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add nlp model icon (#5663)
* add nlp model icon * add nlp model icon
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Diff for: modelcenter/ERNIE-3.0/info.yaml

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@@ -3,7 +3,7 @@ Model_Info:
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name: "ERNIE 3.0"
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description: "ERNIE 3.0 轻量级模型"
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description_en: "ERNIE Tiny"
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icon: "@后续UE统一设计之后,会存到bos上某个位置"
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icon: "https://user-images.githubusercontent.com/11793384/203492126-501d2920-8f49-4d0b-a7f1-a6cecd003f7e.png"
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from_repo: "PaddleNLP"
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Task:
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- tag_en: "Natural Language Processing"
@@ -30,4 +30,4 @@ Paper:
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- title: "ERNIE-Tiny: A Progressive Distillation Framework for Pretrained Transformer Compression"
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url: "https://arxiv.org/abs/2106.02241"
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IfTraining: 0
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IfOnlineDemo: 1
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IfOnlineDemo: 1

Diff for: modelcenter/ERNIE-Layout/info.yaml

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@@ -4,7 +4,7 @@ Model_Info:
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description: 文心跨模态预训练大模型
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description_en: ERNIE transmodel pre training large model
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update_time:
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icon: url
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icon: "https://user-images.githubusercontent.com/11793384/203492273-64f38a22-a347-464b-9d87-6583ce5c3121.png"
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from_repo: PaddleNLP
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Task:
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- tag: 文心大模型

Diff for: modelcenter/ERNIE-M/info.yaml

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@@ -3,15 +3,15 @@ Model_Info:
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name: "ERNIE-M"
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description: "多语言的语言模型"
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description_en: "A cross-lingual model"
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icon: "@后续UE统一设计之后,会存到bos上某个位置"
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icon: "https://user-images.githubusercontent.com/11793384/203492376-77d159e8-d687-44b5-b164-a436eef91256.png"
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from_repo: "PaddleNLP"
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Task:
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- tag_en: "Natural Language Processing"
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tag: "自然语言处理"
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sub_tag_en: "Pretrained Model"
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sub_tag: "预训练模型"
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Example: ""
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Example:
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Datasets: ""
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Publisher: "Baidu"
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License: "apache.2.0"
@@ -20,4 +20,4 @@ Paper:
2020
\ Semantics with Monolingual Corpora"
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url: "https://arxiv.org/pdf/2012.15674.pdf"
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IfTraining: 0
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IfOnlineDemo: 0
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IfOnlineDemo: 0

Diff for: modelcenter/ERNIE-UIE/info.yaml

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@@ -3,7 +3,7 @@ Model_Info:
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name: "ERNIE-UIE"
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description: "信息抽取模型"
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description_en: "Information Extraction Model"
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icon: "@后续UE统一设计之后,会存到bos上某个位置"
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icon: "https://user-images.githubusercontent.com/11793384/203492521-8d09d089-5576-41d3-8bc7-eec7a5385c35.png"
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from_repo: "PaddleNLP"
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Task:
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- tag_en: "Natural Language Processing"
@@ -42,4 +42,4 @@ Paper:
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- title: "Unified Structure Generation for Universal Information Extraction"
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url: "https://arxiv.org/pdf/2203.12277.pdf"
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IfTraining: 0
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IfOnlineDemo: 1
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IfOnlineDemo: 1

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