Overview
The original dataset can be found here while the Github repo is here.
This dataset has been proposed in Combining fact extraction and verification with neural semantic matching networks. This dataset has been created as a modification of FEVER.
In the original FEVER setting, the input is a claim from Wikipedia and the expected output is a label. However, this is different from the standard NLI formalization which is basically a pair-of-sequence to label problem. To facilitate NLI-related research to take advantage of the FEVER dataset, the authors pair the claims in the FEVER dataset with the textual evidence and make it a pair-of-sequence to label formatted dataset.
Dataset curation
The label mapping follows the paper and is the following
mapping = {
"SUPPORTS": 0, # entailment
"NOT ENOUGH INFO": 1, # neutral
"REFUTES": 2, # contradiction
}
Also, the "verifiable" column has been encoded as follows
mapping = {"NOT VERIFIABLE": 0, "VERIFIABLE": 1}
Finally, a consistency check with the labels reported in the original FEVER dataset is performed.
NOTE: no label is available for the "test" split.
NOTE: there are 3 instances in common between dev and train splits.
Code to generate the dataset
import pandas as pd
from datasets import Dataset, ClassLabel, load_dataset, Value, Features, DatasetDict
import json
# download data from https://www.dropbox.com/s/hylbuaovqwo2zav/nli_fever.zip?dl=0
paths = {
"train": "<some_path>/nli_fever/train_fitems.jsonl",
"validation": "<some_path>/nli_fever/dev_fitems.jsonl",
"test": "<some_path>/nli_fever/test_fitems.jsonl",
}
# parsing code from https://github.com/facebookresearch/anli/blob/main/src/utils/common.py
registered_jsonabl_classes = {}
def register_class(cls):
global registered_jsonabl_classes
if cls not in registered_jsonabl_classes:
registered_jsonabl_classes.update({cls.__name__: cls})
def unserialize_JsonableObject(d):
global registered_jsonabl_classes
classname = d.pop("_jcls_", None)
if classname:
cls = registered_jsonabl_classes[classname]
obj = cls.__new__(cls) # Make instance without calling __init__
for key, value in d.items():
setattr(obj, key, value)
return obj
else:
return d
def load_jsonl(filename, debug_num=None):
d_list = []
with open(filename, encoding="utf-8", mode="r") as in_f:
print("Load Jsonl:", filename)
for line in in_f:
item = json.loads(line.strip(), object_hook=unserialize_JsonableObject)
d_list.append(item)
if debug_num is not None and 0 < debug_num == len(d_list):
break
return d_list
def get_original_fever() -> pd.DataFrame:
"""Get original fever datasets."""
fever_v1 = load_dataset("fever", "v1.0")
fever_v2 = load_dataset("fever", "v2.0")
columns = ["id", "label"]
splits = ["paper_test", "paper_dev", "labelled_dev", "train"]
list_dfs = [fever_v1[split].to_pandas()[columns] for split in splits]
list_dfs.append(fever_v2["validation"].to_pandas()[columns])
dfs = pd.concat(list_dfs, ignore_index=False)
dfs = dfs.drop_duplicates()
dfs = dfs.rename(columns={"label": "fever_gold_label"})
return dfs
def load_and_process(path: str, fever_df: pd.DataFrame) -> pd.DataFrame:
"""Load data split and merge with fever."""
df = pd.DataFrame(load_jsonl(path))
df = df.rename(columns={"query": "premise", "context": "hypothesis"})
# adjust dtype
df["cid"] = df["cid"].astype(int)
# merge with original fever to get labels
df = pd.merge(df, fever_df, left_on="cid", right_on="id", how="inner").drop_duplicates()
return df
def encode_labels(df: pd.DataFrame) -> pd.DataFrame:
"""Encode labels using the mapping used in SNLI and MultiNLI"""
mapping = {
"SUPPORTS": 0, # entailment
"NOT ENOUGH INFO": 1, # neutral
"REFUTES": 2, # contradiction
}
df["label"] = df["fever_gold_label"].map(mapping)
# verifiable
df["verifiable"] = df["verifiable"].map({"NOT VERIFIABLE": 0, "VERIFIABLE": 1})
return df
if __name__ == "__main__":
fever_df = get_original_fever()
dataset_splits = {}
for split, path in paths.items():
# from json to dataframe and merge with fever
df = load_and_process(path, fever_df)
if not len(df) > 0:
print(f"Split `{split}` has no matches")
continue
if split == "train":
# train must have same labels
assert sum(df["fever_gold_label"] != df["label"]) == 0
# encode labels using the default mapping used by other nli datasets
# i.e, entailment: 0, neutral: 1, contradiction: 2
df = df.drop(columns=["label"])
df = encode_labels(df)
# cast to dataset
features = Features(
{
"cid": Value(dtype="int64", id=None),
"fid": Value(dtype="string", id=None),
"id": Value(dtype="int32", id=None),
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"verifiable": Value(dtype="int64", id=None),
"fever_gold_label": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
}
)
if "test" in path:
# no features for test set
df["label"] = -1
df["verifiable"] = -1
df["fever_gold_label"] = "not available"
dataset = Dataset.from_pandas(df, features=features)
dataset_splits[split] = dataset
nli_fever = DatasetDict(dataset_splits)
nli_fever.push_to_hub("pietrolesci/nli_fever", token="<your token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(dataset_splits.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
dataset_splits[i].to_pandas(),
dataset_splits[j].to_pandas(),
on=["premise", "hypothesis", "label"],
how="inner",
).shape[0],
)
#> train - dev: 3
#> train - test: 0
#> dev - test: 0