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README.md

@@ -1,349 +0,0 @@
----
-annotations_creators:
-- other
-language_creators:
-- found
-language:
-- multilingual
-- bg
-- cs
-- da
-- de
-- el
-- en
-- es
-- et
-- fi
-- fr
-- ga
-- hu
-- it
-- lt
-- lv
-- mt
-- nl
-- pt
-- ro
-- sk
-- sv
-license:
-- cc-by-4.0
-multilinguality:
-- multilingual
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
-pretty_name: Spanish Datasets for Sensitive Entity Detection in the Legal Domain
-tags:
-- named-entity-recognition-and-classification
----
-
-# Dataset Card for Multilingual European Datasets for Sensitive Entity Detection in the Legal Domain
-
-## 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:** [Spanish](https://elrc-share.eu/repository/browse/mapa-anonymization-package-spanish/b550e1a88a8311ec9c1a00155d026706687917f92f64482587c6382175dffd76/), [Most](https://elrc-share.eu/repository/search/?q=mfsp:3222a6048a8811ec9c1a00155d0267067eb521077db54d6684fb14ce8491a391), [German, Portuguese, Slovak, Slovenian, Swedish](https://elrc-share.eu/repository/search/?q=mfsp:833df1248a8811ec9c1a00155d0267067685dcdb77064822b51cc16ab7b81a36)
-- **Paper:** de Gibert Bonet, O., García Pablos, A., Cuadros, M., & Melero, M. (2022). Spanish Datasets for Sensitive
-  Entity Detection in the Legal Domain. Proceedings of the Language Resources and Evaluation Conference, June,
-  3751–3760. http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.400.pdf
-- **Leaderboard:**
-- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
-
-### Dataset Summary
-
-The dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court
-decisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated
-for named entities following the guidelines of the [MAPA project]( https://mapa-project.eu/) which foresees two
-annotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.
-
-### Supported Tasks and Leaderboards
-
-The dataset supports the task of Named Entity Recognition and Classification (NERC).
-
-### Languages
-
-The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv
-
-## Dataset Structure
-
-### Data Instances
-
-The file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are
-non-overlapping.
-
-### Data Fields
-
-For the annotation the documents have been split into sentences. The annotations has been done on the token level. 
-The files contain the following data fields
-
-- `language`: language of the sentence
-- `type`:  The document type of the sentence. Currently, only EUR-LEX is supported.
-- `file_name`: The document file name the sentence belongs to.
-- `sentence_number`: The number of the sentence inside its document.
-- `tokens`: The list of tokens in the sentence.
-- `coarse_grained`: The coarse-grained annotations for each token
-- `fine_grained`: The fine-grained annotations for each token
-
-
-As previously stated, the annotation has been conducted on a global and a more fine-grained level.
-
-The tagset used for the global and the fine-grained named entities is the following:
-
-- Address
-    - Building
-    - City
-    - Country
-    - Place
-    - Postcode
-    - Street
-    - Territory
-- Amount
-    - Unit
-    - Value
-- Date
-    - Year
-    - Standard Abbreviation
-    - Month
-    - Day of the Week
-    - Day
-    - Calender Event
-- Person
-    - Age
-    - Email
-    - Ethnic Category
-    - Family Name
-    - Financial
-    - Given Name – Female
-    - Given Name – Male
-    - Health Insurance Number
-    - ID Document Number
-    - Initial Name
-    - Marital Status
-    - Medical Record Number
-    - Nationality
-    - Profession
-    - Role
-    - Social Security Number
-    - Title
-    - Url
-- Organisation
-- Time
-- Vehicle
-    - Build Year
-    - Colour
-    - License Plate Number
-    - Model
-    - Type
-
-The final coarse grained tagset (in IOB notation) is the following: 
-
-`['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']`
-
-
-The final fine grained tagset (in IOB notation) is the following: 
-
-`[
-        'O',
-        'B-BUILDING',
-        'I-BUILDING',
-        'B-CITY',
-        'I-CITY',
-        'B-COUNTRY',
-        'I-COUNTRY',
-        'B-PLACE',
-        'I-PLACE',
-        'B-TERRITORY',
-        'I-TERRITORY',
-        'I-UNIT',
-        'B-UNIT',
-        'B-VALUE',
-        'I-VALUE',
-        'B-YEAR',
-        'I-YEAR',
-        'B-STANDARD ABBREVIATION',
-        'I-STANDARD ABBREVIATION',
-        'B-MONTH',
-        'I-MONTH',
-        'B-DAY',
-        'I-DAY',
-        'B-AGE',
-        'I-AGE',
-        'B-ETHNIC CATEGORY',
-        'I-ETHNIC CATEGORY',
-        'B-FAMILY NAME',
-        'I-FAMILY NAME',
-        'B-INITIAL NAME',
-        'I-INITIAL NAME',
-        'B-MARITAL STATUS',
-        'I-MARITAL STATUS',
-        'B-PROFESSION',
-        'I-PROFESSION',
-        'B-ROLE',
-        'I-ROLE',
-        'B-NATIONALITY',
-        'I-NATIONALITY',
-        'B-TITLE',
-        'I-TITLE',
-        'B-URL',
-        'I-URL',
-        'B-TYPE',
-        'I-TYPE',
-]`
-
-
-### Data Splits
-
-Splits created by Joel Niklaus.
-
-
-| language   |   # train files |   # validation files |   # test files |   # train sentences |   # validation sentences |   # test sentences |
-|:-----------|----------------:|---------------------:|---------------:|--------------------:|-------------------------:|-------------------:|
-| bg         |               9 |                    1 |              2 |                1411 |                      166 |                560 |
-| cs         |               9 |                    1 |              2 |                1464 |                      176 |                563 |
-| da         |               9 |                    1 |              2 |                1455 |                      164 |                550 |
-| de         |               9 |                    1 |              2 |                1457 |                      166 |                558 |
-| el         |               9 |                    1 |              2 |                1529 |                      174 |                584 |
-| en         |               9 |                    1 |              2 |                 893 |                       98 |                408 |
-| es         |               7 |                    1 |              1 |                 806 |                      248 |                155 |
-| et         |               9 |                    1 |              2 |                1391 |                      163 |                516 |
-| fi         |               9 |                    1 |              2 |                1398 |                      187 |                531 |
-| fr         |               9 |                    1 |              2 |                1297 |                       97 |                490 |
-| ga         |               9 |                    1 |              2 |                1383 |                      165 |                515 |
-| hu         |               9 |                    1 |              2 |                1390 |                      171 |                525 |
-| it         |               9 |                    1 |              2 |                1411 |                      162 |                550 |
-| lt         |               9 |                    1 |              2 |                1413 |                      173 |                548 |
-| lv         |               9 |                    1 |              2 |                1383 |                      167 |                553 |
-| mt         |               9 |                    1 |              2 |                 937 |                       93 |                442 |
-| nl         |               9 |                    1 |              2 |                1391 |                      164 |                530 |
-| pt         |               9 |                    1 |              2 |                1086 |                      105 |                390 |
-| ro         |               9 |                    1 |              2 |                1480 |                      175 |                557 |
-| sk         |               9 |                    1 |              2 |                1395 |                      165 |                526 |
-| sv         |               9 |                    1 |              2 |                1453 |                      175 |                539 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-*„[…] to our knowledge, there exist no open resources annotated for NERC [Named Entity Recognition and Classificatio] in Spanish in the legal domain. With the
-present contribution, we intend to fill this gap. With the release of the created resources for fine-tuning and
-evaluation of sensitive entities detection in the legal domain, we expect to encourage the development of domain-adapted
-anonymisation tools for Spanish in this field“* (de Gibert Bonet et al., 2022)
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-The dataset consists of documents taken from EUR-Lex corpus which is publicly available. No further
-information on the data collection process are given in de Gibert Bonet et al. (2022).
-
-#### Who are the source language producers?
-
-The source language producers are presumably lawyers.
-
-### Annotations
-
-#### Annotation process
-
-*"The annotation scheme consists of a complex two level hierarchy adapted to the legal domain, it follows the scheme
-described in (Gianola et al., 2020) […] Level 1 entities refer to general categories (PERSON, DATE, TIME, ADDRESS...)
-and level 2 entities refer to more fine-grained subcategories (given name, personal name, day, year, month...). Eur-Lex,
-CPP and DE have been annotated following this annotation scheme […] The manual annotation was performed using
-INCePTION (Klie et al., 2018) by a sole annotator following the guidelines provided by the MAPA consortium."* (de Gibert
-Bonet et al., 2022)
-
-#### Who are the annotators?
-
-Only one annotator conducted the annotation. More information are not provdided in de Gibert Bonet et al. (2022).
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-Note that the dataset at hand presents only a small portion of a bigger corpus as described in de Gibert Bonet et al. 
-(2022). At the time of writing only the annotated documents from the EUR-Lex corpus were available.
-
-Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
-Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
-consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
-dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
-differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
-have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
-original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
-the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
-
-## Additional Information
-
-### Dataset Curators
-
-The names of the original dataset curators and creators can be found in references given below, in the section *Citation
-Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch)
-; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch)
-; [Github](https://github.com/kapllan)).
-
-### Licensing Information
-
-[Attribution 4.0 International (CC BY 4.0) ](https://creativecommons.org/licenses/by/4.0/)
-
-### Citation Information
-
-```
-@article{DeGibertBonet2022,
-author = {{de Gibert Bonet}, Ona and {Garc{\'{i}}a Pablos}, Aitor and Cuadros, Montse and Melero, Maite},
-journal = {Proceedings of the Language Resources and Evaluation Conference},
-number = {June},
-pages = {3751--3760},
-title = {{Spanish Datasets for Sensitive Entity Detection in the Legal Domain}},
-url = {https://aclanthology.org/2022.lrec-1.400},
-year = {2022}
-}
-```
-
-### Contributions
-
-Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this
-dataset.

convert_to_hf_dataset.py

@@ -1,220 +0,0 @@
-import os
-from glob import glob
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-
-from web_anno_tsv import open_web_anno_tsv
-from web_anno_tsv.web_anno_tsv import ReadException, Annotation
-
-pd.set_option('display.max_colwidth', None)
-pd.set_option('display.max_columns', None)
-
-annotation_labels = {'ADDRESS': ['building', 'city', 'country', 'place', 'postcode', 'street', 'territory'],
-                     'AMOUNT': ['unit', 'value'],
-                     'DATE': ['year', 'standard abbreviation', 'month', 'day of the week', 'day', 'calender event'],
-                     'PERSON': ['age', 'email', 'ethnic category', 'family name', 'financial', 'given name – female',
-                                'given name – male',
-                                'health insurance number', 'id document number', 'initial name', 'marital status',
-                                'medical record number',
-                                'nationality', 'profession', 'role', 'social security number', 'title', 'url'],
-                     'ORGANISATION': [],
-                     'TIME': [],
-                     'VEHICLE': ['build year', 'colour', 'license plate number', 'model', 'type']}
-
-# make all labels upper case
-annotation_labels = {key.upper(): [label.upper() for label in labels] for key, labels in annotation_labels.items()}
-print(annotation_labels)
-print("coarse_grained:", list(annotation_labels.keys()))
-print("fine_grained:",
-      [finegrained for finegrained in [finegrained_list for finegrained_list in annotation_labels.values()]])
-
-base_path = Path("extracted")
-
-# TODO future work can add these datasets too to make it larger
-special_paths = {
-    "EL": ["EL/ANNOTATED_DATA/LEGAL/AREIOSPAGOS1/annotated/full_dataset"],
-    "EN": ["EN/ANNOTATED_DATA/ADMINISTRATIVE-LEGAL/annotated/full_dataset"],
-    "FR": ["FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Civil",
-           "FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Commercial",
-           "FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION1/annotated/full_dataset/Criminal",
-           "FR/ANNOTATED_DATA/LEGAL/COUR_CASSATION2/annotated/full_dataset",
-           "FR/ANNOTATED_DATA/MEDICAL/CAS1/annotated/full_dataset"],
-    "IT": ["IT/ANNOTATED_DATA/Corte_Suprema_di_Cassazione/annotated"],
-    "MT": ["MT/ANNOTATED_DATA/ADMINISTRATIVE/annotated/full_dataset",
-           "MT/ANNOTATED_DATA/GENERAL_NEWS/News_1/annotated/full_dataset",
-           "MT/ANNOTATED_DATA/LEGAL/Jurisprudence_1/annotated/full_dataset"],
-}
-
-
-def get_path(language):
-    return base_path / language / "ANNOTATED_DATA/EUR_LEX/annotated/full_dataset"
-
-
-def get_coarse_grained_for_fine_grained(label):
-    for coarse_grained, fine_grained_set in annotation_labels.items():
-        if label in fine_grained_set:
-            return coarse_grained
-    return None  # raise ValueError(f"Did not find fine_grained label {label}")
-
-
-def is_fine_grained(label):
-    for coarse_grained, fine_grained_set in annotation_labels.items():
-        if label.upper() in fine_grained_set:
-            return True
-    return False
-
-
-def is_coarse_grained(label):
-    return label.upper() in annotation_labels.keys()
-
-
-class HashableAnnotation(Annotation):
-    def __init__(self, annotation):
-        super()
-        self.label = annotation.label
-        self.start = annotation.start
-        self.stop = annotation.stop
-        self.text = annotation.text
-
-    def __eq__(self, other):
-        return self.label == other.label and self.start == other.start and self.stop == other.stop and self.text == other.text
-
-    def __hash__(self):
-        return hash(('label', self.label, 'start', self.start, 'stop', self.stop, 'text', self.text))
-
-
-def get_token_annotations(token, annotations):
-    annotations = list(dict.fromkeys([HashableAnnotation(ann) for ann in annotations]))  # remove duplicate annotations
-    coarse_grained = "O"
-    fine_grained = "o"
-    for annotation in annotations:
-        label = annotation.label
-        # if token.start == annotation.start and token.stop == annotation.stop:  # fine_grained annotation
-        if token.start >= annotation.start and token.stop <= annotation.stop:  # course_grained annotation
-            # we don't support multilabel annotations for each token for simplicity.
-            # So when a token already has an annotation for either coarse or fine grained, we don't assign new ones.
-            if coarse_grained == "O" and is_coarse_grained(label):
-                coarse_grained = label
-            elif fine_grained == "o" and is_fine_grained(label):
-                # some DATE are mislabeled as day but it is hard to correct this. So we ignore it
-                fine_grained = label
-
-    return coarse_grained.upper(), fine_grained.upper()
-
-
-def generate_IOB_labelset(series, casing_function):
-    last_ent = ""
-    new_series = []
-    for ent in series:
-        if ent in ["o", "O"]:
-            ent_to_add = ent
-        else:
-            if ent != last_ent:  # we are the first one
-                ent_to_add = "B-" + ent
-            else:
-                ent_to_add = "I-" + ent
-        new_series.append(casing_function(ent_to_add))
-        last_ent = ent
-    return new_series
-
-
-def get_annotated_sentence(result_sentence, sentence):
-    result_sentence["tokens"] = []
-    result_sentence["coarse_grained"] = []
-    result_sentence["fine_grained"] = []
-    for k, token in enumerate(sentence.tokens):
-        coarse_grained, fine_grained = get_token_annotations(token, sentence.annotations)
-        token = token.text.replace(u'\xa0', u' ').strip()  # replace non-breaking spaces
-        if token:  # remove empty tokens (only consisted of whitespace before
-            result_sentence["tokens"].append(token)
-            result_sentence["coarse_grained"].append(coarse_grained)
-            result_sentence["fine_grained"].append(fine_grained)
-    result_sentence["coarse_grained"] = generate_IOB_labelset(result_sentence["coarse_grained"], str.upper)
-    result_sentence["fine_grained"] = generate_IOB_labelset(result_sentence["fine_grained"], str.upper)
-    return result_sentence
-
-
-languages = sorted([Path(file).stem for file in glob(str(base_path / "*"))])
-
-
-def parse_files(language):
-    data_path = get_path(language.upper())
-    result_sentences = []
-    not_parsable_files = 0
-    file_names = sorted(list(glob(str(data_path / "*.tsv"))))
-    for file in file_names:
-        try:
-            with open_web_anno_tsv(file) as f:
-                for i, sentence in enumerate(f):
-                    result_sentence = {"language": language, "type": "EUR-LEX",
-                                       "file_name": Path(file).stem, "sentence_number": i}
-                    result_sentence = get_annotated_sentence(result_sentence, sentence)
-                    result_sentences.append(result_sentence)
-            print(f"Successfully parsed file {file}")
-        except ReadException as e:
-            print(f"Could not parse file {file}")
-            not_parsable_files += 1
-    print("Not parsable files: ", not_parsable_files)
-    return pd.DataFrame(result_sentences), not_parsable_files
-
-
-stats = []
-train_dfs, validation_dfs, test_dfs = [], [], []
-for language in languages:
-    language = language.lower()
-    print(f"Parsing language {language}")
-    df, not_parsable_files = parse_files(language)
-    file_names = df.file_name.unique()
-
-    # df.coarse_grained.apply(lambda x: print(set(x)))
-
-    # split by file_name
-    num_fn = len(file_names)
-    train_fn, validation_fn, test_fn = np.split(np.array(file_names), [int(.8 * num_fn), int(.9 * num_fn)])
-
-    lang_train = df[df.file_name.isin(train_fn)]
-    lang_validation = df[df.file_name.isin(validation_fn)]
-    lang_test = df[df.file_name.isin(test_fn)]
-
-    train_dfs.append(lang_train)
-    validation_dfs.append(lang_validation)
-    test_dfs.append(lang_test)
-
-    lang_stats = {"language": language}
-
-    lang_stats["# train files"] = len(train_fn)
-    lang_stats["# validation files"] = len(validation_fn)
-    lang_stats["# test files"] = len(test_fn)
-
-    lang_stats["# train sentences"] = len(lang_train.index)
-    lang_stats["# validation sentences"] = len(lang_validation.index)
-    lang_stats["# test sentences"] = len(lang_test.index)
-
-    stats.append(lang_stats)
-
-stat_df = pd.DataFrame(stats)
-print(stat_df.to_markdown(index=False))
-
-train = pd.concat(train_dfs)
-validation = pd.concat(validation_dfs)
-test = pd.concat(test_dfs)
-
-df = pd.concat([train, validation, test])
-print(f"The final coarse grained tagset (in IOB notation) is the following: "
-      f"`{list(df.coarse_grained.explode().unique())}`")
-print(f"The final fine grained tagset (in IOB notation) is the following: "
-      f"`{list(df.fine_grained.explode().unique())}`")
-
-
-# save splits
-def save_splits_to_jsonl(config_name):
-    # save to jsonl files for huggingface
-    if config_name: os.makedirs(config_name, exist_ok=True)
-    train.to_json(os.path.join(config_name, "train.jsonl"), lines=True, orient="records", force_ascii=False)
-    validation.to_json(os.path.join(config_name, "validation.jsonl"), lines=True, orient="records", force_ascii=False)
-    test.to_json(os.path.join(config_name, "test.jsonl"), lines=True, orient="records", force_ascii=False)
-
-
-save_splits_to_jsonl("")

validation.jsonl → joelito--mapa/json-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a9b1a294688da296414c8042d4a18deb7a8f93006085f197074fe9a3a9b65abc
-size 2874796
+oid sha256:ee64c067dd8cbcb368c18ff815c7e84ee55ed7738a02b5226c948342118111df
+size 1247012

test.jsonl → joelito--mapa/json-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9498f67f080b1745b5b41feb32f66beb30e2bd2ca024306a910a7c59c3134d14
-size 7717849
+oid sha256:233b1a35c7d1a9974d1d091ba824b16456a29e82ea80a9f8343b24f61f3da706
+size 3313905

train.jsonl → joelito--mapa/json-validation.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f2e8da9572e8bdfd699c998803d40b9680b8c6b92ea3356e8786d675cc5a19e4
-size 22116076
+oid sha256:f228b663a6355b2c2b5c01027e4a066b3b7e80572e8ed5e0c718156353efa125
+size 475237