File size: 2,561 Bytes
b6b15da |
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 |
#!/usr/bin/env python3
"""
Run this script as ./conversion_script.py to convert the SCITE files to HF-compatible parquet files.
"""
import re
from typing import Literal
import pandas as pd
Split = Literal["train", "test"]
def convert_for_causality_detection(split: Split) -> None:
df = pd.read_xml(f"{split}-corpus.xml")
df["label"] = df["label"].apply(lambda x: 0 if x == "Non-Causal" else 1)
df["text"] = df["sentence"].apply(lambda x: re.sub(r'</?e\d+>', "", x))
df["index"] = df["id"].apply(lambda x: f"scite_{split}_{x}")
df = df.set_index("index")
df = df[["label", "text"]]
df.to_parquet(f"./causality-detection/{split}.parquet", engine="pyarrow")
def convert_for_causal_candidate_extraction(split: Split) -> None:
def map_to_tokens(text: str):
splits: list[str] = []
tags: list[set[int]] = []
curtags: set[int] = set()
for match in re.finditer(r"(.*?)<(/?)e(\d+)>", text):
splits.append(match[1])
tags.append(list(curtags))
if match[2] == "":
curtags.add(int(match[3]))
else:
curtags.remove(int(match[3]))
return pd.Series((splits, tags))
df = pd.read_xml(f"{split}-corpus.xml")
df[["tokens", "entity"]] = df["sentence"].apply(map_to_tokens)
df["index"] = df["id"].apply(lambda x: f"scite_{split}_{x}")
df = df[["index", "tokens", "entity"]].set_index("index")
print(df)
df.to_parquet(f"./causal-candidate-extraction/{split}.parquet", engine="pyarrow")
def convert_for_causality_identification(split: Split) -> None:
def map_label(label: str):
if label == "Non-Causal":
return []
tmp = list()
for t in label[len("Cause-Effect("):-1].split("),("):
left, right = t.strip('()').split(',')
tmp.append({"relationship": 1, "first": left, "second": right})
return tmp
df = pd.read_xml(f"{split}-corpus.xml", dtype_backend="pyarrow")
df["relations"] = df["label"].apply(map_label)
df["text"] = df["sentence"]
df["index"] = df["id"].apply(lambda x: f"scite_{split}_{x}")
df = df.set_index("index")
df = df[["text", "relations"]]
df.to_parquet(f"./causality-identification/{split}.parquet", engine="pyarrow")
convert_for_causality_detection("test")
convert_for_causality_detection("train")
convert_for_causal_candidate_extraction("test")
convert_for_causal_candidate_extraction("train")
convert_for_causality_identification("test")
convert_for_causality_identification("train") |