Error code:   ConfigNamesError
Exception:    ReadTimeout
Message:      (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 4c2da59a-7852-4214-ad86-d8a84c4e8644)')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 79, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1263, in get_module
                  patterns = get_data_patterns(base_path, download_config=self.download_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 497, in get_data_patterns
                  return _get_data_files_patterns(resolver)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 272, in _get_data_files_patterns
                  data_files = pattern_resolver(pattern)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/data_files.py", line 384, in resolve_pattern
                  for filepath, info in fs.glob(pattern, detail=True, **glob_kwargs).items()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 416, in glob
                  path = self.resolve_path(path, revision=kwargs.get("revision")).unresolve()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 183, in resolve_path
                  repo_and_revision_exist, err = self._repo_and_revision_exist(repo_type, repo_id, revision)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 131, in _repo_and_revision_exist
                  self._api.repo_info(repo_id, revision=revision, repo_type=repo_type, timeout=HF_HUB_ETAG_TIMEOUT)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2588, in repo_info
                  return method(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2445, in dataset_info
                  r = get_session().get(path, headers=headers, timeout=timeout, params=params)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 602, in get
                  return self.request("GET", url, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 589, in request
                  resp = self.send(prep, **send_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/sessions.py", line 703, in send
                  r = adapter.send(request, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 66, in send
                  return super().send(request, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/adapters.py", line 635, in send
                  raise ReadTimeout(e, request=request)
              requests.exceptions.ReadTimeout: (ReadTimeoutError("HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)"), '(Request ID: 4c2da59a-7852-4214-ad86-d8a84c4e8644)')

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Dataset Card for ACL Anthology Corpus

This repository provides full-text and metadata to the ACL anthology collection (80k articles/posters as of September 2022) also including .pdf files and grobid extractions of the pdfs.

How is this different from what ACL anthology provides and what already exists?

We provide pdfs, full-text, references and other details extracted by grobid from the PDFs while ACL Anthology only provides abstracts.
There exists a similar corpus call ACL Anthology Network but is now showing its age with just 23k papers from Dec 2016.

>>> import pandas as pd
>>> df = pd.read_parquet('acl-publication-info.74k.parquet')
>>> df
         acl_id                                           abstract                                          full_text  corpus_paper_id                                  pdf_hash  ...  number volume journal editor  isbn
0      O02-2002  There is a need to measure word similarity whe...  There is a need to measure word similarity whe...         18022704  0b09178ac8d17a92f16140365363d8df88c757d0  ...    None   None    None   None  None
1      L02-1310                                                                                                                8220988  8d5e31610bc82c2abc86bc20ceba684c97e66024  ...    None   None    None   None  None
2      R13-1042  Thread disentanglement is the task of separati...  Thread disentanglement is the task of separati...         16703040  3eb736b17a5acb583b9a9bd99837427753632cdb  ...    None   None    None   None  None
3      W05-0819  In this paper, we describe a word alignment al...  In this paper, we describe a word alignment al...          1215281  b20450f67116e59d1348fc472cfc09f96e348f55  ...    None   None    None   None  None
4      L02-1309                                                                                                               18078432  011e943b64a78dadc3440674419821ee080f0de3  ...    None   None    None   None  None
...         ...                                                ...                                                ...              ...                                       ...  ...     ...    ...     ...    ...   ...
73280  P99-1002  This paper describes recent progress and the a...  This paper describes recent progress and the a...           715160  ab17a01f142124744c6ae425f8a23011366ec3ee  ...    None   None    None   None  None
73281  P00-1009  We present an LFG-DOP parser which uses fragme...  We present an LFG-DOP parser which uses fragme...          1356246  ad005b3fd0c867667118482227e31d9378229751  ...    None   None    None   None  None
73282  P99-1056  The processes through which readers evoke ment...  The processes through which readers evoke ment...          7277828  924cf7a4836ebfc20ee094c30e61b949be049fb6  ...    None   None    None   None  None
73283  P99-1051  This paper examines the extent to which verb d...  This paper examines the extent to which verb d...          1829043  6b1f6f28ee36de69e8afac39461ee1158cd4d49a  ...    None   None    None   None  None
73284  P00-1013  Spoken dialogue managers have benefited from u...  Spoken dialogue managers have benefited from u...         10903652  483c818c09e39d9da47103fbf2da8aaa7acacf01  ...    None   None    None   None  None

[73285 rows x 21 columns]

Dataset Summary

Dataframe with extracted metadata (table below with details) and full text of the collection for analysis : size 489M

Languages

en, zh and others

Dataset Structure

Dataframe

Data Instances

Each row is a paper from ACL anthology

Data Fields

Column name	Description
`acl_id`	unique ACL id
`abstract`	abstract extracted by GROBID
`full_text`	full text extracted by GROBID
`corpus_paper_id`	Semantic Scholar ID
`pdf_hash`	sha1 hash of the pdf
`numcitedby`	number of citations from S2
`url`	link of publication
`publisher`	-
`address`	Address of conference
`year`	-
`month`	-
`booktitle`	-
`author`	list of authors
`title`	title of paper
`pages`	-
`doi`	-
`number`	-
`volume`	-
`journal`	-
`editor`	-
`isbn`	-

Dataset Creation

The corpus has all the papers in ACL anthology - as of September'22

Source Data

Additional Information

Licensing Information

The ACL OCL corpus is released under the CC BY-NC 4.0. By using this corpus, you are agreeing to its usage terms.

Citation Information

If you use this corpus in your research please use the following BibTeX entry:

@Misc{acl-ocl,
    author =       {Shaurya Rohatgi, Yanxia Qin, Benjamin Aw, Niranjana Unnithan, Min-Yen Kan},
    title =        {The ACL OCL Corpus: advancing Open science in Computational Linguistics},
    howpublished = {arXiv},
    year =         {2022},
    url =          {https://huggingface.co/datasets/ACL-OCL/ACL-OCL-Corpus}
}

Acknowledgements

We thank Semantic Scholar for providing access to the citation-related data in this corpus.

Contributions

Thanks to @shauryr, Yanxia Qin and Benjamin Aw for adding this dataset.

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