Issue: Problems in downloading Amazon reviews from HF
State: closed
Author: 610v4nn1

### Describe the bug

I have a script downloading `amazon_reviews_multi`.

When the download starts, I get

```
Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 1.43MB/s]
Downloading data files: 100%|██████████| 1/1 [00:01<00:00,  1.54s/it]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 842.40it/s]
Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 928kB/s]
Downloading data files: 100%|██████████| 1/1 [00:01<00:00,  1.42s/it]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 832.70it/s]
Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]
Downloading data: 243B [00:00, 1.81MB/s]
Downloading data files: 100%|██████████| 1/1 [00:01<00:00,  1.40s/it]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 1294.14it/s]
Generating train split:   0%|          | 0/200000 [00:00<?, ? examples/s]
```

the file is clearly too small to contain the requested dataset, in fact it contains en error message:

```
<?xml version="1.0" encoding="UTF-8"?>
<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error>
```

obviously the script fails:

```
>           raise DatasetGenerationError("An error occurred while generating the dataset") from e
E           datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
```



### Steps to reproduce the bug

1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE")

### Expected behavior

I would expect the dataset to be downloaded and processed

### Environment info

* The problem is present with both datasets 2.12.0 and 2.14.2
* python version 3.10.12

Comments:
Thanks for reporting, @610v4nn1.

Indeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.

We are adding a notification about this issue to the dataset card.

See: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 

The dataset can be accessed from https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi.

For those willing to transform the csv files from Kaggle into Huggingface datasets for their NLP course (exercise on summarisation), you can use this code on Google Collab:

`from datasets import load_dataset

import pandas as pd
from datasets import Dataset, DatasetDict

# Load your CSV previously downloaded files from Kaggle on Google Collab
train_csv_path = "/content/train.csv"
validation_csv_path = "/content/validation.csv"
test_csv_path = '/content/test.csv'

# Read CSV files into pandas DataFrames
train_df = pd.read_csv(train_csv_path, engine='python')
validation_df = pd.read_csv(validation_csv_path, engine='python')
test_df = pd.read_csv(test_csv_path, engine='python')

# Filter by language ('es' for Spanish and 'en' for English)
spanish_train_df = train_df[train_df['language'] == 'es']
spanish_validation_df = validation_df[validation_df['language'] == 'es']
spanish_test_df = test_df[test_df['language'] == 'es']

english_train_df = train_df[train_df['language'] == 'en']
english_validation_df = validation_df[validation_df['language'] == 'en']
english_test_df = test_df[test_df['language'] == 'en']

# Create Hugging Face datasets
spanish_dataset = DatasetDict({
    'train': Dataset.from_pandas(spanish_train_df),
    'validation': Dataset.from_pandas(spanish_validation_df),
    'test': Dataset.from_pandas(spanish_test_df)
})

english_dataset = DatasetDict({
    'train': Dataset.from_pandas(english_train_df),
    'validation': Dataset.from_pandas(english_validation_df),
    'test': Dataset.from_pandas(english_test_df)
})
english_dataset = english_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])
spanish_dataset = spanish_dataset.remove_columns(['Unnamed: 0', '__index_level_0__'])`