.98s/it]\n",
-      "Extracting data files: 100%|██████████| 3/3 [00:00<00:00, 2462.41it/s]\n",
-      "                                \r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Dataset json downloaded and prepared to /home/jonathan/.cache/huggingface/datasets/jonathanli___json/jonathanli--echr-8f6bd4e68e0f7714/0.0.0/a3e658c4731e59120d44081ac10bf85dc7e1388126b92338344ce9661907f253. Subsequent calls will reuse this data.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 3/3 [00:00<00:00, 189.83it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import datasets\n",
-    "echr = datasets.load_dataset(\"jonathanli/echr\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5. The applicant was born in 1961 and is currently detained in İzmit Ftype prison.\n",
-      "6. On 6 May 2000 the applicant was arrested and taken into police custody on suspicion of membership of a criminal profit-making organisation and carrying out illegal activities on its behalf.\n",
-      "7. On 8 May 2000 the investigating judge at the İstanbul State Security Court ordered the applicant’s pre-trial detention.\n",
-      "8. On 19 June 2000 a bill of indictment was filed against the applicant and four other persons with the İstanbul State Security Court, accusing them of forming a criminal profit-making organisation and of being involved in incidents of murder, extortion and fraud.\n",
-      "9. On 30 January 2003 the first-instance court acquitted the applicant and the other accused of the former charge on the ground that the mental elements of the crime had not been established on their parts. It followed that it lacked jurisdiction to examine the other charges brought against them and transferred the proceedings to the Kartal Assize Court.\n",
-      "10. On 20 December 2004 the Court of Cassation quashed the judgment of the first-instance court, noting that the latter had erroneously acquitted the applicant and his co-accused of the charge concerned. In its decision, the court held that all components of forming a criminal profit-making organisation had been sufficiently established against the accused.\n",
-      "11. Subsequently, the case was remitted to the first-instance court.\n",
-      "12. Following the abolition of the State Security Courts by Law no. 5190, the İstanbul Assize Court resumed the criminal proceedings.\n",
-      "13. During the proceedings, the İstanbul Assize Court reviewed the lawfulness of the applicant’s continued detention regularly at the end of each hearing or, at the latest, every thirty days, of its own motion, without holding any oral hearing.\n",
-      "14. At the hearing on 3 March 2010 the İstanbul Assize Court decided, once more, to extend the applicant’s continued detention on account of the reasonable grounds of suspicion that he had committed the offences with which he was charged, and the state of the evidence in the case file.\n",
-      "15. On 5 January 2011 having regard to the period he had spent in detention, the İstanbul Assize Court released the applicant.\n",
-      "16. On the basis of the range of evidence in the case file, on 6 December 2011 the İstanbul Assize Court convicted the applicant of a number of crimes; including forming a criminal profit-making organisation, murder, abduction and extortion. Subsequently, the court sentenced the applicant to life imprisonment.\n",
-      "17. According to the information in the case file, the applicant lodged an appeal with the Court of Cassation, before which the proceedings are currently pending.\n",
-      "18. The relevant sections of the Turkish Code of Criminal Procedure (Law no.5271) can be found in the judgment of Araz v. Turkey (no. 44319/04, §§ 15-16, 20 May 2010).\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\n\".join(echr[\"train\"][110][\"text\"]))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.5 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.5"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}