Published January 27, 2020
| Version v1
Dataset
Open
Preprocessed Python Code Corpus
Authors/Creators
- 1. The University of Edinburgh
- 2. Free University of Bozen-Bolzano
- 3. Google Research
Description
A preprocessed code corpus for the Python programming language.
The corpus was used for the experiments in the paper Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code.
It contains preprocessed-tokenized files for training, validation, testing, and BPE encoding learning.
The BPE segmented versions of the above files are also included for three different encoding sizes i,e., 2000, 5000, and 10000 BPE merge operations as well as the learned BPE encodings.
Similar versions are also contained for splitting compound identifiers on camelCase and snake_case as in (Allamanis et al., 2015) as well as the corresponding subtoken maps.
Files
Files
(32.9 GB)
| Name | Size | |
|---|---|---|
|
md5:6a8e35b82f0a97a1909b624f7ff468a5
|
587.5 MB | Download |
|
md5:a0d1d73342e0ea294ae605d54e474247
|
4.9 GB | Download |
|
md5:21d298ddb428d550109e70c984231c27
|
6.3 GB | Download |
|
md5:ef42fa6efbc021a6bc00be918a9dd9fd
|
7.3 GB | Download |
|
md5:6470265df0e36a3f272f8d82a416c606
|
6.7 GB | Download |
|
md5:01b000e17528e5c40438c1ac2fc749b3
|
5.7 GB | Download |
|
md5:2b2b77ae2c3d91d9e25b71488a291af8
|
81.3 kB | Download |
|
md5:412841ba9eb5cfdf1981c6bcf57ed1a0
|
13.8 kB | Download |
|
md5:f0a98cfe12cc64b82ea63dbde58745ca
|
37.9 kB | Download |
|
md5:32c37a1385c50e94fc082b3a78a9f93a
|
100.1 MB | Download |
|
md5:e01d57f4625ef6f14f291ccb1bdc918c
|
128.5 MB | Download |
|
md5:5c0e4d9cf9f93e9c058069e9b95096aa
|
150.6 MB | Download |
|
md5:ef3c63ebb05378374b363047a668a89a
|
136.2 MB | Download |
|
md5:cc58af3f8a181815ad23312aef1c0455
|
117.6 MB | Download |
|
md5:c7d4f906eb0aecd489db9714ee5f7ffd
|
53.3 MB | Download |
|
md5:f94592551c04e241740526cf0d17b7a2
|
63.0 MB | Download |
|
md5:da966a012b7226e06642ce6fb86dca31
|
77.8 MB | Download |
|
md5:f6bad97ec45634269f035b1a354a320f
|
90.1 MB | Download |
|
md5:679d2760acbf33e51ccb4686ba349dba
|
82.2 MB | Download |
|
md5:2f4d3dc102c75cb45bc7506215cb4021
|
72.8 MB | Download |
|
md5:5a8fee72aa5cafac4ff7054bb30da68e
|
379.8 kB | Download |
|
md5:756dc817e1660e7eda048fe68f9f9809
|
95.9 MB | Download |
|
md5:d8f068fe5846bf440f2db7e621064acd
|
101.1 MB | Download |
|
md5:062bb5e23fbe732ecc1a465ad04aff91
|
82.1 MB | Download |
Additional details
Related works
- Is referenced by
- Dataset: 10.5281/zenodo.3628665 (DOI)
- Dataset: 10.5281/zenodo.3628638 (DOI)
- Is source of
- Other: DOI 10.5281/zenodo.3628628 (Handle)
Funding
- UK Research and Innovation
- EPSRC Centre for Doctoral Training in Data Science EP/L016427/1