Convert dataset to Parquet (#1)

- Convert dataset to Parquet (b662b33fc844916820ede39f66fe09594e0fee61)
- Delete loading script (dbdab710ecd6259b0cc53c2646570560acb0fae9)


Co-authored-by: Phuc Tran Truong <[email protected]>

DWIE.py

@@ -1,346 +0,0 @@
-# I am trying to understand to the following code. Do not use this for any purpose as I do not support this.
-# Use the original source from https://huggingface.co/datasets/DFKI-SLT/science_ie/raw/main/science_ie.py
-
-
-# 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.
-"""DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document."""  
-
-import datasets
-from datasets import DownloadManager
-import os
-import json
-import requests
-from typing import Optional, List, Union
-import argparse
-import hashlib
-from collections import OrderedDict
-from time import sleep
-
-#from dataset.utils.tokenizer import TokenizerCPN
-
-
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@article{ZAPOROJETS2021102563,
-title = {{DWIE}: An entity-centric dataset for multi-task document-level information extraction},
-journal = {Information Processing & Management},
-volume = {58},
-number = {4},
-pages = {102563},
-year = {2021},
-issn = {0306-4573},
-doi = {https://doi.org/10.1016/j.ipm.2021.102563},
-url = {https://www.sciencedirect.com/science/article/pii/S0306457321000662},
-author = {Klim Zaporojets and Johannes Deleu and Chris Develder and Thomas Demeester}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities 
-on the level of the complete document. This contrasts with currently dominant mention-driven approaches that start 
-from the detection and classification of named entity mentions in individual sentences. Also, the dataset was
-randomly sampled from a news platform (English online content from Deutsche Welle), and the annotation scheme 
-was generated to cover that content. This makes the setting more realistic than in datasets with pre-determined 
-annotation schemes, and non-uniform sampling of content to obtain balanced annotations."""
-
-# Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/klimzaporojets/DWIE"
-
-# Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {"Task_1":
-          {
-          "url":"https://github.com/klimzaporojets/DWIE/archive/refs/heads/master.zip"
-            }
-          }
-
-
-
-class DWIE(datasets.GeneratorBasedBuilder):
-    """
-    DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document.
-    """
-
-    VERSION = datasets.Version("1.1.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="Task_1", version=VERSION,
-                               description="Relation classification"),
-    ]
-    DEFAULT_CONFIG_NAME = "Task_1"
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "content": datasets.Value("string"),
-                "tags": datasets.Value("string"),
-                "mentions": [
-                    {
-                        "begin": datasets.Value("int32"),
-                        "end": datasets.Value("int32"),
-                        "text": datasets.Value("string"),
-                        "concept": datasets.Value("int32"),
-                        "candidates" : datasets.Sequence(datasets.Value("string")),
-                        "scores": datasets.Sequence(datasets.Value("float32"))
-                    }
-                ],
-                "concepts": [
-                    {
-                        "concept": datasets.Value("int32"),
-                        "text": datasets.Value("string"),
-                        "keyword": datasets.Value("bool"),
-                        "count": datasets.Value("int32"),
-                        "link": datasets.Value("string"),
-                        "tags": datasets.Sequence(datasets.Value("string")),  
-                        
-                    }
-                ],
-                "relations": [
-                    {
-                        "s": datasets.Value("int32"),
-                        "p": datasets.Value("string"),
-                        "o": datasets.Value("int32"),
-                        
-                    }
-                ],
-                "frames": [
-                    {
-                        "type": datasets.Value("string"),
-                        "slots": [{
-                          "name": datasets.Value("string"),
-                          "value":datasets.Value("int32")
-                        }]
-                        
-                    }
-                ],
-                "iptc": datasets.Sequence(datasets.Value("string"))
-                
-            }
-        )
-
-        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]
-        downloaded = dl_manager.download_and_extract(_URLS) 
-        article_id_to_url_json= json.load(open(downloaded['Task_1']['url'] + '/DWIE-master/data/article_id_to_url.json'))
-        ids_to_new_ids = dict()
-        # some ids seem to be different, for now only this one:
-        ids_to_new_ids[18525950] = 19026607
-
-        should_tokenize = False
-
-        content_to_new_content = {'DW_40663341': [('starting with Sunday\'s', 'starting Sunday\'s'),
-                                              ('$1 million (€840,000)', 'one million dollars (840,000 euros)'),
-                                              ('who kneel in protest during', 'to kneel in protest during')]}
-        
-        articles_done = 0
-        total_articles = len(article_id_to_url_json)
-        problematic_articles = set()
-        problematic_hash_articles = set()
-        all_annos = []
-        for curr_article in article_id_to_url_json:
-          article_id = curr_article['id']
-          article_url = curr_article['url']
-          article_id_nr = int(article_id[3:])
-          if article_id_nr in ids_to_new_ids:
-            article_url = article_url.replace(str(article_id_nr), str(ids_to_new_ids[article_id_nr]))
-          article_hash = curr_article['hash']
-          #print('fetching {} out of {} articles -'.format(articles_done, total_articles), curr_article)
-
-          annos_only_art_path = downloaded['Task_1']['url'] + '/DWIE-master/data/annos/' + curr_article['id'] + '.json'
-          annos_only_json = json.load(open(annos_only_art_path))
-          done = False
-          attempts = 0
-          while not done and attempts <= 3:
-            # try:
-            a = requests.get(article_url, allow_redirects=True).json()
-            if 'name' in a:
-                article_title = a['name']
-            else:
-                print('WARNING: no name detected for ', article_id)
-                article_title = ''
-            if 'teaser' in a:
-                article_teaser = a['teaser']
-            else:
-                print('WARNING: no teaser detected for ', article_id)
-                article_teaser = ''
-
-            if 'text' in a:
-                article_text = a['text']
-            else:
-                print('WARNING: no text detected for ', article_id)
-                article_text = ''
-
-            article_content_no_strip = '{}\n{}\n{}'.format(article_title, article_teaser, article_text)
-            article_content = article_content_no_strip
-
-            if article_id in content_to_new_content:
-                for str_dw, str_dwie in content_to_new_content[article_id]:
-                    article_content = article_content.replace(str_dw, str_dwie)
-            
-            if 'mentions' in annos_only_json:
-                for idx_mention, curr_mention in enumerate(annos_only_json['mentions']):
-                    curr_mention_text = curr_mention['text'].replace(' ', ' ')
-                    curr_mention_text = curr_mention_text.replace('​', '')
-                    solved = False
-                    if "begin" not in curr_mention:
-                      curr_mention["begin"] = 0
-                    if "end" not in curr_mention:
-                      curr_mention["end"] = 0
-                    if "text" not in curr_mention:
-                      curr_mention["text"] = ""
-                    if "concept" not in curr_mention:
-                      curr_mention["concept"] = 0
-                    
-                    
-                    if "candidates" not in curr_mention:
-                      curr_mention["candidates"] = []
-                    if "scores" not in curr_mention:
-                      curr_mention["scores"] = []
-                
-                    if article_content[curr_mention['begin']:curr_mention['end']] != curr_mention_text:
-                        curr_mention_begin = curr_mention['begin']
-                        curr_mention_end = curr_mention['end']
-                        offset = 0
-
-                        if not solved:
-                            print('--------------------------------')
-                            print('ERROR ALIGNMENT: texts don\'t match for {}: "{}" vs "{}", the textual content of '
-                                  'the files won\'t be complete '
-                                  .format(article_id, article_content[curr_mention['begin']:curr_mention['end']],
-                                          curr_mention_text))
-                            print('--------------------------------')
-                            problematic_articles.add(article_id)
-                        else:
-                            if "candidates" not in curr_mention:
-                              curr_mention["candidates"] = []
-                
-                            curr_mention['begin'] = curr_mention_begin - offset
-                            curr_mention['end'] = curr_mention_end - offset
-            if 'concepts' in annos_only_json:
-                for idx_concept, curr_concept in enumerate(annos_only_json['concepts']):
-                    if "concept" not in curr_concept:
-                      curr_concept["concept"] = 0
-                    if "text" not in curr_concept:
-                      curr_concept["text"] = ""
-                    if "count" not in curr_concept:
-                      curr_concept["count"] = 0
-                    if "link" not in curr_concept:
-                      curr_concept["link"] = ""
-                    if "tags" not in curr_concept:
-                      curr_concept["tags"] = []
-            
-            if not should_tokenize:
-                annos_json = {'id': annos_only_json['id'],
-                                          'content': article_content,
-                                          'tags': annos_only_json['tags'],
-                                          'mentions': annos_only_json['mentions'],
-                                          'concepts': annos_only_json['concepts'],
-                                          'relations': annos_only_json['relations'],
-                                          'frames': annos_only_json['frames'],
-                                          'iptc': annos_only_json['iptc']}
-                all_annos.append(annos_json)
-                
-                #print("annos_json",annos_json)
-            else:
-                tokenized = tokenizer.tokenize(article_content)
-                tokens = list()
-                begin = list()
-                end = list()
-                for curr_token in tokenized:
-                    tokens.append(curr_token['token'])
-                    begin.append(curr_token['offset'])
-                    end.append(curr_token['offset'] + curr_token['length'])
-                annos_json = OrderedDict({'id': annos_only_json['id'],
-                                          'content': article_content,
-                                          'tokenization': OrderedDict({'tokens': tokens, 'begin': begin, 'end': end}),
-                                          'tags': annos_only_json['tags'],
-                                          'mentions': annos_only_json['mentions'],
-                                          'concepts': annos_only_json['concepts'],
-                                          'relations': annos_only_json['relations'],
-                                          'frames': annos_only_json['frames'],
-                                          'iptc': annos_only_json['iptc']})
-                
-            hash_content = hashlib.sha1(article_content.encode("UTF-8")).hexdigest()
-
-            if hash_content != article_hash:
-                print('!!ERROR - hash doesn\'t match for ', article_id)
-                problematic_hash_articles.add(article_id)
-                attempts += 1
-
-            sleep(.1)
-            done = True
-          if done:
-              articles_done += 1
-
-          
-        return[
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "all_annos" : all_annos,
-                    
-                }
-
-            )
-          ]
-        
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, all_annos):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.               
-        for data in all_annos:
-          yield data['id'], {
-            "id": data['id'],
-            "content":data['content'],
-            "tags": data['tags'],
-            "mentions": data['mentions'],
-            "concepts": data['concepts'],
-            "relations": data['relations'],
-            "frames": data['frames'],
-            "iptc": data['iptc']
-          }
-          
-
-

README.md

@@ -1,28 +1,96 @@
 ---
-license: other
-language:
-- en
-pretty_name: >-
-  DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset for
-  document-level multi-task Information Extraction (IE).
-size_categories:
-- 10M<n<100M
 annotations_creators:
 - expert-generated
 language_creators:
 - found
+language:
+- en
+license: other
 multilinguality:
 - monolingual
-paperswithcode_id: acronym-identification
+size_categories:
+- 10M<n<100M
 source_datasets:
 - original
-tags:
-- Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking
 task_categories:
 - feature-extraction
 - text-classification
 task_ids:
 - entity-linking-classification
+paperswithcode_id: acronym-identification
+pretty_name: DWIE (Deutsche Welle corpus for Information Extraction) is a new dataset
+  for document-level multi-task Information Extraction (IE).
+tags:
+- Named Entity Recognition, Coreference Resolution, Relation Extraction, Entity Linking
+dataset_info:
+  config_name: Task_1
+  features:
+  - name: id
+    dtype: string
+  - name: content
+    dtype: string
+  - name: tags
+    dtype: string
+  - name: mentions
+    list:
+    - name: begin
+      dtype: int32
+    - name: end
+      dtype: int32
+    - name: text
+      dtype: string
+    - name: concept
+      dtype: int32
+    - name: candidates
+      sequence: string
+    - name: scores
+      sequence: float32
+  - name: concepts
+    list:
+    - name: concept
+      dtype: int32
+    - name: text
+      dtype: string
+    - name: keyword
+      dtype: bool
+    - name: count
+      dtype: int32
+    - name: link
+      dtype: string
+    - name: tags
+      sequence: string
+  - name: relations
+    list:
+    - name: s
+      dtype: int32
+    - name: p
+      dtype: string
+    - name: o
+      dtype: int32
+  - name: frames
+    list:
+    - name: type
+      dtype: string
+    - name: slots
+      list:
+      - name: name
+        dtype: string
+      - name: value
+        dtype: int32
+  - name: iptc
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 16533390
+    num_examples: 802
+  download_size: 3822277
+  dataset_size: 16533390
+configs:
+- config_name: Task_1
+  data_files:
+  - split: train
+    path: Task_1/train-*
+  default: true
 train-eval-index:
 - col_mapping:
     labels: tags

Task_1/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03fd09eda0e5e817a194d85c10d513f0952f0b5596d0f421f50ef5d7a0011b26
+size 3822277