matejklemen
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Update README.md
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README.md
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@@ -60,9 +60,18 @@ A sample instance from the dataset:
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{
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'id_doc': 'solar1',
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'doc_title': 'KUS-G-slo-1-GO-E-2009-10001',
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'is_manually_validated': True,
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'src_tokens': ['”', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', '”', ',', 'izreče', 'Antigona', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
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'
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'corrections': [
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{'idx_src': [0], 'idx_tgt': [0], 'corr_types': ['Z/LOČ/nerazvrščeno']},
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{'idx_src': [10, 11], 'idx_tgt': [10, 11], 'corr_types': ['Z/LOČ/nerazvrščeno']},
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@@ -81,8 +90,10 @@ The instance represents a correction in the document 'solar1' (`id_doc`), which
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- `id_doc`: a string containing the identifying name of the document in which the sentence appears;
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- `doc_title`: a string containing the assigned document title;
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- `is_manually_validated`: a bool indicating whether the document in which the sentence appears was reviewed by a teacher;
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- `src_tokens`: words in the source sentence (`[]` if there is no source sentence);
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- `tgt_tokens`: words in the target sentence (`[]` if there is no target sentence);
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- `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`.
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{
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'id_doc': 'solar1',
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'doc_title': 'KUS-G-slo-1-GO-E-2009-10001',
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'is_manually_validated': True,
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'src_tokens': ['”', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', '”', ',', 'izreče', 'Antigona', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
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'src_ling_annotations': {
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# truncated for conciseness
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'lemma': ['”', 'ne', 'da', 'sovražiti', ...],
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'ana': ['mte:U', 'mte:L', 'mte:Vd', ...],
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'msd': ['UPosTag=PUNCT', 'UPosTag=PART|Polarity=Neg', 'UPosTag=SCONJ', ...],
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'ne_tag': [..., 'O', 'B-PER', 'O', ...]
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},
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'tgt_tokens': ['„', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', ',', '”', 'izreče', 'Antigona', 'sebi', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
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# omitted for conciseness, the format is the same as in 'src_ling_annotations'
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'tgt_ling_annotations': {...},
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'corrections': [
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{'idx_src': [0], 'idx_tgt': [0], 'corr_types': ['Z/LOČ/nerazvrščeno']},
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{'idx_src': [10, 11], 'idx_tgt': [10, 11], 'corr_types': ['Z/LOČ/nerazvrščeno']},
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- `id_doc`: a string containing the identifying name of the document in which the sentence appears;
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- `doc_title`: a string containing the assigned document title;
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- `is_manually_validated`: a bool indicating whether the document in which the sentence appears was reviewed by a teacher;
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- `src_tokens`: words in the source sentence (`[]` if there is no source sentence);
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- `src_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, and named entity tags encoded using IOB2 (key `"ne_tag"`) for the source tokens;
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- `tgt_tokens`: words in the target sentence (`[]` if there is no target sentence);
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- `tgt_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, and named entity tags encoded using IOB2 (key `"ne_tag"`) for the target tokens;
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- `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`.
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