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zhuqiming
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•
77e4a24
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Parent(s):
d05cebd
更新展示
Browse files- css_html.py +64 -0
- results.json +966 -0
- text_content.py +37 -0
- utils.py +27 -0
css_html.py
ADDED
@@ -0,0 +1,64 @@
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custom_css = """
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#changelog-text {
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font-size: 16px !important;
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}
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#changelog-text h2 {
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font-size: 18px !important;
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}
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.markdown-text {
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font-size: 16px !important;
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}
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#models-to-add-text {
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font-size: 18px !important;
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}
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#citation-button span {
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font-size: 16px !important;
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}
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#citation-button textarea {
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font-size: 16px !important;
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}
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#citation-button > label > button {
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margin: 6px;
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transform: scale(1.3);
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}
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#leaderboard-table {
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margin-top: 15px
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}
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#leaderboard-table-lite {
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margin-top: 15px
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}
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#search-bar-table-box > div:first-child {
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background: none;
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border: none;
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}
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#search-bar {
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padding: 0px;
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}
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/* Hides the final AutoEvalColumn */
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#llm-benchmark-tab-table table td:last-child,
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#llm-benchmark-tab-table table th:last-child {
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display: none;
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}
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/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
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table td:first-child,
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table th:first-child {
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max-width: 400px;
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overflow: auto;
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white-space: nowrap;
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}
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.tab-buttons button {
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font-size: 20px;
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}
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#scale-logo {
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border-style: none !important;
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box-shadow: none;
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display: block;
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margin-left: auto;
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margin-right: auto;
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max-width: 600px;
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}
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#scale-logo .download {
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display: none;
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}
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"""
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results.json
ADDED
@@ -0,0 +1,966 @@
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1 |
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{
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2 |
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"pass_1": [
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3 |
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{
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4 |
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"Model": "gpt-4o-mini",
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5 |
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"Domain": "Computation",
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6 |
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"Pass_at_k": 0.9038123167155425
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7 |
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},
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8 |
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{
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9 |
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"Model": "gpt-3.5-turbo",
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10 |
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"Domain": "Computation",
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11 |
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"Pass_at_k": 0.8340175953079179
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12 |
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},
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13 |
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{
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14 |
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"Model": "Qwen2-72B-Instruct-GPTQ-Int4",
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15 |
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"Domain": "Computation",
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16 |
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"Pass_at_k": 0.8686217008797654
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17 |
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},
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18 |
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{
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19 |
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"Model": "deepseek-coder-33b-instruct",
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20 |
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"Domain": "Computation",
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21 |
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"Pass_at_k": 0.8392961876832845
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22 |
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},
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23 |
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{
|
24 |
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"Model": "DeepSeek-Coder-V2-Lite-Instruct",
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25 |
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"Domain": "Computation",
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26 |
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"Pass_at_k": 0.8604105571847507
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27 |
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},
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28 |
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{
|
29 |
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"Model": "deepseek-coder-6.7b-instruct",
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30 |
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"Domain": "Computation",
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31 |
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"Pass_at_k": 0.8351906158357771
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32 |
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},
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33 |
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{
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34 |
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"Model": "CodeLlama-34b-Instruct-hf",
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35 |
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864 |
+
},
|
865 |
+
{
|
866 |
+
"Model": "DeepSeek-Coder-V2-Lite-Instruct",
|
867 |
+
"Domain": "Mean",
|
868 |
+
"Pass_at_k": 0.615879493228943
|
869 |
+
},
|
870 |
+
{
|
871 |
+
"Model": "deepseek-coder-6.7b-instruct",
|
872 |
+
"Domain": "Mean",
|
873 |
+
"Pass_at_k": 0.6152211001996906
|
874 |
+
},
|
875 |
+
{
|
876 |
+
"Model": "CodeLlama-34b-Instruct-hf",
|
877 |
+
"Domain": "Mean",
|
878 |
+
"Pass_at_k": 0.5706463475004796
|
879 |
+
},
|
880 |
+
{
|
881 |
+
"Model": "CodeLlama-13b-Instruct-hf",
|
882 |
+
"Domain": "Mean",
|
883 |
+
"Pass_at_k": 0.5774106930778998
|
884 |
+
},
|
885 |
+
{
|
886 |
+
"Model": "CodeLlama-7b-Instruct-hf",
|
887 |
+
"Domain": "Mean",
|
888 |
+
"Pass_at_k": 0.5826237976596688
|
889 |
+
},
|
890 |
+
{
|
891 |
+
"Model": "CodeQwen1.5-7B-Chat",
|
892 |
+
"Domain": "Mean",
|
893 |
+
"Pass_at_k": 0.6094825601888648
|
894 |
+
},
|
895 |
+
{
|
896 |
+
"Model": "Phi-3-medium-4k-instruct",
|
897 |
+
"Domain": "Mean",
|
898 |
+
"Pass_at_k": 0.6087859319852885
|
899 |
+
},
|
900 |
+
{
|
901 |
+
"Model": "Llama-2-13b-chat-hf",
|
902 |
+
"Domain": "Mean",
|
903 |
+
"Pass_at_k": 0.46487622755859775
|
904 |
+
},
|
905 |
+
{
|
906 |
+
"Model": "gpt-4o-mini",
|
907 |
+
"Domain": "Std",
|
908 |
+
"Pass_at_k": 0.14747641211035856
|
909 |
+
},
|
910 |
+
{
|
911 |
+
"Model": "gpt-3.5-turbo",
|
912 |
+
"Domain": "Std",
|
913 |
+
"Pass_at_k": 0.19743922837233668
|
914 |
+
},
|
915 |
+
{
|
916 |
+
"Model": "Qwen2-72B-Instruct-GPTQ-Int4",
|
917 |
+
"Domain": "Std",
|
918 |
+
"Pass_at_k": 0.169043537848292
|
919 |
+
},
|
920 |
+
{
|
921 |
+
"Model": "deepseek-coder-33b-instruct",
|
922 |
+
"Domain": "Std",
|
923 |
+
"Pass_at_k": 0.1634243695210041
|
924 |
+
},
|
925 |
+
{
|
926 |
+
"Model": "DeepSeek-Coder-V2-Lite-Instruct",
|
927 |
+
"Domain": "Std",
|
928 |
+
"Pass_at_k": 0.16346984877152868
|
929 |
+
},
|
930 |
+
{
|
931 |
+
"Model": "deepseek-coder-6.7b-instruct",
|
932 |
+
"Domain": "Std",
|
933 |
+
"Pass_at_k": 0.16363528852513812
|
934 |
+
},
|
935 |
+
{
|
936 |
+
"Model": "CodeLlama-34b-Instruct-hf",
|
937 |
+
"Domain": "Std",
|
938 |
+
"Pass_at_k": 0.16828060893964333
|
939 |
+
},
|
940 |
+
{
|
941 |
+
"Model": "CodeLlama-13b-Instruct-hf",
|
942 |
+
"Domain": "Std",
|
943 |
+
"Pass_at_k": 0.2055227025195004
|
944 |
+
},
|
945 |
+
{
|
946 |
+
"Model": "CodeLlama-7b-Instruct-hf",
|
947 |
+
"Domain": "Std",
|
948 |
+
"Pass_at_k": 0.17281566921046676
|
949 |
+
},
|
950 |
+
{
|
951 |
+
"Model": "CodeQwen1.5-7B-Chat",
|
952 |
+
"Domain": "Std",
|
953 |
+
"Pass_at_k": 0.17954181233010988
|
954 |
+
},
|
955 |
+
{
|
956 |
+
"Model": "Phi-3-medium-4k-instruct",
|
957 |
+
"Domain": "Std",
|
958 |
+
"Pass_at_k": 0.15450285935340832
|
959 |
+
},
|
960 |
+
{
|
961 |
+
"Model": "Llama-2-13b-chat-hf",
|
962 |
+
"Domain": "Std",
|
963 |
+
"Pass_at_k": 0.2409835679041833
|
964 |
+
}
|
965 |
+
]
|
966 |
+
}
|
text_content.py
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
HEAD_TEXT = """
|
2 |
+
Based on the DomainEval benchmark, we evaluate code generation ability of different LLMs across multiple domains.
|
3 |
+
|
4 |
+
More details about how to evaluate the LLM are available in the [DomainEval GitHub repository](https://github.com/domaineval/DomainEval).
|
5 |
+
|
6 |
+
For a complete description of DomainEval benchmark and related experimental analysis, please refer to the paper: [DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation](https://arxiv.org/abs/2408.13204). [![](https://img.shields.io/badge/arXiv-2408.13204-b31b1b.svg)](https://arxiv.org/abs/2408.13204)
|
7 |
+
|
8 |
+
**_Latest News_** 🔥
|
9 |
+
- [24/08/26] We release our DomainEval benchmark, leaderboard and paper.
|
10 |
+
"""
|
11 |
+
|
12 |
+
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
|
13 |
+
|
14 |
+
CITATION_BUTTON_TEXT = r"""@misc{zhu2024domainevalautoconstructedbenchmarkmultidomain,
|
15 |
+
title={DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation},
|
16 |
+
author={Qiming Zhu and Jialun Cao and Yaojie Lu and Hongyu Lin and Xianpei Han and Le Sun and Shing-Chi Cheung},
|
17 |
+
year={2024},
|
18 |
+
eprint={2408.13204},
|
19 |
+
archivePrefix={arXiv},
|
20 |
+
primaryClass={cs.AI},
|
21 |
+
url={https://arxiv.org/abs/2408.13204},
|
22 |
+
}
|
23 |
+
"""
|
24 |
+
|
25 |
+
ACKNOWLEDGEMENT_TEXT = """
|
26 |
+
Inspired from the [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
|
27 |
+
"""
|
28 |
+
|
29 |
+
|
30 |
+
NOTES_TEXT = """
|
31 |
+
**Notes:**
|
32 |
+
- Evaluate using pass@k as the evaluation metric.
|
33 |
+
- `Mean` denotes the macro average results of pass@k across 6 different domains.
|
34 |
+
- `Std` denotes the standard deviation of pass@k across 6 different domains.
|
35 |
+
- you can choose differt pass@k in `⏬ Pass@k`.
|
36 |
+
- `⏬ Domains` can choose domains you want to show in the leaderboard.
|
37 |
+
"""
|
utils.py
ADDED
@@ -0,0 +1,27 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from dataclasses import dataclass
|
2 |
+
|
3 |
+
@dataclass
|
4 |
+
class ColumnContent:
|
5 |
+
name: str
|
6 |
+
type: str
|
7 |
+
displayed_by_default: bool
|
8 |
+
hidden: bool = False
|
9 |
+
|
10 |
+
|
11 |
+
def fields(raw_class):
|
12 |
+
return [
|
13 |
+
v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"
|
14 |
+
]
|
15 |
+
|
16 |
+
|
17 |
+
@dataclass(frozen=True)
|
18 |
+
class AutoEvalColumn: # Auto evals column
|
19 |
+
model = ColumnContent("Model", "markdown", True)
|
20 |
+
average = ColumnContent("Mean", "number", True)
|
21 |
+
std = ColumnContent("Std", "number", True)
|
22 |
+
l_0 = ColumnContent("Computation", "number", True)
|
23 |
+
l_1 = ColumnContent("Network", "number", True)
|
24 |
+
l_2 = ColumnContent("Visualization", "number", True)
|
25 |
+
l_3 = ColumnContent("Basic", "number", True)
|
26 |
+
l_4 = ColumnContent("System", "number", True)
|
27 |
+
l_5 = ColumnContent("Cryptography", "number", True)
|