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Maria Khalusova

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Zero-shot image-to-text generation with BLIP-2

Feb 15, 2023

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2039

𝗨𝘀𝗶𝗻𝗴 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲 🧑‍⚖️ 𝗳𝗼𝗿 𝗮𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻

Evaluating LLM outputs is often hard, since many tasks require open-ended answers for which no deterministic metrics work: for instance, when asking a model to summarize a text, there could be hundreds of correct ways to do it. The most versatile way to grade these outputs is then human evaluation, but it is very time-consuming, thus costly.

🤔 Then 𝘄𝗵𝘆 𝗻𝗼𝘁 𝗮𝘀𝗸 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗟𝗟𝗠 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, by providing it relevant rating criteria? 👉 This is the idea behind LLM-as-a-judge.

⚙️ To implement a LLM judge correctly, you need a few tricks.
✅ So 𝗜'𝘃𝗲 𝗷𝘂𝘀𝘁 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝗵𝗼𝘄 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗶𝘁 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆 𝗶𝗻 𝗼𝘂𝗿 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲 𝗖𝗼𝗼𝗸𝗯𝗼𝗼𝗸! (you can run it instantly in Google Colab)
➡️ 𝗟𝗟𝗠-𝗮𝘀-𝗮-𝗷𝘂𝗱𝗴𝗲 𝗰𝗼𝗼𝗸𝗯𝗼𝗼𝗸: https://huggingface.co/learn/cookbook/llm_judge

The Cookbook is a great collection of notebooks demonstrating recipes (thus the "cookbook") for common LLM usages. I recommend you to go take a look!
➡️ 𝗔𝗹𝗹 𝗰𝗼𝗼𝗸𝗯𝗼𝗼𝗸𝘀: https://huggingface.co/learn/cookbook/index

Thank you @MariaK for your support!

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DeepLearning.AI just announced a new short course: Open Source Models with Hugging Face 🤗, taught by Hugging Face's own Maria Khalusova, Marc Sun and Younes Belkada!

As many of you already know, Hugging Face has been a game changer by letting developers quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models.

You'll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces.

Thank you very much to Hugging Face's wonderful team for working with us on this.

You can sign up for the course here: https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/