added causal-candidate-extraction and the conversion script

README.md

@@ -37,6 +37,14 @@ configs:
     path: causal-candidate-extraction/train.parquet
   - split: test
     path: causal-candidate-extraction/test.parquet
+  features:
+    - name: index
+      dtype: string
+    - name: tokens
+      sequence: dtype
+    - name: entity
+      sequence:
+        sequence: int32
 - config_name: causality identification
   data_files:
   - split: train

causal-candidate-extraction/test.parquet

@@ -0,0 +1,3 @@
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+size 49728

causal-candidate-extraction/train.parquet

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+oid sha256:36140219a24ab363966ed55377daea38fc4ce1d9e18bb8bd7787f698ec7facc5
+size 252771

causality-detection/test.parquet

@@ -1,3 +1,3 @@
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+size 60652

causality-detection/train.parquet

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+size 331135

causality-identification/train.parquet

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+oid sha256:647fdcf674408b10e150f428041f756f79bc6914149b1a1e91e1a1386b046971
+size 377299

conversion_script.py

@@ -0,0 +1,67 @@
+#!/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")