Create create_dataset.py

File to convert the dataset into JSON format so that it can be easily loaded in the Huggingface dataset.

create_dataset.py

@@ -0,0 +1,162 @@
+import os
+import json
+import glob
+from tqdm import tqdm
+from io import StringIO
+import pandas as pd
+
+"""
+Steps to convert the data into JSON format.
+
+Step-0: Use a python environment where pandas is installed.
+Step-1: Download the source file from here: https://figshare.com/articles/dataset/MACCROBAT2018/9764942
+Step-2: Unzip the file in put that into a folder (say `data` folder).
+    All unzipped files will be present here.
+        * data/MACCROBAT2020/*.txt
+        * data/MACCROBAT2020/*.ann
+Step-3: Use the correct paths and run this file.
+"""
+
+def remove_overlapped_ner_tags(ner_details: list[dict]):
+    """remove overlapping entities.
+
+    Args:
+        ner_details (List[dict]): a list of dictionary where each dictionary holds
+            the information of a entity.
+
+    NOTE: Priority is given to the entity that is labelled first after sorting all by start index in ascending order.
+    (i.e. it's end-index is less than other start of other overlapping entity.)
+
+    Returns:
+        list[dict]: updated list (removed item if something was overlapping)
+    """
+    # funtion to remove the overlapping NER-tags
+    new_ner_details = []
+
+    ner_details = sorted(ner_details, key=lambda x: x["start"])
+    for i, ner_detail in enumerate(ner_details):
+        if i == 0:
+            start = ner_detail["start"]
+            end = ner_detail["end"]
+            new_ner_details.append(ner_detail)
+            continue
+
+        current_start = ner_detail["start"]
+        current_end = ner_detail["end"]
+        if current_start < end:
+            continue
+
+        # update the start and end
+        start = current_start
+        end = current_end
+
+        new_ner_details.append(ner_detail)
+    return new_ner_details
+
+
+def get_ner_details(ann_file):
+    with open(ann_file, "r") as f:
+        lines = f.readlines()
+        lines = [line.strip() for line in lines]
+        csv_data = "\n".join(lines)
+        csv_data = StringIO(csv_data)
+    df = pd.read_csv(csv_data, sep="\t", header=None)
+
+    df.columns = ["EntityID", "EntityDetails", "EntityText"]
+    # print(df.shape)
+
+    # remove rows where entity-id start other than `T`
+    df = df[df["EntityID"].apply(lambda x: str(x).strip().startswith("T"))]
+
+    # remove the rows which contains the ";" in the `EntityDetails`
+    df = df[df["EntityDetails"].apply(lambda x: ";" not in str(x))]
+
+    # drop where None is present
+    df.dropna(axis=1, inplace=True)
+
+    ner_info = []
+    for i, row in df.iterrows():
+        text = row["EntityText"]
+        details = row["EntityDetails"]
+        try:
+            ner_tag, start, end = details.split(" ")
+        except:
+            print(ann_file)
+            print(details)
+        start = int(float(start))
+        end = int(float(end))
+
+        ner_info.append({"text": text, "label": ner_tag.upper(), "start": start, "end": end})
+
+    # remove the overlapping entities
+    ner_info = remove_overlapped_ner_tags(ner_details=ner_info)
+    # print(ner_info)
+
+    return ner_info
+
+
+def main(input_path: str = "data/MACCROBAT2020", output_path: str = "data/MACCROBAT2020-V2.json"):
+    txt_files = glob.glob(os.path.join(input_path, "*.txt"))
+    txt_files.sort()
+
+    ner_data = {"data": [], "verson": "MACCROBAT-V2 (https://figshare.com/articles/dataset/MACCROBAT2018/9764942)"}
+
+    for txt_file in tqdm(txt_files, desc="Extracting data..."):
+        with open(txt_file, "r") as f:
+            full_text = f.read()
+        a = txt_file.replace(".txt", ".ann")
+        ner_info = get_ner_details(a)
+        data = {"full_text": full_text, "ner_info": ner_info}
+        ner_data["data"].append(data)
+
+    ALL_NER_LABLES = set()
+    for details in tqdm(ner_data["data"], desc="Splitting into tokens..."):
+        text = details["full_text"]
+        ner_details = details["ner_info"]
+
+        tokens = []
+        ner_labels = []
+        start = 0
+        for ner_detail in ner_details:
+            ner_start = ner_detail["start"]
+            ner_end = ner_detail["end"]
+
+            before_ner_token = text[start:ner_start]
+            ner_token = text[ner_start:ner_end]
+
+            tokens.append(before_ner_token)
+            ner_labels.append("O")
+            tokens.append(ner_token)
+            ner_labels.append(f'B-{ner_detail["label"]}')
+            ALL_NER_LABLES.add(f'B-{ner_detail["label"]}')
+            ALL_NER_LABLES.add(f'I-{ner_detail["label"]}')
+            start = ner_end
+        if len(text) >= start:
+            ner_labels.append("O")
+            tokens.append(text[start:])
+        assert len(tokens) == len(ner_labels)
+
+        details["tokens"] = tokens
+        details["ner_labels"] = ner_labels
+
+    ner_data["all_ner_labels"] = sorted(list(ALL_NER_LABLES), key=lambda x: x.split("-")[-1])
+
+    label_2_index = {k: i for i, k in enumerate(ner_data["all_ner_labels"])}
+    index_2_label = {v: k for k, v in label_2_index.items()}
+    ner_data["label_2_index"] = label_2_index
+    ner_data["index_2_label"] = index_2_label
+
+    for details in tqdm(ner_data["data"], desc="label2index..."):
+        ner_labels = details["ner_labels"]
+        ner_labels_ids = []
+        for ner in ner_labels:
+            ner_labels_ids.append(label_2_index.get(ner))
+        details["ner_labels"] = ner_labels_ids
+    with open(output_path, "w") as f:
+        json.dump(ner_data, f, indent=4)
+
+
+if __name__ == "__main__":
+    input_path: str = "data/MACCROBAT2020"
+    output_path: str = "data/MACCROBAT2020-V2.json"
+    main(input_path=input_path, output_path=output_path)