mathiascreutz
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README.md
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@@ -159,29 +159,44 @@ TODO: Add comment about larger and noisier sets being better for training.
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### Data Instances
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```
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DatasetDict({
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test: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows:
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})
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validation: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows:
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})
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test.full: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows:
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})
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validation.full: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows:
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})
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train: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows:
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})
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})
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```
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### Data Fields
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### Data Instances
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As a concrete example, loading the English data with a 95% quality of
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the train split produces the following:
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```
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>>> data = load_dataset("GEM/opusparcus", lang="en", quality=95)
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>>> data
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DatasetDict({
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test: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows: 982
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})
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validation: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows: 1015
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})
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test.full: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows: 1445
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})
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validation.full: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows: 1455
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})
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train: Dataset({
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features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
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num_rows: 1000000
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})
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})
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>>> data["test"][0]
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{'annot_score': 4.0, 'gem_id': 'gem-opusparcus-test-1587', 'lang': 'en', 'sent1': "I haven 't been contacted by anybody .", 'sent2': "Nobody 's contacted me ."}
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>>> data["validation"][2]
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{'annot_score': 3.0, 'gem_id': 'gem-opusparcus-validation-1586', 'lang': 'en', 'sent1': 'No promises , okay ?', 'sent2': "I 'm not promising anything ."}
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>>> data["train"][1000]
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{'annot_score': 0.0, 'gem_id': 'gem-opusparcus-train-12501001', 'lang': 'en', 'sent1': 'Am I beautiful ?', 'sent2': 'Am I pretty ?'}
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```
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### Data Fields
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