Pixel-based Pre-training
Collection
[EMNLP'24] [Autoregressive Pre-Training on Pixels and Texts](https://arxiv.org/pdf/2404.10710).
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6 items
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Updated
Error code: StreamingRowsError Exception: error Message: Error -3 while decompressing data: invalid code lengths set Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/utils.py", line 90, in get_rows_or_raise return get_rows( File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/utils.py", line 68, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2012, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 188, in __iter__ for key_example in islice(self.generate_examples_fn(**gen_kwags), shard_example_idx_start, None): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 113, 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 45, in _get_pipeline_from_tar current_example[field_name.lower()] = f.read() File "/usr/local/lib/python3.9/gzip.py", line 300, in read return self._buffer.read(size) File "/usr/local/lib/python3.9/gzip.py", line 495, in read uncompress = self._decompressor.decompress(buf, size) zlib.error: Error -3 while decompressing data: invalid code lengths set
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This repository contains the rendered GLUE dataset evaluated in the paper Autoregressive Pre-Training on Pixels and Texts (EMNLP 2024). For detailed instructions on how to use the model, please visit our GitHub page.
@misc{chai2024autoregressivepretrainingpixelstexts,
title = {Autoregressive Pre-Training on Pixels and Texts},
author = {Chai, Yekun and Liu, Qingyi and Xiao, Jingwu and Wang, Shuohuan and Sun, Yu and Wu, Hua},
year = {2024},
eprint = {2404.10710},
archiveprefix = {arXiv},
primaryclass = {cs.CL},
url = {https://arxiv.org/abs/2404.10710},
}