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## Overview
Original dataset available [here](https://wellecks.github.io/dialogue_nli/).
## Dataset curation
Original `label` column is renamed `original_label`. The original classes are renamed as follows
```
{"positive": "entailment", "negative": "contradiction", "neutral": "neutral"})
```
and encoded with the following mapping
```
{"entailment": 0, "neutral": 1, "contradiction": 2}
```
and stored in the newly created column `label`.
The following splits and the corresponding columns are present in the original files
```
train {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
dev {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
test {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
verified_test {'dtype', 'annotation3', 'sentence1', 'sentence2', 'annotation1', 'annotation2', 'original_label', 'label', 'triple2', 'triple1'}
extra_test {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
extra_dev {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
extra_train {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
valid_havenot {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
valid_attributes {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
valid_likedislike {'dtype', 'id', 'sentence1', 'sentence2', 'original_label', 'label', 'triple2', 'triple1'}
```
Note that I only keep the common columns, which means that I drop "annotation{1, 2, 3}" from `verified_test`.
Note that there are some splits with the same instances, as found by matching on "original_label", "sentence1", "sentence2".
## Code to create dataset
```python
import pandas as pd
from pathlib import Path
import json
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, Sequence
# load data
ds = {}
for path in Path(".").rglob("<path to folder>/*.jsonl"):
print(path, flush=True)
with path.open("r") as fl:
data = fl.read()
try:
d = json.loads(data, encoding="utf-8")
except json.JSONDecodeError as error:
print(error)
df = pd.DataFrame(d)
# encode labels
df["original_label"] = df["label"]
df["label"] = df["label"].map({"positive": "entailment", "negative": "contradiction", "neutral": "neutral"})
df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
ds[path.name.split(".")[0]] = df
# prettify names of data splits
datasets = {
k.replace("dialogue_nli_", "").replace("uu_", "").lower(): v
for k, v in ds.items()
}
datasets.keys()
#> dict_keys(['train', 'dev', 'test', 'verified_test', 'extra_test', 'extra_dev', 'extra_train', 'valid_havenot', 'valid_attributes', 'valid_likedislike'])
# cast to datasets using only common columns
features = Features({
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
"sentence1": Value(dtype="string", id=None),
"sentence2": Value(dtype="string", id=None),
"triple1": Sequence(feature=Value(dtype="string", id=None), length=3),
"triple2": Sequence(feature=Value(dtype="string", id=None), length=3),
"dtype": Value(dtype="string", id=None),
"id": Value(dtype="string", id=None),
"original_label": Value(dtype="string", id=None),
})
ds = {}
for name, df in datasets.items():
if "id" not in df.columns:
df["id"] = ""
ds[name] = Dataset.from_pandas(df.loc[:, list(features.keys())], features=features)
ds = DatasetDict(ds)
ds.push_to_hub("dialogue_nli", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(ds.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
ds[i].to_pandas(),
ds[j].to_pandas(),
on=["original_label", "sentence1", "sentence2"],
how="inner",
).shape[0],
)
#> train - dev: 58
#> train - test: 98
#> train - verified_test: 90
#> train - extra_test: 0
#> train - extra_dev: 0
#> train - extra_train: 0
#> train - valid_havenot: 0
#> train - valid_attributes: 0
#> train - valid_likedislike: 0
#> dev - test: 19
#> dev - verified_test: 19
#> dev - extra_test: 0
#> dev - extra_dev: 75
#> dev - extra_train: 75
#> dev - valid_havenot: 75
#> dev - valid_attributes: 75
#> dev - valid_likedislike: 75
#> test - verified_test: 12524
#> test - extra_test: 34
#> test - extra_dev: 0
#> test - extra_train: 0
#> test - valid_havenot: 0
#> test - valid_attributes: 0
#> test - valid_likedislike: 0
#> verified_test - extra_test: 29
#> verified_test - extra_dev: 0
#> verified_test - extra_train: 0
#> verified_test - valid_havenot: 0
#> verified_test - valid_attributes: 0
#> verified_test - valid_likedislike: 0
#> extra_test - extra_dev: 0
#> extra_test - extra_train: 0
#> extra_test - valid_havenot: 0
#> extra_test - valid_attributes: 0
#> extra_test - valid_likedislike: 0
#> extra_dev - extra_train: 250946
#> extra_dev - valid_havenot: 250946
#> extra_dev - valid_attributes: 250946
#> extra_dev - valid_likedislike: 250946
#> extra_train - valid_havenot: 250946
#> extra_train - valid_attributes: 250946
#> extra_train - valid_likedislike: 250946
#> valid_havenot - valid_attributes: 250946
#> valid_havenot - valid_likedislike: 250946
#> valid_attributes - valid_likedislike: 250946
``` |