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Add training datasets and paper corpus
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{
"train": {
"documents": 100,
"sentences": 3874,
"evidence_positive_sentences": 0,
"importance_labeled_sentences": 3874,
"importance_positive_sentences": 297,
"role_counts": {
"ablation": 17,
"background": 142,
"baseline": 1,
"comparison": 13,
"component": 28,
"contribution": 46,
"core_method": 1417,
"dataset": 192,
"finding": 18,
"future_work": 34,
"gap": 38,
"mechanism": 13,
"metric": 17,
"motivation": 26,
"objective": 8,
"prior_work": 87,
"problem": 919,
"process": 36,
"result": 822
},
"section_counts": {
"conclusion": 389,
"experiment": 1045,
"intro": 1054,
"method": 1160,
"related_work": 226
}
},
"validation": {
"documents": 50,
"sentences": 1984,
"evidence_positive_sentences": 0,
"importance_labeled_sentences": 1984,
"importance_positive_sentences": 149,
"role_counts": {
"ablation": 5,
"background": 76,
"comparison": 5,
"component": 14,
"contribution": 34,
"core_method": 685,
"dataset": 107,
"finding": 11,
"future_work": 14,
"gap": 17,
"mechanism": 12,
"metric": 17,
"motivation": 7,
"objective": 11,
"prior_work": 50,
"problem": 432,
"process": 24,
"result": 463
},
"section_counts": {
"conclusion": 214,
"experiment": 560,
"intro": 543,
"method": 525,
"related_work": 142
}
},
"test": {
"documents": 100,
"sentences": 3704,
"evidence_positive_sentences": 0,
"importance_labeled_sentences": 3704,
"importance_positive_sentences": 297,
"role_counts": {
"ablation": 19,
"background": 129,
"baseline": 4,
"comparison": 11,
"component": 20,
"contribution": 66,
"core_method": 1270,
"dataset": 181,
"finding": 18,
"future_work": 37,
"gap": 37,
"mechanism": 24,
"metric": 19,
"motivation": 22,
"objective": 10,
"prior_work": 115,
"problem": 803,
"process": 40,
"result": 879
},
"section_counts": {
"conclusion": 472,
"experiment": 939,
"intro": 1025,
"method": 991,
"related_work": 277
}
}
}