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The dataset generation failed
Error code: DatasetGenerationError
Exception: ReadError
Message: unexpected end of data
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1607, in _prepare_split_single
for key, record in generator:
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 687, in wrapped
for item in generator(*args, **kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 122, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 44, in _get_pipeline_from_tar
current_example[field_name.lower()] = f.read()
File "/usr/local/lib/python3.9/tarfile.py", line 690, in read
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
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/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1486, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1643, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
caption
unknown | __key__
string | __url__
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|
1000
|
hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
|
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2000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
|
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10000
|
hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
|
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17000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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21000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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22000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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25000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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32000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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35000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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37000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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38000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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39000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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47000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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50000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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54000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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56000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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67000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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70000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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75000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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102000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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105000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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108000
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
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hf://datasets/6DammK9/danbooru2024-captions-1ktar@ec541d176d968867b36f6a9d8f33ae7c7715d0b7/captions/0000.tar
|
End of preview.
Danbooru 2024 captions only in 1k tar
- Raw captions jointed by 7.62M unpublished extended dataset from KBlueLeaf/danbooru2023-metadata-database and 0.48M generated dataset via Minthy/ToriiGate-v0.4-7B in exl2-8bpw mode. There are 8.13M in total.
python convert_meta_to_tar.py
Reading source JSON
Keys count: 8136011
max id: 8360499
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [07:32<00:00, 2.21it/s]
Files written: 1000
- It aligns to deepghs/danbooru2024.
>python merge_tag_and_caption_to_meta.py
loading JSON_ID
loading JSON_TAGS
loading JSON_CAPTION
loading JSON_LATENT
start merging
merging json files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 8005010/8005010 [00:51<00:00, 155076.84it/s]
ids: 8005010, missing tags: 0, missing caption: 0, missing latent: 0
writing OUTPUT_JSON
writing MISSING_JSON
Merge complete.
meta_cap.jsonhas been provided in compressed format if you want to train with kohyas triner. I have a "tag with caption" which is 14GB decompressed..
Core logic
The scripts / logs / docuements building this 1ktar, majroly batch_nlp_caption_exl.py
Artist and character first. Others can rely on pretrained knowledge. No fancy "quality" score for pretraining focused dataset.
rearranged_tags = [row.phi3v_horny, row.pixtral_caption, row.florence_short, row.florence_long]
must_exist = [tag for tag in rearranged_tags if tag]
tag = " ".join(must_exist)
- It is also reflected in nlp_stuffs.py.
How to build the "dataset" with speed
Refer to the tags-1ktar repo.
This repo is still in develeopment. My current task of building the dataset for "pretraining".
Hint: The files are in
*.captioninstead of*.txt. It should refers to default settings.
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