Convert dataset to Parquet

README.md

@@ -29,7 +29,7 @@ pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0
 tags:
 - semantic-role-labeling
 dataset_info:
-- config_name: english_v4
+- config_name: arabic_v4
   features:
   - name: document_id
     dtype: string
@@ -40,58 +40,7 @@ dataset_info:
     - name: words
       sequence: string
     - name: pos_tags
-      sequence:
-        class_label:
-          names:
-            '0': XX
-            '1': '``'
-            '2': $
-            '3': ''''''
-            '4': ','
-            '5': -LRB-
-            '6': -RRB-
-            '7': .
-            '8': ':'
-            '9': ADD
-            '10': AFX
-            '11': CC
-            '12': CD
-            '13': DT
-            '14': EX
-            '15': FW
-            '16': HYPH
-            '17': IN
-            '18': JJ
-            '19': JJR
-            '20': JJS
-            '21': LS
-            '22': MD
-            '23': NFP
-            '24': NN
-            '25': NNP
-            '26': NNPS
-            '27': NNS
-            '28': PDT
-            '29': POS
-            '30': PRP
-            '31': PRP$
-            '32': RB
-            '33': RBR
-            '34': RBS
-            '35': RP
-            '36': SYM
-            '37': TO
-            '38': UH
-            '39': VB
-            '40': VBD
-            '41': VBG
-            '42': VBN
-            '43': VBP
-            '44': VBZ
-            '45': WDT
-            '46': WP
-            '47': WP$
-            '48': WRB
+      sequence: string
     - name: parse_tree
       dtype: string
     - name: predicate_lemmas
@@ -155,16 +104,16 @@ dataset_info:
         length: 3
   splits:
   - name: train
-    num_bytes: 112246121
-    num_examples: 1940
+    num_bytes: 41944525
+    num_examples: 359
   - name: validation
-    num_bytes: 14116925
-    num_examples: 222
+    num_bytes: 4849937
+    num_examples: 44
   - name: test
-    num_bytes: 14709044
-    num_examples: 222
-  download_size: 193644139
-  dataset_size: 141072090
+    num_bytes: 4890923
+    num_examples: 44
+  download_size: 12754930
+  dataset_size: 51685385
 - config_name: chinese_v4
   features:
   - name: document_id
@@ -278,17 +227,17 @@ dataset_info:
         length: 3
   splits:
   - name: train
-    num_bytes: 77195698
+    num_bytes: 71179293
     num_examples: 1391
   - name: validation
-    num_bytes: 10828169
+    num_bytes: 9952651
     num_examples: 172
   - name: test
-    num_bytes: 9585138
+    num_bytes: 8850344
     num_examples: 166
-  download_size: 193644139
-  dataset_size: 97609005
-- config_name: arabic_v4
+  download_size: 12563307
+  dataset_size: 89982288
+- config_name: english_v12
   features:
   - name: document_id
     dtype: string
@@ -299,7 +248,60 @@ dataset_info:
     - name: words
       sequence: string
     - name: pos_tags
-      sequence: string
+      sequence:
+        class_label:
+          names:
+            '0': XX
+            '1': '``'
+            '2': $
+            '3': ''''''
+            '4': '*'
+            '5': ','
+            '6': -LRB-
+            '7': -RRB-
+            '8': .
+            '9': ':'
+            '10': ADD
+            '11': AFX
+            '12': CC
+            '13': CD
+            '14': DT
+            '15': EX
+            '16': FW
+            '17': HYPH
+            '18': IN
+            '19': JJ
+            '20': JJR
+            '21': JJS
+            '22': LS
+            '23': MD
+            '24': NFP
+            '25': NN
+            '26': NNP
+            '27': NNPS
+            '28': NNS
+            '29': PDT
+            '30': POS
+            '31': PRP
+            '32': PRP$
+            '33': RB
+            '34': RBR
+            '35': RBS
+            '36': RP
+            '37': SYM
+            '38': TO
+            '39': UH
+            '40': VB
+            '41': VBD
+            '42': VBG
+            '43': VBN
+            '44': VBP
+            '45': VBZ
+            '46': VERB
+            '47': WDT
+            '48': WP
+            '49': WP$
+            '50': WRB
     - name: parse_tree
       dtype: string
     - name: predicate_lemmas
@@ -363,17 +365,17 @@ dataset_info:
         length: 3
   splits:
   - name: train
-    num_bytes: 42017761
-    num_examples: 359
+    num_bytes: 173938007
+    num_examples: 10539
   - name: validation
-    num_bytes: 4859292
-    num_examples: 44
+    num_bytes: 24249220
+    num_examples: 1370
   - name: test
-    num_bytes: 4900664
-    num_examples: 44
-  download_size: 193644139
-  dataset_size: 51777717
-- config_name: english_v12
+    num_bytes: 18240264
+    num_examples: 1200
+  download_size: 32775165
+  dataset_size: 216427491
+- config_name: english_v4
   features:
   - name: document_id
     dtype: string
@@ -391,53 +393,51 @@ dataset_info:
             '1': '``'
             '2': $
             '3': ''''''
-            '4': '*'
-            '5': ','
-            '6': -LRB-
-            '7': -RRB-
-            '8': .
-            '9': ':'
-            '10': ADD
-            '11': AFX
-            '12': CC
-            '13': CD
-            '14': DT
-            '15': EX
-            '16': FW
-            '17': HYPH
-            '18': IN
-            '19': JJ
-            '20': JJR
-            '21': JJS
-            '22': LS
-            '23': MD
-            '24': NFP
-            '25': NN
-            '26': NNP
-            '27': NNPS
-            '28': NNS
-            '29': PDT
-            '30': POS
-            '31': PRP
-            '32': PRP$
-            '33': RB
-            '34': RBR
-            '35': RBS
-            '36': RP
-            '37': SYM
-            '38': TO
-            '39': UH
-            '40': VB
-            '41': VBD
-            '42': VBG
-            '43': VBN
-            '44': VBP
-            '45': VBZ
-            '46': VERB
-            '47': WDT
-            '48': WP
-            '49': WP$
-            '50': WRB
+            '4': ','
+            '5': -LRB-
+            '6': -RRB-
+            '7': .
+            '8': ':'
+            '9': ADD
+            '10': AFX
+            '11': CC
+            '12': CD
+            '13': DT
+            '14': EX
+            '15': FW
+            '16': HYPH
+            '17': IN
+            '18': JJ
+            '19': JJR
+            '20': JJS
+            '21': LS
+            '22': MD
+            '23': NFP
+            '24': NN
+            '25': NNP
+            '26': NNPS
+            '27': NNS
+            '28': PDT
+            '29': POS
+            '30': PRP
+            '31': PRP$
+            '32': RB
+            '33': RBR
+            '34': RBS
+            '35': RP
+            '36': SYM
+            '37': TO
+            '38': UH
+            '39': VB
+            '40': VBD
+            '41': VBG
+            '42': VBN
+            '43': VBP
+            '44': VBZ
+            '45': WDT
+            '46': WP
+            '47': WP$
+            '48': WRB
     - name: parse_tree
       dtype: string
     - name: predicate_lemmas
@@ -501,16 +501,49 @@ dataset_info:
         length: 3
   splits:
   - name: train
-    num_bytes: 174173192
-    num_examples: 10539
+    num_bytes: 112145127
+    num_examples: 1940
   - name: validation
-    num_bytes: 24264804
-    num_examples: 1370
+    num_bytes: 14104258
+    num_examples: 222
   - name: test
-    num_bytes: 18254144
-    num_examples: 1200
-  download_size: 193644139
-  dataset_size: 216692140
+    num_bytes: 14696106
+    num_examples: 222
+  download_size: 21191727
+  dataset_size: 140945491
+configs:
+- config_name: arabic_v4
+  data_files:
+  - split: train
+    path: arabic_v4/train-*
+  - split: validation
+    path: arabic_v4/validation-*
+  - split: test
+    path: arabic_v4/test-*
+- config_name: chinese_v4
+  data_files:
+  - split: train
+    path: chinese_v4/train-*
+  - split: validation
+    path: chinese_v4/validation-*
+  - split: test
+    path: chinese_v4/test-*
+- config_name: english_v12
+  data_files:
+  - split: train
+    path: english_v12/train-*
+  - split: validation
+    path: english_v12/validation-*
+  - split: test
+    path: english_v12/test-*
+- config_name: english_v4
+  data_files:
+  - split: train
+    path: english_v4/train-*
+  - split: validation
+    path: english_v4/validation-*
+  - split: test
+    path: english_v4/test-*
 ---
 
 # Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0

arabic_v4/test-00000-of-00001.parquet

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+size 1201311

arabic_v4/train-00000-of-00001.parquet

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+size 10360827

arabic_v4/validation-00000-of-00001.parquet

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+size 1192792

chinese_v4/test-00000-of-00001.parquet

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+size 1272387

chinese_v4/train-00000-of-00001.parquet

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chinese_v4/validation-00000-of-00001.parquet

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+size 1481949

conll2012_ontonotesv5.py

@@ -1,816 +0,0 @@
-# coding=utf-8
-# 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.
-"""CoNLL2012 shared task data based on OntoNotes 5.0"""
-
-import glob
-import os
-from collections import defaultdict
-from typing import DefaultDict, Iterator, List, Optional, Tuple
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{pradhan-etal-2013-towards,
-    title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
-    author = {Pradhan, Sameer  and
-      Moschitti, Alessandro  and
-      Xue, Nianwen  and
-      Ng, Hwee Tou  and
-      Bj{\"o}rkelund, Anders  and
-      Uryupina, Olga  and
-      Zhang, Yuchen  and
-      Zhong, Zhi},
-    booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
-    month = aug,
-    year = "2013",
-    address = "Sofia, Bulgaria",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/W13-3516",
-    pages = "143--152",
-}
-
-Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, \
-Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, \
-Mohammed El-Bachouti, Robert Belvin, Ann Houston. \
-OntoNotes Release 5.0 LDC2013T19. \
-Web Download. Philadelphia: Linguistic Data Consortium, 2013.
-"""
-
-_DESCRIPTION = """\
-OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
-multilingual corpus manually annotated with syntactic, semantic and discourse information.
-
-This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
-It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
-
-The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
-
-See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
-
-For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
-"""
-
-_URL = "https://data.mendeley.com/public-files/datasets/zmycy7t9h9/files/b078e1c4-f7a4-4427-be7f-9389967831ef/file_downloaded"
-
-
-class Conll2012Ontonotesv5Config(datasets.BuilderConfig):
-    """BuilderConfig for the CoNLL formatted OntoNotes dataset."""
-
-    def __init__(self, language=None, conll_version=None, **kwargs):
-        """BuilderConfig for the CoNLL formatted OntoNotes dataset.
-
-        Args:
-          language: string, one of the language {"english", "chinese", "arabic"} .
-          conll_version: string, "v4" or "v12". Note there is only English v12.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        assert language in ["english", "chinese", "arabic"]
-        assert conll_version in ["v4", "v12"]
-        if conll_version == "v12":
-            assert language == "english"
-        super(Conll2012Ontonotesv5Config, self).__init__(
-            name=f"{language}_{conll_version}",
-            description=f"{conll_version} of CoNLL formatted OntoNotes dataset for {language}.",
-            version=datasets.Version("1.0.0"),  # hf dataset script version
-            **kwargs,
-        )
-        self.language = language
-        self.conll_version = conll_version
-
-
-class Conll2012Ontonotesv5(datasets.GeneratorBasedBuilder):
-    """The CoNLL formatted OntoNotes dataset."""
-
-    BUILDER_CONFIGS = [
-        Conll2012Ontonotesv5Config(
-            language=lang,
-            conll_version="v4",
-        )
-        for lang in ["english", "chinese", "arabic"]
-    ] + [
-        Conll2012Ontonotesv5Config(
-            language="english",
-            conll_version="v12",
-        )
-    ]
-
-    def _info(self):
-        lang = self.config.language
-        conll_version = self.config.conll_version
-        if lang == "arabic":
-            pos_tag_feature = datasets.Value("string")
-        else:
-            tag_set = _POS_TAGS[f"{lang}_{conll_version}"]
-            pos_tag_feature = datasets.ClassLabel(num_classes=len(tag_set), names=tag_set)
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "document_id": datasets.Value("string"),
-                    "sentences": [
-                        {
-                            "part_id": datasets.Value("int32"),
-                            "words": datasets.Sequence(datasets.Value("string")),
-                            "pos_tags": datasets.Sequence(pos_tag_feature),
-                            "parse_tree": datasets.Value("string"),
-                            "predicate_lemmas": datasets.Sequence(datasets.Value("string")),
-                            "predicate_framenet_ids": datasets.Sequence(datasets.Value("string")),
-                            "word_senses": datasets.Sequence(datasets.Value("float32")),
-                            "speaker": datasets.Value("string"),
-                            "named_entities": datasets.Sequence(
-                                datasets.ClassLabel(num_classes=37, names=_NAMED_ENTITY_TAGS)
-                            ),
-                            "srl_frames": [
-                                {
-                                    "verb": datasets.Value("string"),
-                                    "frames": datasets.Sequence(datasets.Value("string")),
-                                }
-                            ],
-                            "coref_spans": datasets.Sequence(datasets.Sequence(datasets.Value("int32"), length=3)),
-                        }
-                    ],
-                }
-            ),
-            homepage="https://conll.cemantix.org/2012/introduction.html",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        lang = self.config.language
-        conll_version = self.config.conll_version
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, f"conll-2012/{conll_version}/data")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"train/data/{lang}")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"development/data/{lang}")},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"test/data/{lang}")},
-            ),
-        ]
-
-    def _generate_examples(self, conll_files_directory):
-        conll_files = sorted(glob.glob(os.path.join(conll_files_directory, "**/*gold_conll"), recursive=True))
-        for idx, conll_file in enumerate(conll_files):
-            sentences = []
-            for sent in Ontonotes().sentence_iterator(conll_file):
-                document_id = sent.document_id
-                sentences.append(
-                    {
-                        "part_id": sent.sentence_id,  # should be part id, according to https://conll.cemantix.org/2012/data.html
-                        "words": sent.words,
-                        "pos_tags": sent.pos_tags,
-                        "parse_tree": sent.parse_tree,
-                        "predicate_lemmas": sent.predicate_lemmas,
-                        "predicate_framenet_ids": sent.predicate_framenet_ids,
-                        "word_senses": sent.word_senses,
-                        "speaker": sent.speakers[0],
-                        "named_entities": sent.named_entities,
-                        "srl_frames": [{"verb": f[0], "frames": f[1]} for f in sent.srl_frames],
-                        "coref_spans": [(c[0], *c[1]) for c in sent.coref_spans],
-                    }
-                )
-            yield idx, {"document_id": document_id, "sentences": sentences}
-
-
-# --------------------------------------------------------------------------------------------------------
-# Tag set
-_NAMED_ENTITY_TAGS = [
-    "O",  # out of named entity
-    "B-PERSON",
-    "I-PERSON",
-    "B-NORP",
-    "I-NORP",
-    "B-FAC",  # FACILITY
-    "I-FAC",
-    "B-ORG",  # ORGANIZATION
-    "I-ORG",
-    "B-GPE",
-    "I-GPE",
-    "B-LOC",
-    "I-LOC",
-    "B-PRODUCT",
-    "I-PRODUCT",
-    "B-DATE",
-    "I-DATE",
-    "B-TIME",
-    "I-TIME",
-    "B-PERCENT",
-    "I-PERCENT",
-    "B-MONEY",
-    "I-MONEY",
-    "B-QUANTITY",
-    "I-QUANTITY",
-    "B-ORDINAL",
-    "I-ORDINAL",
-    "B-CARDINAL",
-    "I-CARDINAL",
-    "B-EVENT",
-    "I-EVENT",
-    "B-WORK_OF_ART",
-    "I-WORK_OF_ART",
-    "B-LAW",
-    "I-LAW",
-    "B-LANGUAGE",
-    "I-LANGUAGE",
-]
-
-_POS_TAGS = {
-    "english_v4": [
-        "XX",  # missing
-        "``",
-        "$",
-        "''",
-        ",",
-        "-LRB-",  # (
-        "-RRB-",  # )
-        ".",
-        ":",
-        "ADD",
-        "AFX",
-        "CC",
-        "CD",
-        "DT",
-        "EX",
-        "FW",
-        "HYPH",
-        "IN",
-        "JJ",
-        "JJR",
-        "JJS",
-        "LS",
-        "MD",
-        "NFP",
-        "NN",
-        "NNP",
-        "NNPS",
-        "NNS",
-        "PDT",
-        "POS",
-        "PRP",
-        "PRP$",
-        "RB",
-        "RBR",
-        "RBS",
-        "RP",
-        "SYM",
-        "TO",
-        "UH",
-        "VB",
-        "VBD",
-        "VBG",
-        "VBN",
-        "VBP",
-        "VBZ",
-        "WDT",
-        "WP",
-        "WP$",
-        "WRB",
-    ],  # 49
-    "english_v12": [
-        "XX",  # misssing
-        "``",
-        "$",
-        "''",
-        "*",
-        ",",
-        "-LRB-",  # (
-        "-RRB-",  # )
-        ".",
-        ":",
-        "ADD",
-        "AFX",
-        "CC",
-        "CD",
-        "DT",
-        "EX",
-        "FW",
-        "HYPH",
-        "IN",
-        "JJ",
-        "JJR",
-        "JJS",
-        "LS",
-        "MD",
-        "NFP",
-        "NN",
-        "NNP",
-        "NNPS",
-        "NNS",
-        "PDT",
-        "POS",
-        "PRP",
-        "PRP$",
-        "RB",
-        "RBR",
-        "RBS",
-        "RP",
-        "SYM",
-        "TO",
-        "UH",
-        "VB",
-        "VBD",
-        "VBG",
-        "VBN",
-        "VBP",
-        "VBZ",
-        "VERB",
-        "WDT",
-        "WP",
-        "WP$",
-        "WRB",
-    ],  # 51
-    "chinese_v4": [
-        "X",  # missing
-        "AD",
-        "AS",
-        "BA",
-        "CC",
-        "CD",
-        "CS",
-        "DEC",
-        "DEG",
-        "DER",
-        "DEV",
-        "DT",
-        "ETC",
-        "FW",
-        "IJ",
-        "INF",
-        "JJ",
-        "LB",
-        "LC",
-        "M",
-        "MSP",
-        "NN",
-        "NR",
-        "NT",
-        "OD",
-        "ON",
-        "P",
-        "PN",
-        "PU",
-        "SB",
-        "SP",
-        "URL",
-        "VA",
-        "VC",
-        "VE",
-        "VV",
-    ],  # 36
-}
-
-# --------------------------------------------------------------------------------------------------------
-# The CoNLL(2012) file reader
-# Modified the original code to get rid of extra package dependency.
-# Original code: https://github.com/allenai/allennlp-models/blob/main/allennlp_models/common/ontonotes.py
-
-
-class OntonotesSentence:
-    """
-    A class representing the annotations available for a single CONLL formatted sentence.
-    # Parameters
-    document_id : `str`
-        This is a variation on the document filename
-    sentence_id : `int`
-        The integer ID of the sentence within a document.
-    words : `List[str]`
-        This is the tokens as segmented/tokenized in the bank.
-    pos_tags : `List[str]`
-        This is the Penn-Treebank-style part of speech. When parse information is missing,
-        all parts of speech except the one for which there is some sense or proposition
-        annotation are marked with a XX tag. The verb is marked with just a VERB tag.
-    parse_tree : `nltk.Tree`
-        An nltk Tree representing the parse. It includes POS tags as pre-terminal nodes.
-        When the parse information is missing, the parse will be `None`.
-    predicate_lemmas : `List[Optional[str]]`
-        The predicate lemma of the words for which we have semantic role
-        information or word sense information. All other indices are `None`.
-    predicate_framenet_ids : `List[Optional[int]]`
-        The PropBank frameset ID of the lemmas in `predicate_lemmas`, or `None`.
-    word_senses : `List[Optional[float]]`
-        The word senses for the words in the sentence, or `None`. These are floats
-        because the word sense can have values after the decimal, like `1.1`.
-    speakers : `List[Optional[str]]`
-        The speaker information for the words in the sentence, if present, or `None`
-        This is the speaker or author name where available. Mostly in Broadcast Conversation
-        and Web Log data. When not available the rows are marked with an "-".
-    named_entities : `List[str]`
-        The BIO tags for named entities in the sentence.
-    srl_frames : `List[Tuple[str, List[str]]]`
-        A dictionary keyed by the verb in the sentence for the given
-        Propbank frame labels, in a BIO format.
-    coref_spans : `Set[TypedSpan]`
-        The spans for entity mentions involved in coreference resolution within the sentence.
-        Each element is a tuple composed of (cluster_id, (start_index, end_index)). Indices
-        are `inclusive`.
-    """
-
-    def __init__(
-        self,
-        document_id: str,
-        sentence_id: int,
-        words: List[str],
-        pos_tags: List[str],
-        parse_tree: Optional[str],
-        predicate_lemmas: List[Optional[str]],
-        predicate_framenet_ids: List[Optional[str]],
-        word_senses: List[Optional[float]],
-        speakers: List[Optional[str]],
-        named_entities: List[str],
-        srl_frames: List[Tuple[str, List[str]]],
-        coref_spans,
-    ) -> None:
-
-        self.document_id = document_id
-        self.sentence_id = sentence_id
-        self.words = words
-        self.pos_tags = pos_tags
-        self.parse_tree = parse_tree
-        self.predicate_lemmas = predicate_lemmas
-        self.predicate_framenet_ids = predicate_framenet_ids
-        self.word_senses = word_senses
-        self.speakers = speakers
-        self.named_entities = named_entities
-        self.srl_frames = srl_frames
-        self.coref_spans = coref_spans
-
-
-class Ontonotes:
-    """
-    This `DatasetReader` is designed to read in the English OntoNotes v5.0 data
-    in the format used by the CoNLL 2011/2012 shared tasks. In order to use this
-    Reader, you must follow the instructions provided [here (v12 release):]
-    (https://cemantix.org/data/ontonotes.html), which will allow you to download
-    the CoNLL style annotations for the  OntoNotes v5.0 release -- LDC2013T19.tgz
-    obtained from LDC.
-    Once you have run the scripts on the extracted data, you will have a folder
-    structured as follows:
-    ```
-    conll-formatted-ontonotes-5.0/
-     ── data
-       ├── development
-           └── data
-               └── english
-                   └── annotations
-                       ├── bc
-                       ├── bn
-                       ├── mz
-                       ├── nw
-                       ├── pt
-                       ├── tc
-                       └── wb
-       ├── test
-           └── data
-               └── english
-                   └── annotations
-                       ├── bc
-                       ├── bn
-                       ├── mz
-                       ├── nw
-                       ├── pt
-                       ├── tc
-                       └── wb
-       └── train
-           └── data
-               └── english
-                   └── annotations
-                       ├── bc
-                       ├── bn
-                       ├── mz
-                       ├── nw
-                       ├── pt
-                       ├── tc
-                       └── wb
-    ```
-    The file path provided to this class can then be any of the train, test or development
-    directories(or the top level data directory, if you are not utilizing the splits).
-    The data has the following format, ordered by column.
-    1.  Document ID : `str`
-        This is a variation on the document filename
-    2.  Part number : `int`
-        Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
-    3.  Word number : `int`
-        This is the word index of the word in that sentence.
-    4.  Word : `str`
-        This is the token as segmented/tokenized in the Treebank. Initially the `*_skel` file
-        contain the placeholder [WORD] which gets replaced by the actual token from the
-        Treebank which is part of the OntoNotes release.
-    5.  POS Tag : `str`
-        This is the Penn Treebank style part of speech. When parse information is missing,
-        all part of speeches except the one for which there is some sense or proposition
-        annotation are marked with a XX tag. The verb is marked with just a VERB tag.
-    6.  Parse bit : `str`
-        This is the bracketed structure broken before the first open parenthesis in the parse,
-        and the word/part-of-speech leaf replaced with a `*`. When the parse information is
-        missing, the first word of a sentence is tagged as `(TOP*` and the last word is tagged
-        as `*)` and all intermediate words are tagged with a `*`.
-    7.  Predicate lemma : `str`
-        The predicate lemma is mentioned for the rows for which we have semantic role
-        information or word sense information. All other rows are marked with a "-".
-    8.  Predicate Frameset ID : `int`
-        The PropBank frameset ID of the predicate in Column 7.
-    9.  Word sense : `float`
-        This is the word sense of the word in Column 3.
-    10. Speaker/Author : `str`
-        This is the speaker or author name where available. Mostly in Broadcast Conversation
-        and Web Log data. When not available the rows are marked with an "-".
-    11. Named Entities : `str`
-        These columns identifies the spans representing various named entities. For documents
-        which do not have named entity annotation, each line is represented with an `*`.
-    12. Predicate Arguments : `str`
-        There is one column each of predicate argument structure information for the predicate
-        mentioned in Column 7. If there are no predicates tagged in a sentence this is a
-        single column with all rows marked with an `*`.
-    -1. Co-reference : `str`
-        Co-reference chain information encoded in a parenthesis structure. For documents that do
-         not have co-reference annotations, each line is represented with a "-".
-    """
-
-    def dataset_iterator(self, file_path: str) -> Iterator[OntonotesSentence]:
-        """
-        An iterator over the entire dataset, yielding all sentences processed.
-        """
-        for conll_file in self.dataset_path_iterator(file_path):
-            yield from self.sentence_iterator(conll_file)
-
-    @staticmethod
-    def dataset_path_iterator(file_path: str) -> Iterator[str]:
-        """
-        An iterator returning file_paths in a directory
-        containing CONLL-formatted files.
-        """
-        for root, _, files in list(os.walk(file_path)):
-            for data_file in sorted(files):
-                # These are a relic of the dataset pre-processing. Every
-                # file will be duplicated - one file called filename.gold_skel
-                # and one generated from the preprocessing called filename.gold_conll.
-                if not data_file.endswith("gold_conll"):
-                    continue
-
-                yield os.path.join(root, data_file)
-
-    def dataset_document_iterator(self, file_path: str) -> Iterator[List[OntonotesSentence]]:
-        """
-        An iterator over CONLL formatted files which yields documents, regardless
-        of the number of document annotations in a particular file. This is useful
-        for conll data which has been preprocessed, such as the preprocessing which
-        takes place for the 2012 CONLL Coreference Resolution task.
-        """
-        with open(file_path, "r", encoding="utf8") as open_file:
-            conll_rows = []
-            document: List[OntonotesSentence] = []
-            for line in open_file:
-                line = line.strip()
-                if line != "" and not line.startswith("#"):
-                    # Non-empty line. Collect the annotation.
-                    conll_rows.append(line)
-                else:
-                    if conll_rows:
-                        document.append(self._conll_rows_to_sentence(conll_rows))
-                        conll_rows = []
-                if line.startswith("#end document"):
-                    yield document
-                    document = []
-            if document:
-                # Collect any stragglers or files which might not
-                # have the '#end document' format for the end of the file.
-                yield document
-
-    def sentence_iterator(self, file_path: str) -> Iterator[OntonotesSentence]:
-        """
-        An iterator over the sentences in an individual CONLL formatted file.
-        """
-        for document in self.dataset_document_iterator(file_path):
-            for sentence in document:
-                yield sentence
-
-    def _conll_rows_to_sentence(self, conll_rows: List[str]) -> OntonotesSentence:
-        document_id: str = None
-        sentence_id: int = None
-        # The words in the sentence.
-        sentence: List[str] = []
-        # The pos tags of the words in the sentence.
-        pos_tags: List[str] = []
-        # the pieces of the parse tree.
-        parse_pieces: List[str] = []
-        # The lemmatised form of the words in the sentence which
-        # have SRL or word sense information.
-        predicate_lemmas: List[str] = []
-        # The FrameNet ID of the predicate.
-        predicate_framenet_ids: List[str] = []
-        # The sense of the word, if available.
-        word_senses: List[float] = []
-        # The current speaker, if available.
-        speakers: List[str] = []
-
-        verbal_predicates: List[str] = []
-        span_labels: List[List[str]] = []
-        current_span_labels: List[str] = []
-
-        # Cluster id -> List of (start_index, end_index) spans.
-        clusters: DefaultDict[int, List[Tuple[int, int]]] = defaultdict(list)
-        # Cluster id -> List of start_indices which are open for this id.
-        coref_stacks: DefaultDict[int, List[int]] = defaultdict(list)
-
-        for index, row in enumerate(conll_rows):
-            conll_components = row.split()
-
-            document_id = conll_components[0]
-            sentence_id = int(conll_components[1])
-            word = conll_components[3]
-            pos_tag = conll_components[4]
-            parse_piece = conll_components[5]
-
-            # Replace brackets in text and pos tags
-            # with a different token for parse trees.
-            if pos_tag != "XX" and word != "XX":
-                if word == "(":
-                    parse_word = "-LRB-"
-                elif word == ")":
-                    parse_word = "-RRB-"
-                else:
-                    parse_word = word
-                if pos_tag == "(":
-                    pos_tag = "-LRB-"
-                if pos_tag == ")":
-                    pos_tag = "-RRB-"
-                (left_brackets, right_hand_side) = parse_piece.split("*")
-                # only keep ')' if there are nested brackets with nothing in them.
-                right_brackets = right_hand_side.count(")") * ")"
-                parse_piece = f"{left_brackets} ({pos_tag} {parse_word}) {right_brackets}"
-            else:
-                # There are some bad annotations in the CONLL data.
-                # They contain no information, so to make this explicit,
-                # we just set the parse piece to be None which will result
-                # in the overall parse tree being None.
-                parse_piece = None
-
-            lemmatised_word = conll_components[6]
-            framenet_id = conll_components[7]
-            word_sense = conll_components[8]
-            speaker = conll_components[9]
-
-            if not span_labels:
-                # If this is the first word in the sentence, create
-                # empty lists to collect the NER and SRL BIO labels.
-                # We can't do this upfront, because we don't know how many
-                # components we are collecting, as a sentence can have
-                # variable numbers of SRL frames.
-                span_labels = [[] for _ in conll_components[10:-1]]
-                # Create variables representing the current label for each label
-                # sequence we are collecting.
-                current_span_labels = [None for _ in conll_components[10:-1]]
-
-            self._process_span_annotations_for_word(conll_components[10:-1], span_labels, current_span_labels)
-
-            # If any annotation marks this word as a verb predicate,
-            # we need to record its index. This also has the side effect
-            # of ordering the verbal predicates by their location in the
-            # sentence, automatically aligning them with the annotations.
-            word_is_verbal_predicate = any("(V" in x for x in conll_components[11:-1])
-            if word_is_verbal_predicate:
-                verbal_predicates.append(word)
-
-            self._process_coref_span_annotations_for_word(conll_components[-1], index, clusters, coref_stacks)
-
-            sentence.append(word)
-            pos_tags.append(pos_tag)
-            parse_pieces.append(parse_piece)
-            predicate_lemmas.append(lemmatised_word if lemmatised_word != "-" else None)
-            predicate_framenet_ids.append(framenet_id if framenet_id != "-" else None)
-            word_senses.append(float(word_sense) if word_sense != "-" else None)
-            speakers.append(speaker if speaker != "-" else None)
-
-        named_entities = span_labels[0]
-        srl_frames = [(predicate, labels) for predicate, labels in zip(verbal_predicates, span_labels[1:])]
-
-        if all(parse_pieces):
-            parse_tree = "".join(parse_pieces)
-        else:
-            parse_tree = None
-        coref_span_tuples = {(cluster_id, span) for cluster_id, span_list in clusters.items() for span in span_list}
-        return OntonotesSentence(
-            document_id,
-            sentence_id,
-            sentence,
-            pos_tags,
-            parse_tree,
-            predicate_lemmas,
-            predicate_framenet_ids,
-            word_senses,
-            speakers,
-            named_entities,
-            srl_frames,
-            coref_span_tuples,
-        )
-
-    @staticmethod
-    def _process_coref_span_annotations_for_word(
-        label: str,
-        word_index: int,
-        clusters: DefaultDict[int, List[Tuple[int, int]]],
-        coref_stacks: DefaultDict[int, List[int]],
-    ) -> None:
-        """
-        For a given coref label, add it to a currently open span(s), complete a span(s) or
-        ignore it, if it is outside of all spans. This method mutates the clusters and coref_stacks
-        dictionaries.
-        # Parameters
-        label : `str`
-            The coref label for this word.
-        word_index : `int`
-            The word index into the sentence.
-        clusters : `DefaultDict[int, List[Tuple[int, int]]]`
-            A dictionary mapping cluster ids to lists of inclusive spans into the
-            sentence.
-        coref_stacks : `DefaultDict[int, List[int]]`
-            Stacks for each cluster id to hold the start indices of active spans (spans
-            which we are inside of when processing a given word). Spans with the same id
-            can be nested, which is why we collect these opening spans on a stack, e.g:
-            [Greg, the baker who referred to [himself]_ID1 as 'the bread man']_ID1
-        """
-        if label != "-":
-            for segment in label.split("|"):
-                # The conll representation of coref spans allows spans to
-                # overlap. If spans end or begin at the same word, they are
-                # separated by a "|".
-                if segment[0] == "(":
-                    # The span begins at this word.
-                    if segment[-1] == ")":
-                        # The span begins and ends at this word (single word span).
-                        cluster_id = int(segment[1:-1])
-                        clusters[cluster_id].append((word_index, word_index))
-                    else:
-                        # The span is starting, so we record the index of the word.
-                        cluster_id = int(segment[1:])
-                        coref_stacks[cluster_id].append(word_index)
-                else:
-                    # The span for this id is ending, but didn't start at this word.
-                    # Retrieve the start index from the document state and
-                    # add the span to the clusters for this id.
-                    cluster_id = int(segment[:-1])
-                    start = coref_stacks[cluster_id].pop()
-                    clusters[cluster_id].append((start, word_index))
-
-    @staticmethod
-    def _process_span_annotations_for_word(
-        annotations: List[str],
-        span_labels: List[List[str]],
-        current_span_labels: List[Optional[str]],
-    ) -> None:
-        """
-        Given a sequence of different label types for a single word and the current
-        span label we are inside, compute the BIO tag for each label and append to a list.
-        # Parameters
-        annotations : `List[str]`
-            A list of labels to compute BIO tags for.
-        span_labels : `List[List[str]]`
-            A list of lists, one for each annotation, to incrementally collect
-            the BIO tags for a sequence.
-        current_span_labels : `List[Optional[str]]`
-            The currently open span per annotation type, or `None` if there is no open span.
-        """
-        for annotation_index, annotation in enumerate(annotations):
-            # strip all bracketing information to
-            # get the actual propbank label.
-            label = annotation.strip("()*")
-
-            if "(" in annotation:
-                # Entering into a span for a particular semantic role label.
-                # We append the label and set the current span for this annotation.
-                bio_label = "B-" + label
-                span_labels[annotation_index].append(bio_label)
-                current_span_labels[annotation_index] = label
-            elif current_span_labels[annotation_index] is not None:
-                # If there's no '(' token, but the current_span_label is not None,
-                # then we are inside a span.
-                bio_label = "I-" + current_span_labels[annotation_index]
-                span_labels[annotation_index].append(bio_label)
-            else:
-                # We're outside a span.
-                span_labels[annotation_index].append("O")
-            # Exiting a span, so we reset the current span label for this annotation.
-            if ")" in annotation:
-                current_span_labels[annotation_index] = None

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