huggingface / llm-academy Duplication

Intro

For duplication, we look at places in code where there are 6 or more lines of code that are exactly the same.
Before duplication is calculated, the code is cleaned to remove empty lines, comments, and frequently duplicated constructs such as imports.
You should aim at having as little as possible (<5%) of duplicated code as high-level of duplication can lead to maintenance difficulties, poor factoring, and logical contradictions.

Learn more...

Duplication Overall

49% duplication:

987 cleaned lines of cleaned code (without empty lines, comments, and frequently duplicated constructs such as imports)
490 duplicated lines

26 duplicates

dependency graphs: 2D graph | 3D graph | 3D graph (with duplicates)...

Duplication per Extension

Duplication per Component (primary)

Longest Duplicates

The list of 26 longest duplicates.

See data for all 26 duplicates...

Size	#	Folders	Files	Lines	Code
33	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	473:505 (3%) 747:779 (3%)	view
33	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	341:373 (3%) 615:647 (3%)	view
26	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	270:295 (2%) 544:569 (2%)	view
21	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	397:417 (2%) 671:691 (2%)	view
21	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	451:471 (2%) 725:745 (2%)	view
21	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	375:395 (2%) 649:669 (2%)	view
16	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	297:312 (1%) 571:586 (1%)	view
15	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	419:433 (1%) 693:707 (1%)	view
15	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	435:449 (1%) 709:723 (1%)	view
10	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	804:813 (1%) 829:838 (1%)	view
10	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	319:328 (1%) 593:602 (1%)	view
9	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	331:339 (<1%) 605:613 (<1%)	view
8	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	904:911 (<1%) 933:940 (<1%)	view
7	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	172:178 (<1%) 804:810 (<1%)	view
7	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	172:178 (<1%) 247:253 (<1%)	view
7	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	172:178 (<1%) 829:835 (<1%)	view
7	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	247:253 (<1%) 804:810 (<1%)	view
7	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	247:253 (<1%) 829:835 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	544:549 (<1%) 804:809 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	270:275 (<1%) 829:834 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	247:252 (<1%) 544:549 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	172:177 (<1%) 544:549 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	544:549 (<1%) 829:834 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	172:177 (<1%) 270:275 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	270:275 (<1%) 804:809 (<1%)	view
6	x 2	session1 session1	deep-learning.ipynb deep-learning.ipynb	247:252 (<1%) 270:275 (<1%)	view