[update]add main

README.md

@@ -14,13 +14,20 @@ size_categories:
 是从 (lightinthebox)[https://www.lightinthebox.com/] 网站收集的电商数据. 此数据可用于电商客服机器人的研究.
 
 数据内容:
+
 faq.json: 包含通用问题的问答对.
+
 product.jsonl: 包含一些商品信息.
 
 examples 中包含收集商品信息的爬虫代码.
+
+python==3.8.10
+
 requirements.txt
 ```text
 beautifulsoup4==4.12.2
 requests==2.31.0
 tqdm==4.65.0
+datasets==2.14.4
 ```
+

e_commerce_customer_service.py

@@ -0,0 +1,141 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from glob import glob
+import json
+import os
+from pathlib import Path
+
+import datasets
+
+
+_URLS = {
+    "faq": "data/faq.json",
+    "product": "data/product.jsonl",
+}
+
+
+_CITATION = """\
+@dataset{early_media,
+  author       = {Xing Tian},
+  title        = {e_commerce_customer_service},
+  month        = aug,
+  year         = 2023,
+  publisher    = {Xing Tian},
+  version      = {1.0},
+}
+"""
+
+
+class TelemarketingVoiceClassification(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="faq", version=VERSION, description="faq"),
+        datasets.BuilderConfig(name="product", version=VERSION, description="product"),
+    ]
+
+    def _info(self):
+        if self.config.name == "faq":
+            features = datasets.Features(
+                {
+                    "url": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "label": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "product":
+            features = datasets.Features(
+                {
+                    "title": datasets.Value("string"),
+                    "brand": datasets.Value("string"),
+                    "review": datasets.Value("string"),
+                    "description": datasets.Value("string"),
+                    "mpn": datasets.Value("string"),
+                    "color": datasets.Sequence(datasets.Value("string")),
+                    "size": datasets.Sequence(datasets.Value("string")),
+                    "sku": datasets.Value("string"),
+                    "ratingValue": datasets.Value("float32"),
+                    "reviewCount": datasets.Value("int64"),
+                    "overview": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                }
+            )
+        else:
+            raise NotImplementedError
+
+        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_and_extract(url)
+        archive_path = dl_path
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"archive_path": archive_path, "split": "train"},
+            ),
+        ]
+
+    def _generate_faq(self, archive_path, split):
+        archive_path = Path(archive_path)
+
+        with open(archive_path, "r", encoding="utf-8") as f:
+            faq = json.load(f)
+
+        idx = 0
+        for qa in faq:
+            yield idx, {
+                "url": qa["url"],
+                "question": qa["question"],
+                "answer": qa["answer"],
+                "label": qa["label"],
+            }
+            idx += 1
+
+    def _generate_product(self, archive_path, split):
+        archive_path = Path(archive_path)
+
+        idx = 0
+        with open(archive_path, "r", encoding="utf-8") as f:
+            for row in f:
+                row = json.loads(row)
+
+                yield idx, {
+                    "title": row["title"],
+                    "brand": row["brand"],
+                    "review": row["review"],
+                    "description": row["description"],
+                    "mpn": row["mpn"],
+                    "color": row["color"],
+                    "size": row["size"],
+                    "sku": row["sku"],
+                    "ratingValue": float(row["ratingValue"]) if row["ratingValue"] is not None else None,
+                    "reviewCount": int(row["reviewCount"]) if row["reviewCount"] is not None else None,
+                    "overview": row["overview"],
+                    "category": row["category"],
+                    "url": row["url"],
+                }
+                idx += 1
+
+    def _generate_examples(self, archive_path, split):
+        """Yields examples."""
+        if self.config.name == "faq":
+            return self._generate_faq(archive_path, split)
+        elif self.config.name == "product":
+            return self._generate_product(archive_path, split)
+        else:
+            raise NotImplementedError
+
+
+if __name__ == '__main__':
+    pass

main.py

@@ -0,0 +1,17 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "e_commerce_customer_service.py",
+    name="faq",
+    # name="product",
+    split="train",
+)
+
+for sample in dataset:
+    print(sample)
+
+
+if __name__ == '__main__':
+    pass