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About Us

Zeno is designed, built, and maintained by a team of interdisciplinary researchers at Carnegie Mellon University. We span research groups in Machine Learning, Human-Computer Interaction, and Language Technologies. We are passionate about empowering people to understand the complexities of AI systems in order to design the future of responsible AI.

Team

Alex Cabrera

PhD Candidate

Alex Bäuerle

Research Scientist

Erica Fu

Undergraduate Student

Ankitha Vasudev

Masters Student

Kathy Yu

Masters Student

Josh Zhou

Undergraduate Student

Donald Bertucci

Undergraduate Student

Ameet Talwalkar

Associate Professor

Graham Neubig

Associate Professor

Jason Hong

Professor

Adam Perer

Assistant Professor

Past Members

Tianqi Wu

Masters Student

Yi-Cheng Huang

Research Associate

Sponsors & Organizations

We are thankful to our sponsors and affiliated organizations. Zeno is part of the Mozilla Technology Fund and developed partly with the support of multiple NSF grants and fellowships.

Mozilla logoMozilla logoCMU logo

Contact

If you have any questions or issues with Zeno please send an email to hello@zenoml.com, ask in our Discord, or open an issue on GitHub

Cite

Please reference our CHI 2023 paper if you would like to cite Zeno:


@inproceedings{cabrera23zeno,
  author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
  title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
  year = {2023},
  isbn = {978-1-4503-9421-5/23/04},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3544548.3581268},
  doi = {10.1145/3544548.3581268},
  booktitle = {CHI Conference on Human Factors in Computing Systems},
  location = {Hamburg, Germany},
  series = {CHI '23}
}
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The client API works with Pandas DataFrames, so we can create a sample DataFrame looking at text sentiment classification:"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'from zeno_client import ZenoClient\n# highlight-next-line\nimport pandas as pd\n\nclient = ZenoClient("YOUR API KEY HERE")\n\n# highlight-start\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# highlight-end\n')),(0,o.kt)("p",null,"Let's create a project for this task. ",(0,o.kt)("strong",{parentName:"p"},"Projects")," in Zeno are a base dataset and any number of AI system outputs, and are used to evaluate and compare AI system performance. Here we create a project and upload our base dataset."),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'from zeno_client import ZenoClient, ZenoMetric\n\n...\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')),(0,o.kt)("p",null,'We named our project "Sentiment Classification" and specified that it is a text classification task.\nCheck out all supported data types and tasks ',(0,o.kt)("a",{parentName:"p",href:"https://zenoml.com/docs/views/"},"here"),".\nWe also added an initial accuracy metric which takes the mean of the ",(0,o.kt)("inlineCode",{parentName:"p"},"correct")," column, which will be present in the system outputs we upload later."),(0,o.kt)("p",null,"Next, we can upload some system outputs to evaluate. Here we'll upload some fake predictions from a model:"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'\n...\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')),(0,o.kt)("p",null,"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!"),(0,o.kt)("h4",{id:"complete-example"},"Complete Example"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'from zeno_client import ZenoClient, ZenoMetric\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\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\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\nproject.upload_system(df_system, name="System A", id_column="id", output_column="output")\n')),(0,o.kt)("h2",{id:"quickstart-with-zeno-build"},"Quickstart with Zeno Build"),(0,o.kt)("p",null,(0,o.kt)("a",{parentName:"p",href:"https://github.com/zeno-ml/zeno-build"},"Zeno Build")," is a Python library that makes it easy to set up Zeno projects for common AI and ML tasks."),(0,o.kt)("p",null,"Follow the ",(0,o.kt)("a",{parentName:"p",href:"https://github.com/zeno-ml/zeno-build/tree/main/docs/tutorial"},"Zeno Build Tutorial")," to learn the basic Zeno concepts on an example LLM task."))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/f8409a7e.eb5c48d4.js b/assets/js/f8409a7e.eb5c48d4.js new file mode 100644 index 0000000..53a9f1e --- /dev/null +++ b/assets/js/f8409a7e.eb5c48d4.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[206],{9568:(e,t,n)=>{n.r(t),n.d(t,{assets:()=>l,contentTitle:()=>r,default:()=>u,frontMatter:()=>i,metadata:()=>s,toc:()=>p});var a=n(7462),o=(n(7294),n(3905));n(614);const i={sidebar_position:1},r="Getting Started",s={unversionedId:"intro",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,tags:[],version:"current",sidebarPosition:1,frontMatter:{sidebar_position:1},sidebar:"tutorialSidebar",next:{title:"Using Zeno",permalink:"/docs/using-zeno"}},l={},p=[{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}],c={toc:p},d="wrapper";function u(e){let{components:t,...n}=e;return(0,o.kt)(d,(0,a.Z)({},c,n,{components:t,mdxType:"MDXLayout"}),(0,o.kt)("h1",{id:"getting-started"},"Getting Started"),(0,o.kt)("p",null,"Zeno is an ",(0,o.kt)("strong",{parentName:"p"},"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,o.kt)("a",{parentName:"p",href:"/docs/views/"},"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."),(0,o.kt)("p",null,"To get an idea of Zeno's features, you can explore public projects and reports in ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com"},"Zeno Hub"),".\nFor example, take a look at a ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com/report/cabreraalex/GPT%20MT%20Benchmark%20Report"},"report evaluating how well LLMs like GPT-4 do on language translation"),", along with the ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com/project/cabreraalex/GPT%20MT%20Benchmark/explore"},"underlying data used to create the report."),"."),(0,o.kt)("admonition",{type:"tip"},(0,o.kt)("p",{parentName:"admonition"},"Head over to ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com"},"Zeno Hub")," to explore public Zeno projects and reports.\nCheck out the ",(0,o.kt)("a",{parentName:"p",href:"/docs/using-zeno/"},"Using Zeno")," docs for more information on how to use the Zeno interface.")),(0,o.kt)("h2",{id:"creating-a-project"},"Creating a Project"),(0,o.kt)("p",null,"To create your own projects and reports, first create an account on ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com/signup"},"Zeno Hub"),".\nAfter logging in to Zeno Hub, generate your API key by clicking on your profile at the bottom left to navigate to your ",(0,o.kt)("a",{parentName:"p",href:"https://hub.zenoml.com/account"},"account page"),"."),(0,o.kt)("p",null,"Next, install the ",(0,o.kt)("inlineCode",{parentName:"p"},"zeno-client")," Python package, which is used to upload new datasets and AI system outputs:"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-bash"},"pip install zeno-client\n")),(0,o.kt)("p",null,"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:"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'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')),(0,o.kt)("p",null,"Let's create a project for this task. ",(0,o.kt)("strong",{parentName:"p"},"Projects")," in Zeno are a base dataset and any number of AI system outputs, and are used to evaluate and compare model performance. Here we create a project and upload our base dataset."),(0,o.kt)("admonition",{type:"info"},(0,o.kt)("p",{parentName:"admonition"},"If you are working with tabular data you do not have to pass in a ",(0,o.kt)("inlineCode",{parentName:"p"},"view")," option to ",(0,o.kt)("inlineCode",{parentName:"p"},"create_project")," or a ",(0,o.kt)("inlineCode",{parentName:"p"},"data_column")," option to ",(0,o.kt)("inlineCode",{parentName:"p"},"upload_dataset"),".")),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'from zeno_client import ZenoClient, ZenoMetric\n\n...\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')),(0,o.kt)("p",null,'We named our project "Sentiment Classification" and specified that it is a text classification task.\nCheck out all supported data types and tasks ',(0,o.kt)("a",{parentName:"p",href:"https://zenoml.com/docs/views/"},"here"),".\nWe also added an initial accuracy metric which takes the mean of the ",(0,o.kt)("inlineCode",{parentName:"p"},"correct")," column, which will be present in the system outputs we upload later."),(0,o.kt)("p",null,"Next, we can upload some system outputs to evaluate. Here we'll upload some fake predictions from a model:"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'\n...\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')),(0,o.kt)("p",null,"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!"),(0,o.kt)("h4",{id:"complete-example"},"Complete Example"),(0,o.kt)("pre",null,(0,o.kt)("code",{parentName:"pre",className:"language-python"},'from zeno_client import ZenoClient, ZenoMetric\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\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\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\nproject.upload_system(df_system, name="System A", id_column="id", output_column="output")\n')),(0,o.kt)("h2",{id:"quickstart-with-zeno-build"},"Quickstart with Zeno Build"),(0,o.kt)("p",null,(0,o.kt)("a",{parentName:"p",href:"https://github.com/zeno-ml/zeno-build"},"Zeno Build")," is a Python library that makes it easy to set up Zeno projects for common AI and ML tasks."),(0,o.kt)("p",null,"Follow the ",(0,o.kt)("a",{parentName:"p",href:"https://github.com/zeno-ml/zeno-build/tree/main/docs/tutorial"},"Zeno Build Tutorial")," to learn the basic Zeno concepts on an example LLM task."))}u.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/main.6e6e9e96.js b/assets/js/main.440c8cbc.js similarity index 99% rename from assets/js/main.6e6e9e96.js rename to assets/js/main.440c8cbc.js index 9f6df4d..eee8c0c 100644 --- a/assets/js/main.6e6e9e96.js +++ b/assets/js/main.440c8cbc.js @@ -1,2 +1,2 @@ -/*! 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