Agentic Evaluations Workshop - Deep Dive on the Future on Evals for Agents.
Skills:
Agent Foundations90%Tool Use & Function Calling80%RAG Evaluation80%Multi-Agent Systems70%Autonomous Workflows70%
As agents evolve from text conversations to autonomous agents capable of multi-step reasoning, tool use, and real-world task completion, evaluation methodologies must evolve to keep up. We will discuss topics related to questions such as:
- Where is the state of the art in evaluating agentic systems?
- Why are agentics systems’ performance on benchmarks not reflected in usage?
- How can we evaluate the agentic systems and language models that we use?
This workshop brings together diverse perspectives from academia, industry, and policy to explore the frontier of agentic evaluation.
Including:
Avijit Ghosh, Hugging Face
Arvind Narayanan, Princeton University
Pierre Andrews, Meta
J.J. Allaire, UK AI Security Institute
Mahesh Sathiamoorthy, Bespoke Labs
Nathan Habib, Hugging Face
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The Future of Natural Language Processing
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Trends in Model Size & Computational Efficiency in NLP
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Increasing Data Usage in Natural Language Processing
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In Domain & Out of Domain Generalization in the Future of NLP
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The Limits of NLU & the Rise of NLG in the Future of NLP
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The Lack of Robustness in the Future of NLP
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Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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Train a Hugging Face Transformers Model with Amazon SageMaker
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What is Transfer Learning?
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The pipeline function
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Navigating the Model Hub
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Transformer models: Decoders
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The Transformer architecture
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Transformer models: Encoder-Decoders
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Transformer models: Encoders
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Keras introduction
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The push to hub API
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Fine-tuning with TensorFlow
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Learning rate scheduling with TensorFlow
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TensorFlow Predictions and metrics
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Welcome to the Hugging Face course
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The tokenization pipeline
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Supercharge your PyTorch training loop with Accelerate
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The Trainer API
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Batching inputs together (PyTorch)
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Batching inputs together (TensorFlow)
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Hugging Face Datasets overview (Pytorch)
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Hugging Face Datasets overview (Tensorflow)
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What is dynamic padding?
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What happens inside the pipeline function? (PyTorch)
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What happens inside the pipeline function? (TensorFlow)
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Instantiate a Transformers model (PyTorch)
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Instantiate a Transformers model (TensorFlow)
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Preprocessing sentence pairs (PyTorch)
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Preprocessing sentence pairs (TensorFlow)
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Write your training loop in PyTorch
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Managing a repo on the Model Hub
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Chapter 1 Live Session with Sylvain
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Chapter 2 Live Session with Lewis
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The push to hub API
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Chapter 2 Live Session with Sylvain
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Chapter 3 live sessions with Lewis (PyTorch)
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Day 1 Talks: JAX, Flax & Transformers 🤗
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Day 2 Talks: JAX, Flax & Transformers 🤗
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Day 3 Talks JAX, Flax, Transformers 🤗
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Chapter 4 live sessions with Omar
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Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
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Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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[Webinar] How to add machine learning capabilities with just a few lines of code
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Hugging Face + Zapier Demo Video
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Hugging Face + Google Sheets Demo
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Hugging Face Infinity Launch - 09/28
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Build and Deploy a Machine Learning App in 2 Minutes
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Hugging Face Infinity - GPU Walkthrough
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Otto - 🤗 Infinity Case Study
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Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
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Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
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🤗 Tasks: Causal Language Modeling
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🤗 Tasks: Masked Language Modeling
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