Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
natural-language-inference
Size:
100K - 1M
Tags:
quality-estimation
License:
Update ik-nlp-22_transqe.py
Browse files- ik-nlp-22_transqe.py +22 -22
ik-nlp-22_transqe.py
CHANGED
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@@ -114,25 +114,25 @@ class IkNlp22ExpNLIConfig(datasets.GeneratorBasedBuilder):
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for i, row in enumerate(reader):
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print(row)
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yield i, {
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for i, row in enumerate(reader):
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print(row)
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yield i, {
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"premise_en": row["premise_en"],
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"premise_nl": row["premise_nl"],
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"hypothesis_en": row["hypothesis_en"],
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"hypothesis_nl": row["hypothesis_nl"],
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"label": row["label"],
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"explanation_1_en": row["explanation_1_en"],
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"explanation_1_nl": row["explanation_1_nl"],
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"explanation_2_en": row.get("explanation_2_en", ""),
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"explanation_2_nl": row.get("explanation_2_nl", ""),
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"explanation_3_en": row.get("explanation_3_en", ""),
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"explanation_3_nl": row.get("explanation_3_nl", ""),
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"da_premise": row["da_premise"],
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"mqm_premise": row["mqm_premise"],
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"da_hypothesis": row["da_hypothesis"],
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"mqm_hypothesis": row["mqm_hypothesis"],
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"da_explanation_1": row["da_explanation_1"],
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"mqm_explanation_1": row["mqm_explanation_1"],
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"da_explanation_2": row.get("da_explanation_2", ""),
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"mqm_explanation_2": row.get("mqm_explanation_2", ""),
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"da_explanation_3": row.get("da_explanation_3", ""),
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"mqm_explanation_3": row.get("mqm_explanation_3", ""),
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}
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