Datasets:

cjvt
/

solar3

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- other
-language:
-- sl
-license:
-- cc-by-nc-sa-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- text2text-generation
-- other
-task_ids: []
-pretty_name: solar3
-tags:
-- grammatical-error-correction
-- other-token-classification-of-text-errors
----
-# Dataset Card for solar3
-### Dataset Summary
-Šolar* is a developmental corpus of 5485 school texts (e.g., essays), written by students in Slovenian secondary schools
-(age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade.
-Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the
-document available at https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Smernice-za-oznacevanje-korpusa-Solar_V1.1.pdf (in Slovenian).
-\(*) pronounce "š" as "sh" in "shoe".
-By default the dataset is provided at **sentence-level** (125867 instances): each instance contains a source (the original) and a target (the corrected) sentence. Note that either the source or the target sentence in an instance may be missing - this usually happens when a source sentence is marked as redundant or when a new sentence is added by the teacher. Additionally, a source or a target sentence may appear in multiple instances - for example, this happens when one sentence gets divided into multiple sentences.
-There is also an option to aggregate the instances at the **document-level** or **paragraph-level**
-by explicitly providing the correct config:
-```
-datasets.load_dataset("cjvt/solar3", "paragraph_level")`
-datasets.load_dataset("cjvt/solar3", "document_level")`
-```
-### Supported Tasks and Leaderboards
-Error correction, e.g., at token/sequence level, as token/sequence classification or text2text generation.
-### Languages
-Slovenian.
-## Dataset Structure
-### Data Instances
-A sample instance from the dataset:
-```json
-{
-	'id_doc': 'solar1',
-	'doc_title': 'KUS-G-slo-1-GO-E-2009-10001',
-	'is_manually_validated': True,
-	'src_tokens': ['”', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', '”', ',', 'izreče', 'Antigona', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
-	'src_ling_annotations': {
-		# truncated for conciseness
-		'lemma': ['”', 'ne', 'da', 'sovražiti', ...],
-		'ana': ['mte:U', 'mte:L', 'mte:Vd', ...],
-		'msd': ['UPosTag=PUNCT', 'UPosTag=PART|Polarity=Neg', 'UPosTag=SCONJ', ...],
-		'ne_tag': [..., 'O', 'B-PER', 'O', ...],
-        'space_after': [False, True, True, False, ...]
-	},
-	'tgt_tokens': ['„', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', ',', '”', 'izreče', 'Antigona', 'sebi', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
-	# omitted for conciseness, the format is the same as in 'src_ling_annotations'
-	'tgt_ling_annotations': {...},
-	'corrections': [
-		{'idx_src': [0], 'idx_tgt': [0], 'corr_types': ['Z/LOČ/nerazvrščeno']},
-		{'idx_src': [10, 11], 'idx_tgt': [10, 11], 'corr_types': ['Z/LOČ/nerazvrščeno']},
-		{'idx_src': [], 'idx_tgt': [14], 'corr_types': ['O/KAT/povratnost']}
-	]
-}
-```
-The instance represents a correction in the document 'solar1' (`id_doc`), which were manually assigned/validated (`is_manually_validated`). More concretely, the source sentence contains three errors (as indicated by three elements in `corrections`):
-- a punctuation change: '”' -> '„';
-- a punctuation change: ['”', ','] -> [',', '”'] (i.e. comma inside the quote, not outside);
-- addition of a new word: 'sebi'.
-### Data Fields
-- `id_doc`: a string containing the identifying name of the document in which the sentence appears;
-- `doc_title`: a string containing the assigned document title;
-- `is_manually_validated`: a bool indicating whether the document in which the sentence appears was reviewed by a teacher;
-- `src_tokens`: words in the source sentence (`[]` if there is no source sentence);
-- `src_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the source tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token;
-- `tgt_tokens`: words in the target sentence (`[]` if there is no target sentence);
-- `tgt_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the target tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token;
-- `corrections`: a list of the corrections, with each correction represented with a dictionary, containing the indices of the source tokens involved (`idx_src`), target tokens involved (`idx_tgt`), and the categories of the corrections made (`corr_types`). Please note that there can be multiple assigned categories for one annotated correction, in which case `len(corr_types) > 1`.
-## Dataset Creation
-The Developmental corpus Šolar consists of 5,485 texts written by students in Slovenian secondary schools (age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade. The information on school (elementary or secondary), subject, level (grade or year), type of text, region, and date of production is provided for each text. School essays form the majority of the corpus while other material includes texts created during lessons, such as text recapitulations or descriptions, examples of formal applications, etc.
-Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the attached document (in Slovenian). Teacher corrections were part of the original files and reflect real classroom situations of essay marking. Corrections were then inserted into texts by annotators and subsequently categorized. Due to the annotations being gathered in a practical (i.e. classroom) setting, only the most relevant errors may sometimes be annotated, e.g., not all incorrectly placed commas are annotated if there is a bigger issue in the text.
-## Additional Information
-### Dataset Curators
-Špela Arhar Holdt; et al. (please see http://hdl.handle.net/11356/1589 for the full list)
-### Licensing Information
-CC BY-NC-SA 4.0.
-### Citation Information
-```
-@misc{solar3,
-  title = {Developmental corpus {\v S}olar 3.0},
-  author = {Arhar Holdt, {\v S}pela and Rozman, Tadeja and Stritar Ku{\v c}uk, Mojca and Krek, Simon and Krap{\v s} Vodopivec, Irena and Stabej, Marko and Pori, Eva and Goli, Teja and Lavri{\v c}, Polona and Laskowski, Cyprian and Kocjan{\v c}i{\v c}, Polonca and Klemenc, Bojan and Krsnik, Luka and Kosem, Iztok},
-   url = {http://hdl.handle.net/11356/1589},
-   note = {Slovenian language resource repository {CLARIN}.{SI}},
-   year = {2022}
- }
-```
-### Contributions
-Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

dataset_infos.json DELETED Viewed

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-import logging
-import os
-import re
-import xml.etree.ElementTree as ET
-from copy import deepcopy
-from itertools import groupby
-from typing import Optional
-import datasets
-_CITATION = """\
-@misc{solar3.0,
-    title = {Developmental corpus {\v S}olar 3.0},
-    author = {Arhar Holdt, {\v S}pela and Rozman, Tadeja and Stritar Ku{\v c}uk, Mojca and Krek, Simon and Krap{\v s} Vodopivec, Irena and Stabej, Marko and Pori, Eva and Goli, Teja and Lavri{\v c}, Polona and Laskowski, Cyprian and Kocjan{\v c}i{\v c}, Polonca and Klemenc, Bojan and Krsnik, Luka and Kosem, Iztok},
-    url = {http://hdl.handle.net/11356/1589},
-    note = {Slovenian language resource repository {CLARIN}.{SI}},
-    year = {2022}
-}
-"""
-_DESCRIPTION = """\
-Šolar is a developmental corpus of 5485 school texts (e.g., essays), written by students in Slovenian secondary schools
-(age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade.
-Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the
-document available at https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Smernice-za-oznacevanje-korpusa-Solar_V1.1.pdf (in Slovenian).
-"""
-_HOMEPAGE = "http://hdl.handle.net/11356/1589"
-_LICENSE = "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
-_URLS = {
-    "solar_tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Solar.TEI.zip"
-}
-XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
-def namespace(element):
-    # https://stackoverflow.com/a/12946675
-    m = re.match(r'\{.*\}', element.tag)
-    return m.group(0) if m else ''
-def resolve_element(tag_el, ne_tag: Optional[str] = "O"):
-    if not tag_el.tag.endswith(("w", "pc", "seg")):
-        return []
-    if tag_el.tag.endswith(("w", "pc")):
-        form = tag_el.text.strip()
-        lemma = tag_el.text.strip() if tag_el.tag.endswith("pc") else tag_el.attrib["lemma"]
-        ana = tag_el.attrib["ana"]  # JOS/MTE specifications
-        msd = tag_el.attrib["msd"]  # UD specifications
-        ret_ne_tag = ne_tag
-        id_tag = tag_el.attrib[f"{XML_NAMESPACE}id"]
-        space_after = False if "join" in tag_el.attrib and tag_el.attrib["join"]=="right" else True
-        return [(id_tag, form, lemma, ana, msd, ret_ne_tag, space_after)]
-    # Named entities: words and punctuation nested directly below current element
-    elif tag_el.tag.endswith("seg"):
-        anns = []
-        ret_ne_tag = tag_el.attrib["subtype"].upper()
-        for idx_child, curr_child in enumerate(tag_el):
-            anns.extend(resolve_element(curr_child, ne_tag=f"B-{ret_ne_tag}" if idx_child == 0 else f"I-{ret_ne_tag}"))
-        return anns
-def extract_sent_id(tok_id):
-    # e.g., `extract_sent_id("#solar1s.3.2.44") == "solar1s.3.2"` or `extract_sent_id("solar1s.3.2.44") == "solar1s.3.2"`
-    _tok_id = tok_id[1:] if tok_id.startswith("#") else tok_id
-    return ".".join(_tok_id.split(".")[: -1])
-def find_involved_sents(correction_group_el):
-    src_sent_ids = set()
-    tgt_sent_ids = set()
-    for _curr_corr in correction_group_el:
-        sent_ids = list(map(lambda _tok_id: extract_sent_id(_tok_id),
-                            _curr_corr.attrib["target"].split(" ")))
-        for _s_id in sent_ids:
-            if "t" in _s_id:
-                tgt_sent_ids.add(_s_id)
-            else:
-                src_sent_ids.add(_s_id)
-    return sorted(list(src_sent_ids)), sorted(list(tgt_sent_ids))
-def read_data(data_path):
-    data = {}  # ID_sent -> sentence_metadata
-    tree = ET.parse(data_path)
-    root = tree.getroot()
-    NAMESPACE = namespace(root)
-    for curr_text in root.iterfind(f".//{NAMESPACE}div"):
-        id_text = curr_text.attrib[f"{XML_NAMESPACE}id"]
-        bibl_el = curr_text.find(f"{NAMESPACE}bibl")
-        if bibl_el is None:
-            text_title = "Unknown_title"
-            logging.warning(f"The following text does not have a 'bibl' element: {curr_text.attrib}. "
-                            f"Setting title to 'Unknown_title'")
-            is_manually_validated = False
-        else:
-            text_title = bibl_el.attrib["n"]
-            note_el = bibl_el.find(f"{NAMESPACE}note")
-            is_manually_validated = note_el.text == "DA"
-        for idx_par, curr_par in enumerate(curr_text.iterfind(f".//{NAMESPACE}p")):
-            for idx_sent, curr_sent in enumerate(curr_par.iterfind(f".//{NAMESPACE}s")):
-                id_sent = curr_sent.attrib[f"{XML_NAMESPACE}id"]
-                ids, forms, lemmas, msds, nes, spaces_after = [], [], [], [], [], []
-                msds_jos, msds_ud = [], []
-                for curr_el in curr_sent:
-                    curr_annotations = resolve_element(curr_el)
-                    for curr_ann in curr_annotations:
-                        ids.append(curr_ann[0])
-                        forms.append(curr_ann[1])
-                        lemmas.append(curr_ann[2])
-                        msds_jos.append(curr_ann[3])
-                        msds_ud.append(curr_ann[4])
-                        nes.append(curr_ann[5])
-                        spaces_after.append(curr_ann[6])
-                data[id_sent] = {
-                    "id_doc": id_text,
-                    "doc_title": text_title,
-                    "idx_par": idx_par,
-                    "id_token": ids, "form": forms, "lemma": lemmas, "ana": msds_jos, "msd": msds_ud, "ne_tag": nes, "space_after": spaces_after,
-                    "is_manually_validated": is_manually_validated
-                }
-    return data
-class Solar3(datasets.GeneratorBasedBuilder):
-    """Šolar is a developmental corpus of school texts (e.g., essays), annotated with metadata and (partially)
-    with teachers' corrections. """
-    VERSION = datasets.Version("3.0.2")
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="sentence_level", version=VERSION,
-                               description="Annotations at sentence-level."),
-        datasets.BuilderConfig(name="paragraph_level", version=VERSION,
-                               description="Annotations at paragraph-level."),
-        datasets.BuilderConfig(name="document_level", version=VERSION,
-                               description="Annotations at document-level."),
-    ]
-    DEFAULT_CONFIG_NAME = "sentence_level"  # default = annotations as provided in the original data
-    def _info(self):
-        features = datasets.Features(
-            {
-                "id_doc": datasets.Value("string"),
-                "doc_title": datasets.Value("string"),
-                "is_manually_validated": datasets.Value("bool"),
-                "src_tokens": datasets.Sequence(datasets.Value("string")),
-                "src_ling_annotations": {
-                    "lemma": datasets.Sequence(datasets.Value("string")),
-                    "ana": datasets.Sequence(datasets.Value("string")),
-                    "msd": datasets.Sequence(datasets.Value("string")),
-                    "ne_tag": datasets.Sequence(datasets.Value("string")),
-                    "space_after": datasets.Sequence(datasets.Value("bool"))
-                },
-                "tgt_tokens": datasets.Sequence(datasets.Value("string")),
-                "tgt_ling_annotations": {
-                    "lemma": datasets.Sequence(datasets.Value("string")),
-                    "ana": datasets.Sequence(datasets.Value("string")),
-                    "msd": datasets.Sequence(datasets.Value("string")),
-                    "ne_tag": datasets.Sequence(datasets.Value("string")),
-                    "space_after": datasets.Sequence(datasets.Value("bool"))
-                },
-                "corrections": [
-                    {
-                        "idx_src": datasets.Sequence(datasets.Value("int32")),
-                        "idx_tgt": datasets.Sequence(datasets.Value("int32")),
-                        "corr_types": datasets.Sequence(datasets.Value("string"))
-                    }
-                ]
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        urls = _URLS["solar_tei"]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "source_path": os.path.join(data_dir, "Solar.TEI", "solar-orig.xml"),
-                    "target_path": os.path.join(data_dir, "Solar.TEI", "solar-corr.xml"),
-                    "links_path": os.path.join(data_dir, "Solar.TEI", "solar-errs.xml")
-                }
-            )
-        ]
-    @staticmethod
-    def generate_sentences(source_path, target_path, links_path):
-        source_data = read_data(source_path)
-        target_data = read_data(target_path)
-        data = ET.parse(links_path)
-        root = data.getroot()
-        NAMESPACE = namespace(root)
-        for idx_corr, corrected_sent in enumerate(root.iterfind(f"{NAMESPACE}linkGrp")):
-            # Involved sentences according to the IDs of token mappings - 'corresp' does not list all of them!
-            # (possible bug in data)
-            involved_src_sents, involved_tgt_sents = find_involved_sents(corrected_sent)
-            id_doc, doc_title, is_manually_validated = None, None, False
-            src_sent_data, tgt_sent_data = {}, {}
-            tok2position = {}
-            assert len(involved_src_sents) > 0 or len(involved_tgt_sents) > 0
-            if len(involved_src_sents) > 0:
-                src_sent_data = deepcopy(source_data[involved_src_sents[0]])
-                if not isinstance(src_sent_data["idx_par"], list):
-                    src_sent_data["idx_par"] = [src_sent_data["idx_par"]]
-                for src_sent_id in involved_src_sents[1:]:
-                    curr_sent_data = source_data[src_sent_id]
-                    src_sent_data["id_token"].extend(curr_sent_data["id_token"])
-                    src_sent_data["idx_par"].append(curr_sent_data["idx_par"])
-                    src_sent_data["form"].extend(curr_sent_data["form"])
-                    src_sent_data["lemma"].extend(curr_sent_data["lemma"])
-                    src_sent_data["ana"].extend(curr_sent_data["ana"])
-                    src_sent_data["msd"].extend(curr_sent_data["msd"])
-                    src_sent_data["ne_tag"].extend(curr_sent_data["ne_tag"])
-                    src_sent_data["space_after"].extend(curr_sent_data["space_after"])
-                id_doc = src_sent_data["id_doc"]
-                doc_title = src_sent_data["doc_title"]
-                is_manually_validated |= src_sent_data["is_manually_validated"]
-                for _pos, _tok in enumerate(src_sent_data["id_token"]):
-                    tok2position[_tok] = _pos
-            if len(involved_tgt_sents) > 0:
-                tgt_sent_data = deepcopy(target_data[involved_tgt_sents[0]])
-                if not isinstance(tgt_sent_data["idx_par"], list):
-                    tgt_sent_data["idx_par"] = [tgt_sent_data["idx_par"]]
-                for tgt_sent_id in involved_tgt_sents[1:]:
-                    curr_sent_data = target_data[tgt_sent_id]
-                    tgt_sent_data["id_token"].extend(curr_sent_data["id_token"])
-                    tgt_sent_data["idx_par"].append(curr_sent_data["idx_par"])
-                    tgt_sent_data["form"].extend(curr_sent_data["form"])
-                    tgt_sent_data["lemma"].extend(curr_sent_data["lemma"])
-                    tgt_sent_data["ana"].extend(curr_sent_data["ana"])
-                    tgt_sent_data["msd"].extend(curr_sent_data["msd"])
-                    tgt_sent_data["ne_tag"].extend(curr_sent_data["ne_tag"])
-                    tgt_sent_data["space_after"].extend(curr_sent_data["space_after"])
-                id_doc = tgt_sent_data["id_doc"]
-                doc_title = tgt_sent_data["doc_title"]
-                is_manually_validated |= tgt_sent_data["is_manually_validated"]
-                for _pos, _tok in enumerate(tgt_sent_data["id_token"]):
-                    tok2position[_tok] = _pos
-            corr_data = []
-            for token_info in corrected_sent.findall(f"{NAMESPACE}link"):
-                connections = token_info.attrib["target"].split(" ")
-                corrections = token_info.attrib["type"]
-                if corrections == "ID":
-                    continue
-                src_inds, tgt_inds = [], []
-                corr_types = []
-                for curr_corr in corrections.split("|"):
-                    corr_types.append(curr_corr)
-                for curr_tok in connections:
-                    # Token IDs have an index at the end, but it is 1-based; convert it to 0-based
-                    idx_tok = tok2position[curr_tok[1:]]
-                    if "t" in curr_tok:  # target token
-                        tgt_inds.append(idx_tok)
-                    else:  # source token
-                        src_inds.append(idx_tok)
-                corr_data.append({"idx_src": src_inds, "idx_tgt": tgt_inds, "corr_types": corr_types})
-            yield idx_corr, {
-                "id_doc": id_doc[:-1],  # doc ID without the "s" or "t" info
-                "doc_title": doc_title,
-                "is_manually_validated": is_manually_validated,
-                "idx_src_par": src_sent_data.get("idx_par", []),
-                "id_src_tokens": src_sent_data.get("id_token", []),
-                "src_tokens": src_sent_data.get("form", []),
-                "src_ling_annotations": {
-                    "lemma": src_sent_data.get("lemma", []),
-                    "ana": src_sent_data.get("ana", []),
-                    "msd": src_sent_data.get("msd", []),
-                    "ne_tag": src_sent_data.get("ne_tag", []),
-                    "space_after": src_sent_data.get("space_after", [])
-                },
-                "idx_tgt_par": tgt_sent_data.get("idx_par", []),
-                "id_tgt_tokens": tgt_sent_data.get("id_token", []),
-                "tgt_tokens": tgt_sent_data.get("form", []),
-                "tgt_ling_annotations": {
-                    "lemma": tgt_sent_data.get("lemma", []),
-                    "ana": tgt_sent_data.get("ana", []),
-                    "msd": tgt_sent_data.get("msd", []),
-                    "ne_tag": tgt_sent_data.get("ne_tag", []),
-                    "space_after": tgt_sent_data.get("space_after", [])
-                },
-                "corrections": corr_data
-            }
-    @staticmethod
-    def aggregate_pars(sent_level_data):
-        # TODO: the code is a copypaste of the document aggregation, with an additional groupby - could use a refactor
-        uniq_idx_par = 0
-        for idx_doc, (curr_id, curr_group) in enumerate(groupby(sent_level_data, key=lambda tup: tup[1]["id_doc"])):
-            curr_instances = list(map(lambda tup: tup[1], curr_group))  # remove the redundant index info from datasets
-            # Some sentences have no `idx_src_par` because they are added by the teacher (not present in the source)
-            for idx_par, curr_par_group in groupby(
-                    curr_instances,
-                    key=lambda _inst: _inst["idx_src_par"][0] if len(_inst["idx_src_par"]) > 0 else
-                    _inst["idx_tgt_par"][0]
-            ):
-                src_tokens, tgt_tokens, mapped_corrections = [], [], []
-                src_ling_anns = {"lemma": [], "ana": [], "msd": [], "ne_tag": [], "space_after": []}
-                tgt_ling_anns = {"lemma": [], "ana": [], "msd": [], "ne_tag": [], "space_after": []}
-                seen_src_tokens, seen_tgt_tokens = {}, {}
-                src_base, tgt_base = 0, 0
-                prev_src_base, prev_tgt_base = 0, 0
-                doc_title, is_validated = None, None
-                for curr_inst in curr_par_group:
-                    doc_title, is_validated = curr_inst["doc_title"], curr_inst["is_manually_validated"]
-                    id_src_toks, id_tgt_toks = curr_inst["id_src_tokens"], curr_inst["id_tgt_tokens"]
-                    curr_src_toks, curr_tgt_toks = curr_inst["src_tokens"], curr_inst["tgt_tokens"]
-                    curr_src_anns, curr_tgt_anns = curr_inst["src_ling_annotations"], curr_inst["tgt_ling_annotations"]
-                    curr_corrs = curr_inst["corrections"]
-                    num_added_src, num_added_tgt = 0, 0
-                    for idx_position, (id_tok, tok) in enumerate(zip(id_src_toks, curr_src_toks)):
-                        if id_tok not in seen_src_tokens:
-                            src_tokens.append(tok)
-                            src_ling_anns["lemma"].append(curr_src_anns["lemma"][idx_position])
-                            src_ling_anns["ana"].append(curr_src_anns["ana"][idx_position])
-                            src_ling_anns["msd"].append(curr_src_anns["msd"][idx_position])
-                            src_ling_anns["ne_tag"].append(curr_src_anns["ne_tag"][idx_position])
-                            src_ling_anns["space_after"].append(curr_src_anns["space_after"][idx_position])
-                            seen_src_tokens[id_tok] = tok
-                            num_added_src += 1
-                    for idx_position, (id_tok, tok) in enumerate(zip(id_tgt_toks, curr_tgt_toks)):
-                        if id_tok not in seen_tgt_tokens:
-                            tgt_tokens.append(tok)
-                            tgt_ling_anns["lemma"].append(curr_tgt_anns["lemma"][idx_position])
-                            tgt_ling_anns["ana"].append(curr_tgt_anns["ana"][idx_position])
-                            tgt_ling_anns["msd"].append(curr_tgt_anns["msd"][idx_position])
-                            tgt_ling_anns["ne_tag"].append(curr_tgt_anns["ne_tag"][idx_position])
-                            tgt_ling_anns["space_after"].append(curr_tgt_anns["space_after"][idx_position])
-                            seen_tgt_tokens[id_tok] = tok
-                            num_added_tgt += 1
-                    if num_added_src == 0:
-                        src_base, prev_src_base = prev_src_base, src_base
-                    if num_added_tgt == 0:
-                        tgt_base, prev_tgt_base = prev_tgt_base, tgt_base
-                    for corr in curr_corrs:
-                        mapped_corrections.append({
-                            "idx_src": list(map(lambda _i: src_base + _i, corr["idx_src"])),
-                            "idx_tgt": list(map(lambda _i: tgt_base + _i, corr["idx_tgt"])),
-                            "corr_types": corr["corr_types"]
-                        })
-                    src_base += num_added_src
-                    tgt_base += num_added_tgt
-                    if num_added_src == 0:
-                        src_base, prev_src_base = prev_src_base, src_base
-                    if num_added_tgt == 0:
-                        tgt_base, prev_tgt_base = prev_tgt_base, tgt_base
-                yield uniq_idx_par, {
-                    "id_doc": curr_id,
-                    "doc_title": doc_title,
-                    "is_manually_validated": is_validated,
-                    "src_tokens": src_tokens,
-                    "src_ling_annotations": src_ling_anns,
-                    "tgt_tokens": tgt_tokens,
-                    "tgt_ling_annotations": tgt_ling_anns,
-                    "corrections": mapped_corrections
-                }
-                uniq_idx_par += 1
-    @staticmethod
-    def aggregate_docs(sent_level_data):
-        # NOTE: assuming here that `sent_level_data` is pre-sorted by id_doc, which is done in the raw data
-        for idx_doc, (curr_id, curr_group) in enumerate(groupby(sent_level_data, key=lambda tup: tup[1]["id_doc"])):
-            curr_instances = map(lambda tup: tup[1], curr_group)  # remove the redundant index info from datasets
-            src_tokens, tgt_tokens, mapped_corrections = [], [], []
-            src_ling_anns = {"lemma": [], "ana": [], "msd": [], "ne_tag": [], "space_after": []}
-            tgt_ling_anns = {"lemma": [], "ana": [], "msd": [], "ne_tag": [], "space_after": []}
-            seen_src_tokens, seen_tgt_tokens = {}, {}
-            # Need to keep the current base position of source and target tokens AND previous base position:
-            # A source may map into multiple targets (or vice versa), but we do not want to write it twice in a doc.
-            # Therefore, when the same sentence is encountered twice, the base is shifted to the previous one to map
-            # the indices of corrected tokens correctly.
-            src_base, tgt_base = 0, 0
-            prev_src_base, prev_tgt_base = 0, 0
-            doc_title, is_validated = None, None
-            for curr_inst in curr_instances:
-                doc_title, is_validated = curr_inst["doc_title"], curr_inst["is_manually_validated"]
-                id_src_toks, id_tgt_toks = curr_inst["id_src_tokens"], curr_inst["id_tgt_tokens"]
-                curr_src_toks, curr_tgt_toks = curr_inst["src_tokens"], curr_inst["tgt_tokens"]
-                curr_src_anns, curr_tgt_anns = curr_inst["src_ling_annotations"], curr_inst["tgt_ling_annotations"]
-                curr_corrs = curr_inst["corrections"]
-                num_added_src, num_added_tgt = 0, 0
-                for idx_position, (id_tok, tok) in enumerate(zip(id_src_toks, curr_src_toks)):
-                    if id_tok not in seen_src_tokens:
-                        src_tokens.append(tok)
-                        src_ling_anns["lemma"].append(curr_src_anns["lemma"][idx_position])
-                        src_ling_anns["ana"].append(curr_src_anns["ana"][idx_position])
-                        src_ling_anns["msd"].append(curr_src_anns["msd"][idx_position])
-                        src_ling_anns["ne_tag"].append(curr_src_anns["ne_tag"][idx_position])
-                        src_ling_anns["space_after"].append(curr_src_anns["space_after"][idx_position])
-                        seen_src_tokens[id_tok] = tok
-                        num_added_src += 1
-                for idx_position, (id_tok, tok) in enumerate(zip(id_tgt_toks, curr_tgt_toks)):
-                    if id_tok not in seen_tgt_tokens:
-                        tgt_tokens.append(tok)
-                        tgt_ling_anns["lemma"].append(curr_tgt_anns["lemma"][idx_position])
-                        tgt_ling_anns["ana"].append(curr_tgt_anns["ana"][idx_position])
-                        tgt_ling_anns["msd"].append(curr_tgt_anns["msd"][idx_position])
-                        tgt_ling_anns["ne_tag"].append(curr_tgt_anns["ne_tag"][idx_position])
-                        tgt_ling_anns["space_after"].append(curr_tgt_anns["space_after"][idx_position])
-                        seen_tgt_tokens[id_tok] = tok
-                        num_added_tgt += 1
-                if num_added_src == 0:
-                    src_base, prev_src_base = prev_src_base, src_base
-                if num_added_tgt == 0:
-                    tgt_base, prev_tgt_base = prev_tgt_base, tgt_base
-                for corr in curr_corrs:
-                    mapped_corrections.append({
-                        "idx_src": list(map(lambda _i: src_base + _i, corr["idx_src"])),
-                        "idx_tgt": list(map(lambda _i: tgt_base + _i, corr["idx_tgt"])),
-                        "corr_types": corr["corr_types"]
-                    })
-                src_base += num_added_src
-                tgt_base += num_added_tgt
-                if num_added_src == 0:
-                    src_base, prev_src_base = prev_src_base, src_base
-                if num_added_tgt == 0:
-                    tgt_base, prev_tgt_base = prev_tgt_base, tgt_base
-            yield idx_doc, {
-                "id_doc": curr_id,
-                "doc_title": doc_title,
-                "is_manually_validated": is_validated,
-                "src_tokens": src_tokens,
-                "src_ling_annotations": src_ling_anns,
-                "tgt_tokens": tgt_tokens,
-                "tgt_ling_annotations": tgt_ling_anns,
-                "corrections": mapped_corrections
-            }
-    def _generate_examples(self, source_path, target_path, links_path):
-        sent_level_data = list(Solar3.generate_sentences(source_path, target_path, links_path))
-        if self.config.name == "sentence_level":
-            # Remove IDs and indices that are only useful for aggregating the document-level data
-            for i, instance in sent_level_data:
-                yield i, {_k: _v for _k, _v in instance.items() if _k not in {"id_src_tokens", "id_tgt_tokens",
-                                                                              "idx_src_par", "idx_tgt_par"}}
-        elif self.config.name == "paragraph_level":
-            yield from list(Solar3.aggregate_pars(sent_level_data))
-        else:
-            yield from list(Solar3.aggregate_docs(sent_level_data))