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| import gradio as gr | |
| import pandas as pd | |
| from constants import * | |
| # ... 其他导入 ... | |
| # 定义自定义 CSS | |
| custom_css = """ | |
| h1 { /* 根据需要选择正确的标题标签 */ | |
| background-color: blue; /* 蓝色背景 */ | |
| color: white; /* 白色文字 */ | |
| padding: 10px; /* 内边距 */ | |
| text-align: center; /* 文本居中 */ | |
| } | |
| h2 { /* 根据需要选择正确的标题标签 */ | |
| color: black; /* 黑色文字 */ | |
| padding: 10px; /* 内边距 */ | |
| text-align: center; /* 文本居中 */ | |
| } | |
| """ | |
| def get_preview_data(): | |
| df = pd.read_json(DATA_DIR) | |
| df=df.head(4) | |
| return df | |
| def get_result_data(): | |
| df={ | |
| "DataSet": ["WikiData_recent", "WikiData_recent", "WikiData_recent", "WikiData_recent", | |
| "ZsRE", "ZsRE", "ZsRE", "ZsRE", | |
| "WikiBio", "WikiBio", "WikiBio", | |
| "WikiData_counterfact", "WikiData_counterfact", "WikiData_counterfact", "WikiData_counterfact", | |
| "ConvSent", "ConvSent", "ConvSent", | |
| "Sanitation", "Sanitation", "Sanitation"], | |
| "Metric": ["Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", | |
| "Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", | |
| "Edit Succ. ↑", "Locality ↑", "Fluency ↑", | |
| "Edit Succ. ↑", "Portability ↑", "Locality ↑", "Fluency ↑", | |
| "Edit Succ. ↑", "Locality ↓", "Fluency ↑", | |
| "Edit Succ. ↑", "Locality ↑", "Fluency ↑"], | |
| "SERAC": [98.68, 63.52, 100.00, 553.19, | |
| 99.67, 56.48, 30.23, 410.89, | |
| 99.69, 69.79, 606.95, | |
| 99.99, 76.07, 98.96, 549.91, | |
| 62.75, 0.26, 458.21, | |
| 0.00, 100.00, 416.29], | |
| "ICE": [60.74, 36.93, 33.34, 531.01, | |
| 66.01, 63.94, 23.14, 541.14, | |
| 95.53, 47.90, 632.92, | |
| 69.83, 45.32, 32.38, 547.22, | |
| 52.78, 49.73, 621.45, | |
| 72.50, 56.58, 794.15], | |
| "AdaLoRA": [65.61, 47.22, 55.78, 537.51, | |
| 69.86, 52.95, 72.21, 532.82, | |
| 97.02, 57.87, 615.86, | |
| 72.14, 55.17, 66.78, 553.85, | |
| 44.89, 0.18, 606.42, | |
| 2.50, 65.50, 330.44], | |
| "MEND": [76.88, 50.11, 92.87, 586.34, | |
| 96.74, 60.41, 92.79, 524.33, | |
| 93.66, 69.51, 609.39, | |
| 78.82, 57.53, 94.16, 588.94, | |
| 50.76, 3.42, 379.43, | |
| 0.00, 5.29, 407.18], | |
| "ROME": [85.08, 37.45, 66.2, 574.28, | |
| 96.57, 52.20, 27.14, 570.47, | |
| 95.05, 46.96, 617.25, | |
| 83.21, 38.69, 65.4, 578.84, | |
| 45.79, 0.00, 606.32, | |
| 85.00, 50.31, 465.12], | |
| "MEMIT": [85.32, 37.94, 64.78, 566.66, | |
| 83.07, 51.43, 25.46, 559.72, | |
| 94.29, 51.56, 616.65, | |
| 83.41, 40.09, 63.68, 568.58, | |
| 44.75, 0.00, 602.62, | |
| 48.75, 67.47, 466.10], | |
| "FT-L": [71.18, 48.71, 63.7, 549.35, | |
| 54.65, 45.02, 71.12, 474.18, | |
| 83.41, 40.09, 63.68, 568.58, | |
| 66.27, 60.14, 604.00, | |
| 51.12, 39.07, 62.51, | |
| 48.75, 67.47, 466.10] | |
| } | |
| df=pd.DataFrame(df) | |
| return df | |
| def get_struct_data(): | |
| df = { | |
| "Datasets":["train","test"], | |
| "ZsRE":["10,000","1230"], | |
| "Wikirecent":["5700","1266"], | |
| "Wikicounterfact":["1455","885"], | |
| "WikiBio":["592","1392"] | |
| } | |
| df=pd.DataFrame(df) | |
| return df | |
| block = gr.Blocks(css=custom_css) # 应用自定义 CSS | |
| with block: | |
| gr.Markdown(TITLE) | |
| gr.Markdown("## BACKGROUND") | |
| gr.Markdown( | |
| BACKGROUND | |
| ) | |
| gr.Image('./img/demo.gif') | |
| gr.Markdown("## DATA PREVIEW") | |
| gr.Markdown(LEADERBORAD_INTRODUCTION) | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 Data preview ", elem_id="ke-benchmark-tab-table", id=0): | |
| # 创建数据帧组件 | |
| ke_data_component = gr.components.Dataframe( | |
| value=get_preview_data(), | |
| headers=DATA_COLUMN_NAMES, | |
| type="pandas", | |
| ) | |
| with gr.TabItem("🏅 Data Struct ", elem_id="ke-struct-tab-table", id=1): | |
| # 创建数据帧组件 | |
| ke_data_component = gr.components.Dataframe( | |
| value=get_struct_data(), | |
| headers=STRUCT_COLUMN_NAMES, | |
| type="pandas", | |
| ) | |
| with gr.TabItem("📝 data schema", elem_id="about-benchmark-tab-table", id=4): | |
| gr.Markdown(DATA_SCHEMA, elem_classes="markdown-text") | |
| gr.Markdown("## EXPERIMENT RESULTS") | |
| gr.Markdown("We list the results of current knowledge editing methods on Llama2-7b-chat in Table") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 result", elem_id="ke-benchmark-tab-table", id=0): | |
| # 创建数据帧组件 | |
| ke_data_component = gr.components.Dataframe( | |
| value=get_result_data(), | |
| headers=RESULT_COLUMN_NAMES, | |
| type="pandas", | |
| ) | |
| # About tab | |
| with gr.TabItem("📝 About", elem_id="about-benchmark-tab-table", id=4): | |
| gr.Markdown("Results of existing knowledge edit methods on the constructed benchmark. The symbol indicates that higher numbers correspond to better performance, while ↓ denotes the opposite, with lower numbers indicating better performance. For WikiBio and Convsent, we do not test the portability as they are about specific topics. ", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Accordion("Citation", open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| elem_id="citation-button", | |
| ) | |
| block.launch() | |