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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/imthanhlv/binhvq_dedup/resolve/main/train.jsonl.zst'
Traceback: Traceback (most recent call last):
File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 485, in compute_first_rows_response
rows = get_rows(
File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 120, in decorator
return func(*args, **kwargs)
File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 176, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 917, in __iter__
for key, example in ex_iterable:
File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
yield from self.generate_examples_fn(**self.kwargs)
File "/tmp/modules-cache/datasets_modules/datasets/imthanhlv--binhvq_dedup/60d03216e35c1b592ca70ae909d1c1f504292e38e9622d04108a14fe9c34868e/binhvq_dedup.py", line 78, in _generate_examples
for doc in reader.read_jsonl_zst(filepath):
File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/lm_dataformat/__init__.py", line 245, in read_jsonl_zst
with open(file, 'rb') as fh:
FileNotFoundError: [Errno 2] No such file or directory: 'https://huggingface.co/datasets/imthanhlv/binhvq_dedup/resolve/main/train.jsonl.zst'Need help to make the dataset viewer work? Open an discussion for direct support.
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BinhVQ dedup
Important: Please install lm_dataformat by pip install lm_dataformat before using this dataset
How to use
import datasets
dataset = datasets.load_dataset("imthanhlv/binhvq_dedup")
Dataset information
This dataset was created from https://github.com/binhvq/news-corpus dump with date 21/05/2021. I applied some simple preprocessing:
- Using BeautifulSoup to clean content
- Each record is concatenate of (title + "\n" + sapo + "\n" + content)
- Then perform shuffling + split train & validation + deduplicate (exact match using sha256)
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