update

README.md

@@ -12,7 +12,7 @@ license: apache-2.0
 |    数据     |  语言  |  任务类型  |  原始数据/项目地址  | 样本个数  |  原始数据描述  | 替代数据下载地址 |
 |    :---     | :---: |   :---:   |       :---:        |   :---:  |     :---:     |     :---:     |
 |  sms_spam  |  英语  | 垃圾短信分类 | [SMS Spam Collection](https://archive.ics.uci.edu/dataset/228/sms+spam+collection); [SMS Spam Collection Dataset](https://www.kaggle.com/datasets/uciml/sms-spam-collection-dataset) | 5,574 | SMS 垃圾邮件集合是一组公开的 SMS 标记消息，为移动电话垃圾邮件研究而收集。 | [sms_spam](https://huggingface.co/datasets/sms_spam) |
-| spam_assassin  |  英语  |  垃圾邮件分类  |  [Apache SpamAssassin’s public datasets](https://spamassassin.apache.org/old/publiccorpus/); [Spam or Not Spam Dataset](https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset)  |   |  这是一组邮件消息，适合用于测试垃圾邮件过滤系统。  | [SpamAssassin](https://huggingface.co/datasets/talby/spamassassin) |
+| spam_assassin  |  英语  |  垃圾邮件分类  |  [Apache SpamAssassin’s public datasets](https://spamassassin.apache.org/old/publiccorpus/); [Spam or Not Spam Dataset](https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset)  | 10.7K |  这是一组邮件消息，适合用于测试垃圾邮件过滤系统。  | [SpamAssassin](https://huggingface.co/datasets/talby/spamassassin) |
 | enron_spam |  英语  |  垃圾邮件分类  | [enron_spam_data](https://github.com/MWiechmann/enron_spam_data); [Enron-Spam](https://www2.aueb.gr/users/ion/data/enron-spam/); [spam-mails-dataset](https://www.kaggle.com/datasets/venky73/spam-mails-dataset)  | 17,171 spam; 16,545 ham  |  Enron-Spam 数据集是 V. Metsis、I. Androutsopoulos 和 G. Paliouras 收集的绝佳资源  | [enron_spam](https://huggingface.co/datasets/SetFit/enron_spam) |
 
 

data/sms_spam.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65b1bb054a8b310bbf949cb2ce527a93f9e51401275e06f0e4b43d422edb46d5
+size 868244

examples/preprocess/process_sms_spam.py

@@ -0,0 +1,70 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+from collections import defaultdict
+import json
+import os
+from pathlib import Path
+import random
+import re
+import sys
+
+pwd = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(os.path.join(pwd, '../../'))
+
+from datasets import load_dataset
+from tqdm import tqdm
+
+from project_settings import project_path
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--dataset_path", default="sms_spam", type=str)
+    parser.add_argument(
+        "--dataset_cache_dir",
+        default=(project_path / "hub_datasets").as_posix(),
+        type=str
+    )
+    parser.add_argument(
+        "--output_file",
+        default=(project_path / "data/sms_spam.jsonl"),
+        type=str
+    )
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    dataset_dict = load_dataset(
+        path=args.dataset_path,
+        cache_dir=args.dataset_cache_dir,
+    )
+    print(dataset_dict)
+
+    with open(args.output_file, "w", encoding="utf-8") as f:
+        for sample in tqdm(dataset_dict["train"]):
+            # print(sample)
+            text = sample["sms"]
+            label = sample["label"]
+
+            text = text.strip()
+            label = "spam" if label == 1 else "ham"
+
+            row = {
+                "text": text,
+                "label": label,
+                "data_source": "sms_spam",
+                "split": "train"
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write("{}\n".format(row))
+
+    return
+
+
+if __name__ == '__main__':
+    main()

main.py

@@ -1,5 +1,23 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+from datasets import load_dataset, DownloadMode
+
+
+dataset = load_dataset(
+    "spam_detect.py",
+    name="sms_spam",
+    split="train",
+    cache_dir=None,
+    download_mode=DownloadMode.FORCE_REDOWNLOAD
+)
+
+for sample in dataset:
+    text = sample["text"]
+    label = sample["label"]
+
+    print(text)
+    print(label)
+    print("-" * 150)
 
 
 if __name__ == '__main__':

requirements.txt

@@ -1,2 +1,3 @@
 datasets==2.10.1
+fsspec==2023.9.2
 tqdm==4.66.1

spam_detect.py

@@ -1,5 +1,99 @@
 #!/usr/bin/python3
 # -*- coding: utf-8 -*-
+from collections import defaultdict
+import json
+from pathlib import Path
+import random
+import re
+from typing import Any, Dict, List, Tuple
+
+import datasets
+
+
+_urls = {
+    "sms_spam": "data/sms_spam.jsonl"
+}
+
+
+_CITATION = """\
+@dataset{spam_detect,
+  author       = {Xing Tian},
+  title        = {spam_detect},
+  month        = sep,
+  year         = 2023,
+  publisher    = {Xing Tian},
+  version      = {1.0},
+}
+"""
+
+
+class SpamDetect(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    intent_configs = list()
+    for name in _urls.keys():
+        config = datasets.BuilderConfig(name=name, version=VERSION, description=name)
+        intent_configs.append(config)
+
+    BUILDER_CONFIGS = [
+        *intent_configs,
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            "text": datasets.Value("string"),
+            "label": datasets.Value("string"),
+            "data_source": datasets.Value("string"),
+        })
+
+        return datasets.DatasetInfo(
+            features=features,
+            supervised_keys=None,
+            homepage="",
+            license="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        url = _urls[self.config.name]
+        dl_path = dl_manager.download(url)
+        archive_path = dl_path
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"archive_path": archive_path, "split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"archive_path": archive_path, "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, split):
+        """Yields examples."""
+        archive_path = Path(archive_path)
+
+        idx = 0
+
+        with open(archive_path, "r", encoding="utf-8") as f:
+            for row in f:
+                sample = json.loads(row)
+
+                if sample["split"] != split:
+                    continue
+
+                yield idx, {
+                    "text": sample["text"],
+                    "label": sample["label"],
+                    "data_source": sample["data_source"],
+                }
+                idx += 1
 
 
 if __name__ == '__main__':