Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Column() changed from object to string in row 0
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
df = pandas_read_json(f)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
obj = self._get_object_parser(self.data)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
self._parse()
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1402, in _parse
self.obj = DataFrame(
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/frame.py", line 778, in __init__
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
index = _extract_index(arrays)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
raise ValueError(
ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
return next(iter(self.iter(batch_size=n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
for key, example in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
for key, pa_table in self._iter_arrow():
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow
for key, pa_table in iterator:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
raise e
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Stage 1 Packed Pretraining Dataset
This dataset contains preprocessed and token-packed .bin files intended for use in pretraining a decoder-only Transformer language model.
Dataset Contents
- Each
.binfile contains a fixed number of samples, where each sample is exactly 8192 tokens long. - Samples are grouped into batches of 125 samples, totaling 1.024 million tokens per batch.
- Each file (called a "block") contains 62500 samples (approximately 512 million tokens).
- All samples are tokenized using the
GPT2TokenizerFastfrom Hugging Face Transformers.
Structure
- Format: Binary files (
int32) containing token IDs. - File naming:
stage1_block_0000.bin,stage1_block_0001.bin, etc. - Tokenizer:
GPT2TokenizerFastwitheos_tokenused as a separator and padding token. - Context length: 8192 tokens per sample.
Source Datasets
Tokens were drawn from a diverse mix of high-quality open datasets:
C4 (en)Wikipedia (2023/11 dump)OpenWebTextCCNewsGutenbergarXivBookCorpus OpenS2ORCTriviaQAPAQNatural Questions
Each dataset was assigned a token quota to ensure a balanced mix.
Preprocessing & Packing Strategy
- Samples were streamed using Hugging Face Datasets with shuffling.
- Texts were tokenized, filtered using a garbage filter, and concatenated with separator tokens.
- Samples were packed into fixed-length chunks of 8192 tokens.
- Leftover tokens from one batch are carried forward to the next to ensure no token duplication or loss.
Garbage Filtering Heuristics:
- Removed texts with:
- Too few words or characters.
- High symbol-to-alphanumeric ratio.
- Excessive character repetition.
- Very low word diversity.
Usage Example
You can load and decode tokens using PyTorch:
import torch
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
with open("stage1_block_0000.bin", "rb") as f:
tokens = torch.frombuffer(f.read(), dtype=torch.int32)
sample = tokens[:8192].tolist()
text = tokenizer.decode(sample)
print(text)
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