added dataloader, preparation script and dataset card

MultiLegalPile_Wikipedia_Filtered.py

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+"""MultiLegalPile Wikipedia Filtered"""
+
+import json
+
+import datasets
+from huggingface_hub.file_download import hf_hub_url
+
+try:
+    import lzma as xz
+except ImportError:
+    import pylzma as xz
+
+datasets.logging.set_verbosity_info()
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """
+A filtered version of the MultiLegalPile dataset, together with wikipedia articles.
+"""
+
+_URL = "https://huggingface.co/datasets/joelito/MultiLegalPile_Wikipedia_Filtered"
+
+_LANGUAGES = ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr",
+              "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"]
+
+_TYPES = ["caselaw", "contracts", "legislation", "other", "wikipedia"]
+
+_JURISDICTONS = ["Austria", "Belgium", "Bulgaria", "Croatia", "Czechia", "Denmark", "Estonia", "Finland",
+                 "France", "Germany", "Greece", "Hungary", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg",
+                 "Malta", "Netherlands", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden",
+                 "EU", "Switzerland", "UK", "US", "Canada", "N/A"]
+
+# IMPORTANT: Increase this once larger datasets are available (en_caselaw has 9 at the moment)
+_HIGHEST_NUMBER_OF_SHARDS = 9
+
+
+class MultiLegalPileWikipediaFilteredConfig(datasets.BuilderConfig):
+    """BuilderConfig for MultiLegalPileWikipediaFiltered."""
+
+    def __init__(self, name: str, **kwargs):
+        """BuilderConfig for MultiLegalPileWikipediaFiltered.
+        Args:
+                name:           combination of language and type with _
+                language:       One of bg,cs,da,de,el,en,es,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv or all
+                type:           One of caselaw,contracts,legislation,other,wikipedia or all
+              **kwargs: keyword arguments forwarded to super.
+            """
+        super(MultiLegalPileWikipediaFilteredConfig, self).__init__(**kwargs)
+        self.name = name
+        self.language = name.split("_")[0]
+        self.type = name.split("_")[1]
+
+
+class MultiLegalPileWikipediaFiltered(datasets.GeneratorBasedBuilder):
+    """
+    MultiLegalPileWikipediaFiltered:
+    A filtered dataset of multilingual legal data and wikipedias in the EU languages
+    """
+    BUILDER_CONFIG_CLASS = MultiLegalPileWikipediaFilteredConfig
+
+    BUILDER_CONFIGS = [MultiLegalPileWikipediaFilteredConfig(f"{language}_{type}")
+                       for type in _TYPES + ["all"]
+                       for language in _LANGUAGES + ["all"]]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "language": datasets.Value("string"),  # one of _LANGUAGES
+                    "type": datasets.Value("string"),  # one of _TYPES
+                    "jurisdiction": datasets.Value("string"),  # one of _JURISDICTONS
+                    "text": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        def download_url(file_name):
+            url = hf_hub_url(repo_id="joelito/MultiLegalPile_Wikipedia_Filtered",
+                             filename=f"data/{file_name}.jsonl.xz", repo_type="dataset")
+            return dl_manager.download(url)
+
+        languages = _LANGUAGES if self.config.language == "all" else [self.config.language]
+        types = _TYPES if self.config.type == "all" else [self.config.type]
+
+        split_generators = []
+        for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION]:
+            filepaths = []
+            for language in languages:
+                for type in types:
+                    for shard in range(_HIGHEST_NUMBER_OF_SHARDS):
+                        try:
+                            filepaths.append(download_url(f"{language}_{type}_{split}_{shard}"))
+                        except:
+                            break  # we found the last shard
+            split_generators.append(
+                datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": filepaths})
+            )
+        return split_generators
+
+    def _generate_examples(self, filepaths):
+        """This function returns the examples in the raw (text) form by iterating on all the files."""
+        id_ = 0
+        for filepath in filepaths:
+            logger.info("Generating examples from = %s", filepath)
+            try:
+                with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
+                    for line in f:
+                        if line:
+                            example = json.loads(line)
+                            if example is not None and isinstance(example, dict):
+                                yield id_, example
+                                id_ += 1
+            except Exception:
+                logger.exception("Error while processing file %s", filepath)

README.md

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+---
+annotations_creators:
+- other
+language_creators:
+- found
+language:
+- bg 
+- cs 
+- da 
+- de 
+- el 
+- en
+- es 
+- et 
+- fi 
+- fr 
+- ga
+- hr 
+- hu 
+- it 
+- lt 
+- lv 
+- mt
+- nl 
+- pl 
+- pt 
+- ro 
+- sk 
+- sl 
+- sv
+license:
+- cc-by-4.0
+multilinguality:
+- multilingual
+paperswithcode_id: null
+pretty_name: "MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles."
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+task_categories:
+- fill-mask
+
+---
+
+# Dataset Card for MultiLegalPile_Wikipedia_Filtered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles
+
+## 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:** [Joel Niklaus](mailto:[email protected])
+
+### Dataset Summary
+
+The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
+It spans over 24 languages and four legal text types. 
+
+### Supported Tasks and Leaderboards
+
+The dataset supports the tasks of fill-mask.
+
+### Languages
+
+The following languages are supported: 
+bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
+
+## Dataset Structure
+
+It is structured in the following format: {language}_{text_type}_{shard}.jsonl.xz
+
+text_type is one of the following:
+
+- caselaw
+- contracts
+- legislation
+- other
+- wikipedia
+
+
+Use the dataset like this:
+```python
+from datasets import load_dataset
+
+config = 'en_contracts' # {language}_{text_type}
+dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
+```
+
+'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
+To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
+all_legislation').
+
+### Data Instances
+
+The file format is jsonl.xz and there is a `train` and `validation` split available. 
+Since some configurations are very small or non-existent, they might not contain a train split or not be present at all.
+
+The complete dataset consists of five large subsets:
+- [Native Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile)
+- [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources) 
+- [MC4 Legal](https://huggingface.co/datasets/joelito/mc4_legal)
+- [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law)
+- [EU Wikipedias](https://huggingface.co/datasets/joelito/EU_Wikipedias)
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+This dataset has been created by combining the following datasets:
+Native Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.
+It has been filtered to remove short documents (less than 64 whitespace-separated tokens) and 
+documents with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+TODO add citation
+```
+
+### Contributions
+
+Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.

prepare_legal_data.py

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+# No chunks, one doc per line
+
+# remove new lines, etc.
+# create a corpus of min 200-400 GB ==> ~100B tokens
+# max file size: 4GB because of huggingface
+# validation set: ~100M tokens ==> 200-400MB
+
+import glob
+import json
+import multiprocessing
+
+import tqdm
+import os
+import re
+from multiprocessing import Pool
+
+from datasets import load_dataset
+from tokenizers import normalizers
+
+_LANGUAGES = ['bg', 'cs', 'da', 'de', 'el', 'en', 'es', 'et', 'fi', 'fr', 'ga', 'hr',
+              'hu', 'it', 'lt', 'lv', 'mt', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl', 'sv']
+_DOMAIN_TYPES = ['legislation', 'caselaw', 'contracts', 'other', 'wikipedia']
+
+custom_normalizer = normalizers.NFKD()
+
+VALIDATION_SIZE = 1_000  # ~1MB per configuration ==> some low-resource configs will only have a validation file
+
+filtered_dir = os.path.join('data', 'filtered')
+os.makedirs(filtered_dir, exist_ok=True)
+
+
+def preprocess_dataset(languages=None, domain_types=None):
+    lang_type_datasets = []
+    # set defaults if they are not set
+    if languages is None:
+        languages = _LANGUAGES
+    if domain_types is None:
+        domain_types = _DOMAIN_TYPES
+
+    for LANG in languages:
+        for DOMAIN_TYPE in domain_types:
+            try:
+                if DOMAIN_TYPE == 'wikipedia':
+                    # get from EU_Wikipedias
+                    dataset = load_dataset("joelito/EU_Wikipedias", date="20221120", language=LANG,
+                                           split='train', streaming=True, use_auth_token=True)
+                else:
+                    # get from Multi_Legal_Pile
+                    dataset = load_dataset("joelito/Multi_Legal_Pile", f'{LANG}_{DOMAIN_TYPE}',
+                                           split='train', streaming=True, use_auth_token=True)
+                dataset = dataset.shuffle(seed=42, buffer_size=10_000)
+                print(f'Found data for `{DOMAIN_TYPE}` in language `{LANG}`.')
+            except:
+                print(f'There is no data for `{DOMAIN_TYPE}` in language `{LANG}`.')
+                continue
+            lang_type_datasets.append(dataset)
+    return lang_type_datasets
+
+
+def write_samples(dataset_number):
+    dataset, dataset_name = dataset_number
+    if len(dataset_name.split('_')) == 1:  # wikipedia
+        language = dataset_name.split('.')[1]
+        domain_type = "wikipedia"
+        dataset_name = f"{language}_{domain_type}"  # reformat the config name so that we have wikipedia in the name
+    else:
+        language, domain_type = dataset_name.split('_')
+    total_count, temp_count, all_samples, file_number = 0, 0, 0, 0
+    out_file = open_file(dataset_name, file_number, "validation")  # we save the first examples to the validation set
+    print(f'Processing for dataset {dataset_name} started!')
+    # Read each document
+    for sample in tqdm.tqdm(dataset):
+        try:
+            text = normalize_text(sample['text'])
+            if "validation" in out_file.name and temp_count > VALIDATION_SIZE:
+                # if we are saving to eval, and we have enough samples in the eval set, switch to train
+                out_file.close()
+                temp_count = 0
+                out_file = open_file(dataset_name, file_number, "train")
+            # on average approx. 2GB per file, compresses (with xz) to around ~500MB (xz: ~75% compression ratio)
+            if "train" in out_file.name and temp_count > 500_000: # err on the small side of the file size
+                # if we are saving to train, and we reached the max size per file, switch to the next file
+                out_file.close()
+                file_number += 1
+                temp_count = 0
+                out_file = open_file(dataset_name, file_number, "train")
+            # if the text is usable for pretraining, save it
+            if is_text_usable(text):
+                jurisdiction = sample.get('jurisdiction', "N/A")  # set defaults for wikipedia
+                type = sample.get("type", "wikipedia")  # set defaults for wikipedia
+                entry = {"language": sample["language"], "type": type, "jurisdiction": jurisdiction, "text": text}
+                out_file.write(json.dumps(entry) + '\n')
+                total_count += 1
+                temp_count += 1
+            all_samples += 1
+        except:
+            continue
+
+    try:
+        out_file.close()
+    except:
+        pass
+
+    print(f'Processing for dataset {dataset_name} finished with {total_count}/{all_samples}!')
+    return
+
+
+def is_text_usable(text):
+    # Compute percentage of alphabetical characters in relation to full sequence length
+    punctuation = '!\"#$%&\'()*+,\-\./:;<=>?@\[\\\]\^_`{\|}~'
+    alpha_text = re.sub(rf'[{punctuation}\d]', '', text)  # remove numbers and punctuation
+    alpha_percent = len(alpha_text) / len(text)
+    # Compute total chunk length
+    text_length = len(text.split())
+    # Ignore sequences with more than 30% numbers or short sequences (less than 64 tokens)
+    return alpha_percent > 0.7 and text_length > 64
+
+
+def normalize_text(text):
+    # Normalize the document
+    text = custom_normalizer.normalize_str(text)
+    # Replace multiple newline and whitespaces
+    return re.sub(r'(\n )+', r'\n ', re.sub(r'( *[\n\r]+ *)+', r'\n ', re.sub(r'[\t ]+', r' ', text)))
+
+
+def open_file(dataset_name, file_number, split):
+    return open(os.path.join(filtered_dir, f'{dataset_name}_{split}_{file_number}.jsonl'), 'w', encoding='utf8')
+
+
+def clean_and_filter_documents():
+    # Load all datasets across languages and types
+    lang_type_datasets = preprocess_dataset(languages=None, domain_types=None)
+    # also pass in dataset_name
+    lang_type_datasets = [(dataset, dataset.config_name) for dataset in lang_type_datasets]
+    print(lang_type_datasets)
+
+    # Launch pool to preprocess datasets in parallel
+    max_num_processes = min(multiprocessing.cpu_count() - 2, len(lang_type_datasets))
+    num_processes = max(max_num_processes, 1)
+    print(f'Launching a Pool with maximum {num_processes} processes...')
+    with Pool(num_processes) as pool:
+        pool.map(write_samples, lang_type_datasets)
+
+    # Compress datasets
+    print(f"Compressing datasets at {filtered_dir}")
+    # Do this at the end because we use multithreading
+    for path in glob.glob(os.path.join(filtered_dir, '*.jsonl')):
+        print(f"Compressing {path}")
+        os.system(f'xz -zkf -T0 {path}')  # -TO to use multithreading
+        print(f"Removing uncompressed file at {path}")
+        os.system(f'rm {path}')  # remove uncompressed file to save space
+
+    print(f"Finished preparing legal data")
+
+
+
+if __name__ == '__main__':
+    """
+    Run with 
+    export PYTHONPATH=. && python prepare_legal_data.py | tee prepare_legal_data.log 
+    """
+    clean_and_filter_documents()
+
+# Get locally
+# def get_file(LANG, DOMAIN_TYPE, split, number):
+#    base_folder = "data/mlm_dataset/chunks_512"
+#    return f'{base_folder}/{LANG}_{DOMAIN_TYPE}_{split}_{number}.jsonl.xz'
+
+# files = [get_file(LANG, DOMAIN_TYPE, 'train', i) for i in range(1, 5)]
+# files = [f for f in files if os.path.exists(f)] # make sure the file actually exists
+# dataset = load_dataset("json", data_files={'train': files}, split='train', streaming=True)
+
+# TODO write dataset cards for chunked, eu wikipedia and filtered dataset