From 6e03ca74430a193985184f28bf2ca2de7f8570ed Mon Sep 17 00:00:00 2001 From: cabreraalex Date: Thu, 4 Jan 2024 18:05:59 +0000 Subject: [PATCH] deploy: 4c15aaab9afa7eb01816d39be3a9220119049fe8 --- 404.html | 6 +- about/index.html | 6 +- assets/css/styles.c971543d.css | 1 + assets/css/styles.f123def6.css | 1 - ...768db.2b6edd77.js => 24c768db.38f3c2e8.js} | 2 +- assets/js/5c1fb99f.1d165b84.js | 1 + assets/js/5c1fb99f.38a1515b.js | 1 - assets/js/842e05e7.c92ed74b.js | 1 - assets/js/935f2afb.33c47b9f.js | 1 + assets/js/935f2afb.e857cdaa.js | 1 - assets/js/f8409a7e.5cf27439.js | 1 + assets/js/f8409a7e.811632f3.js | 1 - assets/js/main.0c16feeb.js | 2 + ...CENSE.txt => main.0c16feeb.js.LICENSE.txt} | 0 assets/js/main.5da3e305.js | 2 - assets/js/runtime~main.8dc9a542.js | 1 + assets/js/runtime~main.b4ecad3e.js | 1 - blog/archive/index.html | 6 +- blog/index.html | 6 +- blog/tags/index.html | 6 +- blog/tags/introduction/index.html | 6 +- blog/welcome-post/index.html | 6 +- docs/intro/index.html 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mode 100644 index 07fd1eb..0000000 --- a/assets/css/styles.f123def6.css +++ /dev/null @@ -1 +0,0 @@ -@import url(https://fonts.googleapis.com/css2?family=Hammersmith+One&family=Source+Sans+Pro:wght@400;700&display=swap);@import url(https://fonts.googleapis.com/css2?family=Rubik:wght@500;600&display=swap);.col,.container{padding:0 var(--ifm-spacing-horizontal);width:100%}.markdown>h2,.markdown>h3,.markdown>h4,.markdown>h5,.markdown>h6{margin-bottom:calc(var(--ifm-heading-vertical-rhythm-bottom)*var(--ifm-leading))}.markdown li,body{word-wrap:break-word}body,ol ol,ol ul,ul ol,ul ul{margin:0}pre,table{overflow:auto}blockquote,pre{margin:0 0 var(--ifm-spacing-vertical)}.breadcrumbs__link,.button{transition-timing-function:var(--ifm-transition-timing-default)}.button,code{vertical-align:middle}.button--outline.button--active,.button--outline:active,.button--outline:hover,:root{--ifm-button-color:var(--ifm-font-color-base-inverse)}.menu__link:hover,a{transition:color 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.bar{background:var(--docusaurus-progress-bar-color);height:2px;left:0;position:fixed;top:0;width:100%;z-index:1031}#nprogress .peg{box-shadow:0 0 10px var(--docusaurus-progress-bar-color),0 0 5px var(--docusaurus-progress-bar-color);height:100%;opacity:1;position:absolute;right:0;transform:rotate(3deg) translateY(-4px);width:100px}[data-theme=dark]{--ifm-color-primary:#efd6ff;--ifm-color-primary-dark:#dda7ff;--ifm-color-primary-darker:#d490ff;--ifm-color-primary-darkest:#b849ff;--ifm-color-primary-light:#fff;--ifm-color-primary-lighter:#fff;--ifm-color-primary-lightest:#fff;--ifm-navbar-background-color:#4a136c}.header-github-link:hover{opacity:.6}[data-theme=dark] .header-github-link:before{background:url("data:image/svg+xml;charset=utf-8,%3Csvg fill='%23fff' viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 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5px;object-fit:contain;width:50%}.tabs-container{max-width:66%;min-width:66%}.button:hover{color:var(--p1)}.sign-in{background:var(--p1);border:none;border-radius:5px;cursor:pointer;font-size:14px;font-weight:600;padding:7px 20px}.sign-in:hover{background:var(--ifm-color-primary-lightest)}body:not(.navigation-with-keyboard) :not(input):focus{outline:0}#__docusaurus-base-url-issue-banner-container,.docSidebarContainer_YfHR,.sidebarLogo_isFc,.themedComponent_mlkZ,[data-theme=dark] .lightToggleIcon_pyhR,[data-theme=light] .darkToggleIcon_wfgR,html[data-announcement-bar-initially-dismissed=true] .announcementBar_mb4j{display:none}.skipToContent_fXgn{background-color:var(--ifm-background-surface-color);color:var(--ifm-color-emphasis-900);left:100%;padding:calc(var(--ifm-global-spacing)/2) var(--ifm-global-spacing);position:fixed;top:1rem;z-index:calc(var(--ifm-z-index-fixed) + 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\ No newline at end of file diff --git a/assets/js/842e05e7.c92ed74b.js b/assets/js/842e05e7.c92ed74b.js deleted file mode 100644 index 2517e8f..0000000 --- a/assets/js/842e05e7.c92ed74b.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[954],{8338:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>s,default:()=>d,frontMatter:()=>i,metadata:()=>r,toc:()=>c});var o=n(5893),a=n(1151);const i={sidebar_position:2},s="Using Zeno",r={id:"using-zeno",title:"Using Zeno",description:"We recommend you watch the following video for a quick overview of Zeno's features.",source:"@site/docs/using-zeno.mdx",sourceDirName:".",slug:"/using-zeno",permalink:"/docs/using-zeno",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:2,frontMatter:{sidebar_position:2},sidebar:"tutorialSidebar",previous:{title:"Getting Started",permalink:"/docs/intro"},next:{title:"Python Client API",permalink:"/docs/python-client"}},l={},c=[{value:"Data and Model Output Exploration",id:"data-and-model-output-exploration",level:2},{value:"Chart Building",id:"chart-building",level:2},{value:"Qualitative Comparison",id:"qualitative-comparison",level:2}];function h(e){const t={a:"a",code:"code",h1:"h1",h2:"h2",hr:"hr",img:"img",li:"li",p:"p",strong:"strong",ul:"ul",...(0,a.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsx)(t.h1,{id:"using-zeno",children:"Using Zeno"}),"\n",(0,o.jsx)(t.p,{children:"We recommend you watch the following video for a quick overview of Zeno's features."}),"\n",(0,o.jsx)("iframe",{width:"560",height:"315",src:"https://www.youtube.com/embed/28eLbSSUZOA?si=8VVSzaV-LIv6CJ4H",title:"YouTube video player",frameborder:"0",allow:"accelerometer; 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If you want to try to add new features, you can implement them and add them to the config.py file in the examples that you're using, some examples below:"}),"\n",(0,o.jsxs)(t.ul,{children:["\n",(0,o.jsx)(t.li,{children:(0,o.jsx)(t.a,{href:"https://github.com/zeno-ml/zeno-build/blob/6ab05c7083ef556aa12a626c0771814c74f030aa/examples/chatbot/config.py#L187-L201",children:"Features for Chatbots"})}),"\n",(0,o.jsx)(t.li,{children:(0,o.jsx)(t.a,{href:"https://github.com/zeno-ml/zeno-build/blob/6ab05c7083ef556aa12a626c0771814c74f030aa/examples/analysis_gpt_mt/config.py#L117-L134",children:"Features for Translation"})}),"\n"]}),"\n",(0,o.jsx)(t.h2,{id:"chart-building",children:"Chart Building"}),"\n",(0,o.jsx)(t.p,{children:'Once you have some models to compare and some slices to compare them on, you can start building interactive charts to summarize model performance. 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For example, you can create a chart comparing model performance across instances with short, medium, or long ground truth labels:"}),"\n",(0,o.jsx)(t.p,{children:(0,o.jsx)(t.img,{src:"https://github.com/zeno-ml/zeno-build/raw/main/docs/images/zeno-walkthrough-report-slices.png",alt:"Chart example"})}),"\n",(0,o.jsx)(t.h2,{id:"qualitative-comparison",children:"Qualitative Comparison"}),"\n",(0,o.jsx)(t.p,{children:"One final handy feature of Zeno is the ability to compare the outputs of two models on the same examples. You can do this by clicking on the qualitative comparison button:"}),"\n",(0,o.jsx)(t.p,{children:(0,o.jsx)(t.img,{src:"https://github.com/zeno-ml/zeno-build/raw/main/docs/images/zeno-walkthrough-qualitative-button.png",alt:"Qualitative comparison"})}),"\n",(0,o.jsxs)(t.p,{children:["On the page, you can then select the two models you want to compare side-by-side, and select the metric you'd like to compare them by. Here we choose ",(0,o.jsx)(t.code,{children:"gpt-3.5-turbo"})," and ",(0,o.jsx)(t.code,{children:"vicuna"})," and compare them according to the ",(0,o.jsx)(t.code,{children:"bert_score"})," metric."]}),"\n",(0,o.jsx)(t.p,{children:(0,o.jsx)(t.img,{src:"https://github.com/zeno-ml/zeno-build/raw/main/docs/images/zeno-walkthrough-qualitative-standard.png",alt:"Comparison"})}),"\n",(0,o.jsx)(t.p,{children:'You can also sort the outputs by the difference between the scores between the two systems by clicking on the header of the "difference" column. This allows you to find examples where one of the two systems produces much better outputs than the other, such as the one below where one model suddenly went off track and produced an incomprehensible output.'}),"\n",(0,o.jsx)(t.p,{children:(0,o.jsx)(t.img,{src:"https://github.com/zeno-ml/zeno-build/raw/main/docs/images/zeno-walkthrough-qualitative-sorted.png",alt:"Example find"})})]})}function d(e={}){const{wrapper:t}={...(0,a.a)(),...e.components};return t?(0,o.jsx)(t,{...e,children:(0,o.jsx)(h,{...e})}):h(e)}}}]); \ No newline at end of file diff --git a/assets/js/935f2afb.33c47b9f.js b/assets/js/935f2afb.33c47b9f.js new file mode 100644 index 0000000..a63632d --- /dev/null +++ b/assets/js/935f2afb.33c47b9f.js @@ -0,0 +1 @@ +"use 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at end of file diff --git a/assets/js/f8409a7e.5cf27439.js b/assets/js/f8409a7e.5cf27439.js new file mode 100644 index 0000000..31b168d --- /dev/null +++ b/assets/js/f8409a7e.5cf27439.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[206],{9325:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>c,contentTitle:()=>s,default:()=>h,frontMatter:()=>o,metadata:()=>r,toc:()=>l});var a=t(5893),i=t(1151);t(9286);const o={sidebar_position:1},s="Getting Started",r={id:"intro",title:"Getting Started",description:"Zeno is an interactive AI evaluation platform for exploring, debugging, and sharing how your AI systems perform.",source:"@site/docs/intro.mdx",sourceDirName:".",slug:"/intro",permalink:"/docs/intro",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:1,frontMatter:{sidebar_position:1},sidebar:"tutorialSidebar",next:{title:"Python Client API",permalink:"/docs/python-client"}},c={},l=[{value:"Creating a Project",id:"creating-a-project",level:2},{value:"Creating a Zeno Account",id:"creating-a-zeno-account",level:3},{value:"Using the Python API",id:"using-the-python-api",level:3},{value:"Setting up Your Data",id:"setting-up-your-data",level:3},{value:"Initializing a Project",id:"initializing-a-project",level:3},{value:"Uploading AI System Outputs",id:"uploading-ai-system-outputs",level:3},{value:"Quickstart with Zeno Build",id:"quickstart-with-zeno-build",level:2}];function d(e){const n={a:"a",admonition:"admonition",code:"code",em:"em",h1:"h1",h2:"h2",h3:"h3",li:"li",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,i.a)(),...e.components},{Details:t}=n;return t||function(e,n){throw new Error("Expected "+(n?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}("Details",!0),(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(n.h1,{id:"getting-started",children:"Getting Started"}),"\n",(0,a.jsxs)(n.p,{children:["Zeno is an ",(0,a.jsx)(n.strong,{children:"interactive AI evaluation platform"})," for exploring, debugging, and sharing how your AI systems perform.\nEvaluate any task and data type with Zeno's ",(0,a.jsx)(n.a,{href:"/docs/views/",children:"modular views"})," which support everything from chatbot conversations to object detection and audio transcription."]}),"\n",(0,a.jsx)(n.h2,{id:"creating-a-project",children:"Creating a Project"}),"\n",(0,a.jsxs)(n.p,{children:["The core of Zeno are ",(0,a.jsx)(n.strong,{children:"projects"}),", which consist of a base evaluation dataset and any number of AI system outputs.\nWe'll walk through creating your first project step by step, which will result in this final project example:"]}),"\n",(0,a.jsxs)(t,{children:[(0,a.jsx)("summary",{children:"Complete Example"}),(0,a.jsx)("div",{children:(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'from zeno_client import ZenoClient, ZenoMetric\nimport pandas as pd\n\nclient = ZenoClient("YOUR API KEY HERE")\n\ndf = pd.DataFrame(\n {\n "id": [1, 2, 3],\n "text": [\n "I love this movie!",\n "I hate this movie!",\n "This movie is ok.",\n ],\n "label": ["positive", "negative", "neutral"],\n }\n)\n\n# Add any additional columns you want to do analysis across.\ndf["input length"] = df["text"].str.len()\n\n# Create a project.\nproject = client.create_project(\nname="Sentiment Classification",\nview="text-classification",\nmetrics=[\n ZenoMetric(name="accuracy", type="mean", columns=["correct"]),\n ]\n)\n\n# Upload the data.\nproject.upload_dataset(df, id_column="id", data_column=\'text\', label_column="label")\n\n# Create a system DataFrame.\ndf_system = pd.DataFrame(\n {\n "output": ["positive", "negative", "negative"],\n }\n)\n\n# Create an id column to match the base dataset.\ndf_system["id"] = df_system.index\n\n# Measure accuracy for each instance, which is averaged by the ZenoMetric above.\ndf_system["correct"] = (df_system["output"] == df["label"]).astype(int)\n\nproj.upload_system(df_system, name="System A", id_column="id", output_column="output")\n'})})})]}),"\n",(0,a.jsx)(n.h3,{id:"creating-a-zeno-account",children:"Creating a Zeno Account"}),"\n",(0,a.jsxs)(n.p,{children:["If you don't have a Zeno account already, create one on ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/signup",children:"Zeno Hub"}),".\nAfter logging in to Zeno Hub, generate your API key by clicking on your profile at the top right to navigate to your ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/account",children:"account page"}),"."]}),"\n",(0,a.jsx)(n.h3,{id:"using-the-python-api",children:"Using the Python API"}),"\n",(0,a.jsxs)(n.p,{children:["Zeno projects are created and managed using the ",(0,a.jsx)(n.code,{children:"zeno-client"})," Python library:"]}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-bash",children:"pip install zeno-client\n"})}),"\n",(0,a.jsx)(n.p,{children:"We can now initialize a client with the API key which we will use to create projects and upload data."}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'from zeno_client import ZenoClient, ZenoMetric\nimport pandas as pd\n\n# Initialize a client with our API key.\nclient = ZenoClient("YOUR API KEY HERE")\n'})}),"\n",(0,a.jsx)(n.h3,{id:"setting-up-your-data",children:"Setting up Your Data"}),"\n",(0,a.jsxs)(n.p,{children:["Zeno projects take data and model outputs as ",(0,a.jsx)(n.strong,{children:"Pandas DataFrames"}),".\nIn this example, we will upload a toy example of sentiment classification with 3 instances and ground truth labels:"]}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'...\n\n# Put all data in a Pandas DataFrame\ndf = pd.DataFrame(\n {\n "id": [1, 2, 3],\n "text": [\n "I love this movie!",\n "I hate this movie!",\n "This movie is ok.",\n ],\n "label": ["positive", "negative", "neutral"],\n }\n)\n\n# Add any additional columns you want to do analysis across.\ndf["input length"] = df["text"].str.len()\n'})}),"\n",(0,a.jsx)(n.admonition,{type:"tip",children:(0,a.jsxs)(n.p,{children:["Every DataFrame in Zeno requires a unique ",(0,a.jsx)(n.code,{children:"id_column"})," that identifies each instance. This is used to match the base dataset with system outputs."]})}),"\n",(0,a.jsx)(n.h3,{id:"initializing-a-project",children:"Initializing a Project"}),"\n",(0,a.jsxs)(n.p,{children:["We can now initialize a ",(0,a.jsx)(n.strong,{children:"project"})," using the client and upload our base dataset:"]}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'...\n\nproject = client.create_project(\n name="Sentiment Classification",\n view="text-classification",\n metrics=[\n ZenoMetric(name="accuracy", type="mean", columns=["correct"]),\n ]\n)\n\nproject.upload_dataset(df, id_column="id", data_column=\'text\', label_column="label")\n'})}),"\n",(0,a.jsxs)(n.p,{children:["We named our project ",(0,a.jsx)(n.em,{children:"Sentiment Classification"})," and specified to use the ",(0,a.jsx)(n.em,{children:"text_classification"})," view.\nWe also added an ",(0,a.jsx)(n.em,{children:"accuracy"})," metric which takes the mean of the ",(0,a.jsx)(n.code,{children:"correct"})," column that will be present in the system outputs we upload later."]}),"\n",(0,a.jsx)(n.admonition,{type:"tip",children:(0,a.jsxs)(n.p,{children:["Learn more about Zeno's ",(0,a.jsx)(n.code,{children:"view"})," specification in the ",(0,a.jsx)(n.a,{href:"/docs/views",children:"instance view docs"}),".\nYou can find the default supported views ",(0,a.jsx)(n.a,{href:"/docs/views/existing",children:"here"}),"."]})}),"\n",(0,a.jsx)(n.h3,{id:"uploading-ai-system-outputs",children:"Uploading AI System Outputs"}),"\n",(0,a.jsxs)(n.p,{children:["Lastly, we can upload our AI system outputs to the project.\nWe add an ",(0,a.jsx)(n.code,{children:"id"})," column to match the base dataset and a ",(0,a.jsx)(n.code,{children:"correct"})," column that measures accuracy for each instance."]}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'...\n\ndf_system = pd.DataFrame(\n {\n "output": ["positive", "negative", "negative"],\n }\n)\n\n# Create an id column to match the base dataset.\ndf_system["id"] = df_system.index\n\n# Measure accuracy for each instance, which is averaged by the ZenoMetric above.\ndf_system["correct"] = (df_system["output"] == df["label"]).astype(int)\n\nproj.upload_system(df_system, name="System A", id_column="id", output_column="output")\n'})}),"\n",(0,a.jsx)(n.p,{children:"You can now navigate to the project URL in Zeno Hub to see the uploaded data and metrics and start exploring your AI system's performance!"}),"\n",(0,a.jsx)(n.h2,{id:"quickstart-with-zeno-build",children:"Quickstart with Zeno Build"}),"\n",(0,a.jsxs)(n.p,{children:[(0,a.jsx)(n.a,{href:"https://github.com/zeno-ml/zeno-build",children:"Zeno Build"})," is a collection of example projects for common AI and ML tasks. Check out some common Zeno Build notebooks:"]}),"\n",(0,a.jsxs)(n.ul,{children:["\n",(0,a.jsx)(n.li,{children:(0,a.jsx)(n.a,{href:"https://github.com/zeno-ml/zeno-build/tree/main/examples/eleuther_harness",children:"EleutherAI LM Evaluation Harness"})}),"\n",(0,a.jsx)(n.li,{children:(0,a.jsx)(n.a,{href:"https://github.com/zeno-ml/zeno-build/tree/main/examples/open_llm_leaderboard",children:"\ud83e\udd17 OpenLLM Leaderboard"})}),"\n",(0,a.jsx)(n.li,{children:(0,a.jsx)(n.a,{href:"https://github.com/zeno-ml/zeno-build/tree/main/examples/transcription",children:"Audio Transcription Bias"})}),"\n"]})]})}function h(e={}){const{wrapper:n}={...(0,i.a)(),...e.components};return n?(0,a.jsx)(n,{...e,children:(0,a.jsx)(d,{...e})}):d(e)}}}]); \ No newline at end of file diff --git a/assets/js/f8409a7e.811632f3.js b/assets/js/f8409a7e.811632f3.js deleted file mode 100644 index 9b8d6c2..0000000 --- a/assets/js/f8409a7e.811632f3.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[206],{9325:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>c,contentTitle:()=>s,default:()=>h,frontMatter:()=>i,metadata:()=>r,toc:()=>d});var a=t(5893),o=t(1151);t(9286);const i={sidebar_position:1},s="Getting Started",r={id:"intro",title:"Getting Started",description:"Zeno is an interactive AI evaluation platform for exploring, discovering and reporting the performance of your models.",source:"@site/docs/intro.mdx",sourceDirName:".",slug:"/intro",permalink:"/docs/intro",draft:!1,unlisted:!1,tags:[],version:"current",sidebarPosition:1,frontMatter:{sidebar_position:1},sidebar:"tutorialSidebar",next:{title:"Using Zeno",permalink:"/docs/using-zeno"}},c={},d=[{value:"Creating a Project",id:"creating-a-project",level:2},{value:"Complete Example",id:"complete-example",level:4},{value:"Quickstart with Zeno Build",id:"quickstart-with-zeno-build",level:2}];function l(e){const n={a:"a",admonition:"admonition",code:"code",h1:"h1",h2:"h2",h4:"h4",li:"li",p:"p",pre:"pre",strong:"strong",ul:"ul",...(0,o.a)(),...e.components};return(0,a.jsxs)(a.Fragment,{children:[(0,a.jsx)(n.h1,{id:"getting-started",children:"Getting Started"}),"\n",(0,a.jsxs)(n.p,{children:["Zeno is an ",(0,a.jsx)(n.strong,{children:"interactive AI evaluation platform"})," for exploring, discovering and reporting the performance of your models.\nUse it for any task and data type with Zeno's ",(0,a.jsx)(n.a,{href:"/docs/views/",children:"modular views"})," for everything from object detection to audio transcription and chatbot conversations.\nZeno helps you move beyond aggregate metrics and spot-checking model outputs to develop a deep and quantitative understanding of how your model behaves."]}),"\n",(0,a.jsxs)(n.p,{children:["To get an idea of Zeno's features, you can explore public projects and reports in ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com",children:"Zeno Hub"}),".\nFor example, take a look at a ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/report/cabreraalex/GPT%20MT%20Benchmark%20Report",children:"report evaluating how well LLMs like GPT-4 do on language translation"}),", along with the ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/project/cabreraalex/GPT%20MT%20Benchmark/explore",children:"underlying data used to create the report."}),"."]}),"\n",(0,a.jsxs)(n.admonition,{type:"tip",children:[(0,a.jsxs)(n.p,{children:["Head over to ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com",children:"Zeno Hub"})," to explore public Zeno projects and reports."]}),(0,a.jsxs)(n.p,{children:["Check out the ",(0,a.jsx)(n.a,{href:"/docs/using-zeno/",children:"Using Zeno"})," docs for more information on how to use the Zeno interface."]})]}),"\n",(0,a.jsx)(n.h2,{id:"creating-a-project",children:"Creating a Project"}),"\n",(0,a.jsxs)(n.p,{children:["To create your own projects and reports, first create an account on ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/signup",children:"Zeno Hub"}),".\nAfter logging in to Zeno Hub, generate your API key by clicking on your profile at the top right to navigate to your ",(0,a.jsx)(n.a,{href:"https://hub.zenoml.com/account",children:"account page"}),"."]}),"\n",(0,a.jsxs)(n.p,{children:["Next, install the ",(0,a.jsx)(n.code,{children:"zeno-client"})," Python package, which is used to upload new datasets and AI system outputs:"]}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-bash",children:"pip install zeno-client\n"})}),"\n",(0,a.jsx)(n.p,{children:"We can now create a client with out API key, and use it to create a project and upload data. The client API works with Pandas DataFrames, so we can create a sample DataFrame looking at text sentiment classification:"}),"\n",(0,a.jsx)(n.pre,{children:(0,a.jsx)(n.code,{className:"language-python",children:'from zeno_client import ZenoClient\nimport pandas as pd\n\nclient = ZenoClient("YOUR API KEY HERE")\n\ndf = pd.DataFrame(\n {\n "text": [\n "I love this movie!",\n "I hate this movie!",\n "This movie is ok.",\n ],\n "label": ["positive", "negative", "neutral"],\n }\n)\n\n# Explicitly save the index as a column to upload.\ndf["id"] = df.index\n\n# Add any additional columns you want to do analysis across.\ndf["input length"] = df["text"].str.len()\n'})}),"\n",(0,a.jsxs)(n.p,{children:["Let's create a project for this task. ",(0,a.jsx)(n.strong,{children:"Projects"})," in Zeno are a base dataset and any number of AI system outputs, and are used to evaluate and compare model performance. 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