checksum error subjqa dataset
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['Hi @RensDimmendaal, \r\n\r\nI\'m sorry but I can\'t reproduce your bug:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n   ...: ds = load_dataset("subjqa", "electronics")\r\nDownloading builder script: 9.15kB [00:00, 4.10MB/s]                                                                                                                                                       \r\nDownloading metadata: 17.7kB [00:00, 8.51MB/s]                                                                                                                                                             \r\nDownloading and preparing dataset subjqa/electronics (download: 10.86 MiB, generated: 3.01 MiB, post-processed: Unknown size, total: 13.86 MiB) to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd...\r\nDownloading data: 11.4MB [00:03, 3.50MB/s]\r\nDataset subjqa downloaded and prepared to .../.cache/huggingface/datasets/subjqa/electronics/1.1.0/e5588f9298ff2d70686a00cc377e4bdccf4e32287459e3c6baf2dc5ab57fe7fd. Subsequent calls will reuse this data.\r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 605.09it/s]\r\n\r\nIn [2]: ds\r\nOut[2]: \r\nDatasetDict({\r\n    train: Dataset({\r\n        features: [\'domain\', \'nn_mod\', \'nn_asp\', \'query_mod\', \'query_asp\', \'q_reviews_id\', \'question_subj_level\', \'ques_subj_score\', \'is_ques_subjective\', \'review_id\', \'id\', \'title\', \'context\', \'question\', \'answers\'],\r\n        num_rows: 1295\r\n    })\r\n    test: Dataset({\r\n        features: [\'domain\', \'nn_mod\', \'nn_asp\', \'query_mod\', \'query_asp\', \'q_reviews_id\', \'question_subj_level\', \'ques_subj_score\', \'is_ques_subjective\', \'review_id\', \'id\', \'title\', \'context\', \'question\', \'answers\'],\r\n        num_rows: 358\r\n    })\r\n    validation: Dataset({\r\n        features: [\'domain\', \'nn_mod\', \'nn_asp\', \'query_mod\', \'query_asp\', \'q_reviews_id\', \'question_subj_level\', \'ques_subj_score\', \'is_ques_subjective\', \'review_id\', \'id\', \'title\', \'context\', \'question\', \'answers\'],\r\n        num_rows: 255\r\n    })\r\n})\r\n```\r\n\r\nCould you please try again and see if the problem persists?\r\n\r\nIf that is the case, you can circumvent the issue by passing `ignore_verifications`:\r\n```python\r\nds = load_dataset("subjqa", "electronics", ignore_verifications=True)'
 "Thanks checking!\r\n\r\nYou're totally right. I don't know what's changed, but I'm glad it's working now!\r\n\r\n"]
## Describe the bug

I get a checksum error when loading the `subjqa` dataset (used in the transformers book).

## Steps to reproduce the bug
```python
from datasets import load_dataset
subjqa = load_dataset("subjqa","electronics")
```

## Expected results
Loading the dataset

## Actual results

```
---------------------------------------------------------------------------

NonMatchingChecksumError                  Traceback (most recent call last)

<ipython-input-2-d2857d460155> in <module>()
      2 from datasets import load_dataset
      3 
----> 4 subjqa = load_dataset("subjqa","electronics")

3 frames

/usr/local/lib/python3.7/dist-packages/datasets/utils/info_utils.py in verify_checksums(expected_checksums, recorded_checksums, verification_name)
     38     if len(bad_urls) > 0:
     39         error_msg = "Checksums didn't match" + for_verification_name + ":\n"
---> 40         raise NonMatchingChecksumError(error_msg + str(bad_urls))
     41     logger.info("All the checksums matched successfully" + for_verification_name)
     42 

NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://github.com/lewtun/SubjQA/archive/refs/heads/master.zip']
```

## Environment info

Google colab

- `datasets` version: 1.18.2
- Platform: Linux-5.4.144+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.12
- PyArrow version: 3.0.0
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