Datasets:

mzzhang2014
/

glyph_machina_medieval_lines

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
                  raise ValueError(
                  ...<2 lines>...
                  )
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              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/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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glyph_machina_medieval_lines

Noisy HTR pretraining set: text-line crops from pre-Elizabeth-I English legal manuscripts (AALT scans), with machine-generated transcriptions (confidence prefixes stripped, confidence-filtered upstream). Line images are dewarped, background-subtracted, inverted, 64 px tall.

Format: page-grouped WebDataset

data/*.tar are WebDataset shards (~1 GB each). One sample = one page. For a page whose key is e.g. H4-E159no176-bE159no176dorses-IMG_0698:

H4-E159no176-bE159no176dorses-IMG_0698.00.png   line 0 (reading order, y-sorted)
H4-E159no176-bE159no176dorses-IMG_0698.01.png   line 1
...
H4-E159no176-bE159no176dorses-IMG_0698.json     {"texts": ["...", "...", ...]}

texts[i] is the transcription of {key}.{i:02d}.png; up to 80 lines per page. Shards named b####.tar mirror the extraction batches; loose-####.tar hold the original run. Pages never straddle shards. The dataset is being extended — new b####.tar shards appear as extraction proceeds.

Streaming on a GPU cluster (no local storage)

Use the webdataset library — it streams tars over HTTP and yields one dict per page. (Prefer it over datasets.load_dataset("webdataset", ...) here: pages have varying line counts, so samples have heterogeneous columns, which the HF builder dislikes but webdataset handles natively.)

import io, json
import webdataset as wds
from huggingface_hub import get_token, HfApi

repo = "mzzhang2014/glyph_machina_medieval_lines"
names = [f for f in HfApi().list_repo_files(repo, repo_type="dataset")
         if f.endswith(".tar")]
urls = [f"pipe:curl -sfL -H 'Authorization: Bearer {get_token()}' "
        f"https://huggingface.co/datasets/{repo}/resolve/main/{n}" for n in names]

def to_page(sample):
    keys = sorted(k for k in sample if k.endswith(".png"))
    return {"__key__": sample["__key__"],
            "images": [sample[k] for k in keys],          # raw PNG bytes
            "texts": json.loads(sample["json"])["texts"]}

ds = (wds.WebDataset(urls, shardshuffle=True, nodesplitter=wds.split_by_node)
      .map(to_page))

Wrap in wds.WebLoader for multi-worker loading; shards are the unit of shuffling/distribution across nodes. Decode PNGs with PIL.Image.open(io.BytesIO(b)) (mode L, height 64, white-on-black).

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