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: EmptyDataError
Message: No columns to parse from file
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows_from_streaming.py", line 132, in compute_first_rows_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2211, 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 1235, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1384, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1040, in __iter__
yield from islice(self.ex_iterable, self.n)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
for key, pa_table in self.generate_tables_fn(**self.kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 185, in _generate_tables
csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 75, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 778, in xpandas_read_csv
return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 912, in read_csv
return _read(filepath_or_buffer, kwds)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 577, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1407, in __init__
self._engine = self._make_engine(f, self.engine)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1679, in _make_engine
return mapping[engine](f, **self.options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
self._reader = parsers.TextReader(src, **kwds)
File "pandas/_libs/parsers.pyx", line 557, in pandas._libs.parsers.TextReader.__cinit__
pandas.errors.EmptyDataError: No columns to parse from fileNeed help to make the dataset viewer work? Open a discussion for direct support.
YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/datasets-cards)
NER Fine-Tuning
We use Flair for fine-tuning NER models on HIPE-2022 datasets from HIPE-2022 Shared Task.
All models are fine-tuned on A10 (24GB) and A100 (40GB) instances from Lambda Cloud using Flair:
$ git clone https://github.com/flairNLP/flair.git
$ cd flair && git checkout 419f13a05d6b36b2a42dd73a551dc3ba679f820c
$ pip3 install -e .
$ cd ..
Clone this repo for fine-tuning NER models:
$ git clone https://github.com/stefan-it/hmTEAMS.git
$ cd hmTEAMS/bench
Authorize via Hugging Face CLI (needed because hmTEAMS is currently only available after approval):
# Use access token from https://huggingface.co/settings/tokens
$ huggingface-cli login login
We use a config-driven hyper-parameter search. The script flair-fine-tuner.py can be used to
fine-tune NER models from our Model Zoo.
Benchmark
We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana. The following table shows an overview of used datasets.
| Language | Datasets |
|---|---|
| English | AjMC - TopRes19th |
| German | AjMC - NewsEye |
| French | AjMC - ICDAR-Europeana - LeTemps - NewsEye |
| Finnish | NewsEye |
| Swedish | NewsEye |
| Dutch | ICDAR-Europeana |
Results
We report averaged F1-score over 5 runs with different seeds on development set:
| Model | English AjMC | German AjMC | French AjMC | German NewsEye | French NewsEye | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | French LeTemps | English TopRes19th | Avg. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| hmBERT (32k) Schweter et al. | 85.36 ± 0.94 | 89.08 ± 0.09 | 85.10 ± 0.60 | 39.65 ± 1.01 | 81.47 ± 0.36 | 77.28 ± 0.37 | 82.85 ± 0.83 | 82.11 ± 0.61 | 77.21 ± 0.16 | 65.73 ± 0.56 | 80.94 ± 0.86 | 76.98 |
| hmTEAMS (Ours) | 86.41 ± 0.36 | 88.64 ± 0.42 | 85.41 ± 0.67 | 41.51 ± 2.82 | 83.20 ± 0.79 | 79.27 ± 1.88 | 82.78 ± 0.60 | 88.21 ± 0.39 | 78.03 ± 0.39 | 66.71 ± 0.46 | 81.36 ± 0.59 | 78.32 |
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