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Datasets:
jingxiang11111
/
amazon_reviews_for_rec

Size:
1M<n<10M
Tags:
recommendation-system
multimodal
e-commerce
amazon
webdataset
computer-vision
Libraries:
WebDataset
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 55, in _get_pipeline_from_tar
                  current_example[field_name] = cls.DECODERS[data_extension](current_example[field_name])
                File "/usr/local/lib/python3.9/json/__init__.py", line 346, in loads
                  return _default_decoder.decode(s)
                File "/usr/local/lib/python3.9/json/decoder.py", line 337, in decode
                  obj, end = self.raw_decode(s, idx=_w(s, 0).end())
                File "/usr/local/lib/python3.9/json/decoder.py", line 355, in raw_decode
                  raise JSONDecodeError("Expecting value", s, err.value) from None
              json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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Amazon Reviews for Multimodal Recommendation

GitHub Repo stars

数据集概览

这是一个为端到端多模态推荐系统设计的WebDataset 格式的数据集。该数据集源自 Amazon 评论，包含了处理后的文本（评论）和图像数据，旨在支持高效的分布式训练。

该数据集是 GitHub 项目 的一部分，该项目基于 Apache Beam 和 PyTorch DDP 构建，涵盖了从分布式特征工程、数据加载到复杂模型（MMoE）分布式训练的整个工作流。我们已将所用的数据集、验证集和模型全部开源。

数据集统计

子集名称 文件格式 样本数量 文件大小
train .tar.gz 1848930 128 GB
valid .tar.gz 22281 2 GB

如何下载和加载数据集？

由于数据集是 WebDataset 格式，我们推荐使用 WebDataset 库 进行流式加载，这对于分布式训练非常高效。

使用 webdataset 直接加载

你可以直接通过 webdataset 库从 Hugging Face Hub 加载数据。请注意，webdataset 库需要你本地安装。

import webdataset as wds

# 加载训练集中的所有分片
# webdataset库会自动处理从Hugging Face下载文件
train_dataset = wds.WebDataset("[https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/train/data-*.tar.gz](https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/train/data-*.tar.gz)")

# 加载验证集中的所有分片
valid_dataset = wds.WebDataset("[https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/valid/data-*.tar.gz](https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/valid/data-*.tar.gz)")

# 你也可以通过 resolve/main/{folder}/{filename} 这样的URL来加载单个分片
# single_shard = wds.WebDataset("[https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/train/data-000000-000000.tar.gz](https://huggingface.co/datasets/jingxiang11111/amazon_reviews_for_rec/resolve/main/train/data-000000-000000.tar.gz)")

# 示例：遍历一个数据集
for sample in train_dataset.decode("pil").to_tuple("jpg", "txt"):
    image, text = sample
    print(f"Sample Text: {text}")
    print(f"Sample Image format: {image.format}")
    break
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