File size: 3,560 Bytes
a40f768 308cc0f a40f768 308cc0f a40f768 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import json
import datasets
from pathlib import Path
import pandas as pd
BASE_DATA_PATH = Path("./data")
class CSCOMMConfig(datasets.BuilderConfig):
"""BuilderConfig for CSCOMM."""
def __init__(self, key, pretraining=False, data_path="./data", **kwargs):
"""BuilderConfig for CSCOMM.
Args:
key: `string`
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 0.0.1: Initial version.
super(CSCOMMConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
self.key = key
self.pretraining = pretraining
class CSCOMM(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
CSCOMMConfig(
name="AP",
key="ap"
),
CSCOMMConfig(
name="AP+P",
key="ap_p"
),
CSCOMMConfig(
name="AP+J",
key="ap_j"
),
CSCOMMConfig(
name="AP+PJ",
key="ap_pj"
),
CSCOMMConfig(
name="BA",
key="ba"
),
CSCOMMConfig(
name="BA+P",
key="ba_p"
),
CSCOMMConfig(
name="BA+J",
key="ba_j"
),
CSCOMMConfig(
name="BA+PJ",
key="ba_pj"
),
CSCOMMConfig(
name="pretrain-unlabeled",
key="pt_un",
pretraining=True
),
CSCOMMConfig(
name="pretrain-labeled",
key="pt_la",
pretraining=True
),
CSCOMMConfig(
name="pretrain-both",
key="pt_unla",
pretraining=True
),
]
def _info(self):
features = {
"round_id": datasets.Value("string"),
"source": datasets.Value("string")
}
if not self.config.pretraining:
features["commentary"] = datasets.Value("string")
return datasets.DatasetInfo(
features=datasets.Features(features),
)
def _split_generators(self, dl_manager):
dl_dir = dl_manager.download_and_extract({
"train": f"./data/{self.config.key}/train.csv",
"valid": f"./data/{self.config.key}/valid.csv",
"test": f"./data/{self.config.key}/test.csv"
})
data_path = Path(dl_dir) / self.config.key
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": data_path / "train.csv",
"split": datasets.Split.TRAIN,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": data_path / "valid.csv",
"split": datasets.Split.VALIDATION,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": data_path / "test.csv",
"split": datasets.Split.TEST,
},
),
]
def _generate_examples(self, data_file, split):
df = pd.read_csv(data_file)
for i, row in enumerate(df.itertuples()):
example = {"idx": i, "round_id": row.round_id, "source": row.source}
if not self.config.pretraining:
example["commentary"] = row.commentary
yield example["idx"], example
|