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Error code: ConfigNamesError Exception: ValueError Message: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): (None, {}), NamedSplit('validation'): (None, {}), NamedSplit('test'): ('json', {})} Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1054, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 513, in infer_module_for_data_files raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): (None, {}), NamedSplit('validation'): (None, {}), NamedSplit('test'): ('json', {})}
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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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