Datasets:
[update]add main
Browse files- README.md +7 -0
- e_commerce_customer_service.py +141 -0
- main.py +17 -0
README.md
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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.
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数据内容:
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faq.json: 包含通用问题的问答对.
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product.jsonl: 包含一些商品信息.
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examples 中包含收集商品信息的爬虫代码.
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requirements.txt
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```text
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beautifulsoup4==4.12.2
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requests==2.31.0
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tqdm==4.65.0
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```
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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.
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数据内容:
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+
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faq.json: 包含通用问题的问答对.
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+
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product.jsonl: 包含一些商品信息.
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examples 中包含收集商品信息的爬虫代码.
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python==3.8.10
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requirements.txt
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```text
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beautifulsoup4==4.12.2
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requests==2.31.0
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tqdm==4.65.0
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datasets==2.14.4
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```
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e_commerce_customer_service.py
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#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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from glob import glob
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import json
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import os
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from pathlib import Path
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import datasets
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_URLS = {
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"faq": "data/faq.json",
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"product": "data/product.jsonl",
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}
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_CITATION = """\
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@dataset{early_media,
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author = {Xing Tian},
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title = {e_commerce_customer_service},
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month = aug,
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year = 2023,
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publisher = {Xing Tian},
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version = {1.0},
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}
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"""
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class TelemarketingVoiceClassification(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="faq", version=VERSION, description="faq"),
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datasets.BuilderConfig(name="product", version=VERSION, description="product"),
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]
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def _info(self):
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if self.config.name == "faq":
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features = datasets.Features(
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{
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"url": datasets.Value("string"),
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"question": datasets.Value("string"),
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"answer": datasets.Value("string"),
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"label": datasets.Value("string"),
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}
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)
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elif self.config.name == "product":
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features = datasets.Features(
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{
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"title": datasets.Value("string"),
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"brand": datasets.Value("string"),
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"review": datasets.Value("string"),
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"description": datasets.Value("string"),
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"mpn": datasets.Value("string"),
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"color": datasets.Sequence(datasets.Value("string")),
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"size": datasets.Sequence(datasets.Value("string")),
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"sku": datasets.Value("string"),
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"ratingValue": datasets.Value("float32"),
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"reviewCount": datasets.Value("int64"),
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"overview": datasets.Value("string"),
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"category": datasets.Value("string"),
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"url": datasets.Value("string"),
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}
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)
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else:
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raise NotImplementedError
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return datasets.DatasetInfo(
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features=features,
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supervised_keys=None,
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homepage="",
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license="",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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url = _URLS[self.config.name]
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dl_path = dl_manager.download_and_extract(url)
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archive_path = dl_path
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"archive_path": archive_path, "split": "train"},
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),
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]
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def _generate_faq(self, archive_path, split):
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archive_path = Path(archive_path)
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with open(archive_path, "r", encoding="utf-8") as f:
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faq = json.load(f)
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idx = 0
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for qa in faq:
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yield idx, {
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"url": qa["url"],
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"question": qa["question"],
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"answer": qa["answer"],
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"label": qa["label"],
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}
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idx += 1
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def _generate_product(self, archive_path, split):
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archive_path = Path(archive_path)
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idx = 0
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with open(archive_path, "r", encoding="utf-8") as f:
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for row in f:
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row = json.loads(row)
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yield idx, {
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"title": row["title"],
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"brand": row["brand"],
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"review": row["review"],
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"description": row["description"],
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"mpn": row["mpn"],
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"color": row["color"],
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"size": row["size"],
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"sku": row["sku"],
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"ratingValue": float(row["ratingValue"]) if row["ratingValue"] is not None else None,
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"reviewCount": int(row["reviewCount"]) if row["reviewCount"] is not None else None,
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"overview": row["overview"],
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"category": row["category"],
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"url": row["url"],
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}
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idx += 1
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def _generate_examples(self, archive_path, split):
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"""Yields examples."""
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if self.config.name == "faq":
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return self._generate_faq(archive_path, split)
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elif self.config.name == "product":
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return self._generate_product(archive_path, split)
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else:
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raise NotImplementedError
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if __name__ == '__main__':
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pass
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main.py
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@@ -0,0 +1,17 @@
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#!/usr/bin/python3
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# -*- coding: utf-8 -*-
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from datasets import load_dataset
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dataset = load_dataset(
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"e_commerce_customer_service.py",
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name="faq",
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# name="product",
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split="train",
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)
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for sample in dataset:
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print(sample)
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if __name__ == '__main__':
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pass
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