toxicity
seldon-core
toxicity | seldon-core | |
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11 | 14 | |
166 | 4,220 | |
0.0% | 1.2% | |
0.0 | 7.6 | |
almost 2 years ago | 3 days ago | |
HTML | ||
MIT License | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
toxicity
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Perhaps It Is a Bad Thing That the Leading AI Companies Cannot Control Their AIs
I'm a PM at a human data company (https://www.surgehq.ai) that helps the large language model companies ensure their models are safe (we're the “clever prompt engineers” who helped Redwood assess their model performance).
We actually just published a blog today that includes our perspective on building “AI red teams” and best practices for AI alignment/safety: https://www.surgehq.ai/blog/ai-red-teams-for-adversarial-tra...
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30% of Google's Emotions Dataset Is Mislabeled
I'd love to chat. Want to reach out to the email in my profile? I'm the founder of a much higher-quality data startup (https://www.surgehq.ai), and previously built the human computation platforms at a couple FAANGs.
We work with a lot of the top AI/NLP companies and research labs, and do both the "typical" data labeling work (sentiment analysis, text categorization, etc), but also a lot more advanced stuff (e.g., training coding assistants, evaluating the new wave of large language models, adversarial labeling, etc -- so not just distinguishing cats and dogs, but rather making full use of the power of the human mind!).
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Building a No-Code Toxicity Classifier – By Talking to GitHub Copilot
> Rather than operating under a strict definition of toxicity, we asked our team to identify comments that they personally found toxic.
[0]: https://github.com/surge-ai/toxicity
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Ask HN: Who is hiring? (January 2022)
Love language? So do we, and our mission is to infuse AI with that same love. At Surge, we're building the human infrastructure to power NLP — from detecting hate speech, to parsing complex documents, to injecting human values into the next wave of language models. Our first product is a platform that helps ML teams create amazing, human-powered datasets to train AI in the richness of language. We're a team of former Google, Facebook, and Airbnb engineering leads, and we work with top companies at the forefront of machine learning. Our tech stack is Ruby on Rails, React, and Python. We’re rapidly growing, and we're looking for full-stack engineers to join the team and develop our product. To apply, please email [email protected] with a resume and 2-3 sentences describing your interest in Surge. We love personal projects and writings too!
More information: https://www.surgehq.ai/about#careers
A blog post explaining the problems we are working to solve: https://www.surgehq.ai/blog/the-ai-bottleneck-high-quality-h...
- The Toxicity Dataset – building the largest free dataset of online toxicity
- [Free] The Toxicity Dataset — building the world's largest free dataset of online toxicity [Github]
- The Toxicity Dataset — building the world's largest free dataset of online toxicity
- The Toxicity Dataset (1000 social media comments) — any ideas for interesting visualizations? [github]
- The Toxicity Dataset - free dataset of online toxicity (Github) - could be used for interesting portfolio projects
- The Toxicity Dataset — free dataset of online toxicity (Github)
seldon-core
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seldon-core VS MLDrop - a user suggested alternative
2 projects | 20 Feb 2023
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. Seldon Core is a production grade open source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
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[D] BentoML's Compatibility with Seldon;
I am using BentoML to build the docker container for a BERT model, and then deploy that using Seldon on GKE. The model's REST API endpoint works fine. at terms of compatibility with Seldon, the metrics are being scraped by Prometheus and visualized on Grafana. The only Seldon component that doesn't appear to be working is the request logging, which I have working for other applications that were deployed on Seldon. I am using the elastic stack from here. From my understanding, request logging should still be compatible and the ⠀only lost functionality should be Seldon's model metadata. Any insight on how to get the centralized request logging working? No errors were shown; it's just that the logs aren't being captured and sent to ElasticSearch. Anyone have any success using BentoML with Seldon and not losing any of Seldon's features?
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Building a Responsible AI Solution - Principles into Practice
While tools in the model experimentation space normally include diagnostic charts on a model's performance, there are also specialised solutions that help ensure that the deployed model continues to perform as they are expected to. This includes the likes of seldon-core, why-labs and fiddler.ai.
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
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Has anyone implemented Seldon?
Also note our github repo has a link to our slack where you can ask active users: https://github.com/SeldonIO/seldon-core
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
If you want to serve your model at scale, with a bunch of production features you should have a look at the open-source framework Seldon Core. It does what you're asking for plus a bunch of other cool stuff like routing, logging and monitoring.
- Seldon Core : Open-source platform for rapidly deploying machine learning models on Kubernetes
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Looking for open-source model serving framework with dashboard for test data quality
Seldon ticks most of those boxes if you already have some experience with kubernetes. You can set up a/b tests, do payload logging to elastic and then do monitoring on top of that, and it has drift detection and model explainer modules too. Idk about great expectations integration, but you could probably do something with a custom transformer module as part of the inference graph.
What are some alternatives?
hate-speech-and-offensive-language - Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
zotero - Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share your research sources.
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Fleet - Open-source platform for IT, security, and infrastructure teams. (Linux, macOS, Chrome, Windows, cloud, data center)
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
great_expectations - Always know what to expect from your data.
datapane - Build and share data reports in 100% Python
alibi-detect - Algorithms for outlier, adversarial and drift detection
deno - A modern runtime for JavaScript and TypeScript.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.