Streamlit & Ollama: Querying Mistral LLM Locally and Generating Titles & Keywords

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In this video, we delve into the challenge of evaluating the quality of AI-generated text, focusing on how to ensure it is accurate and avoids “hallucinations.” We’ll demonstrate how to measure the semantic similarity between the output generated by the Mistral LLM locally with the help of Ollama and the human proposal (the gold standard) created in the first video.

We’ll cover the following steps:
1. Launch a Python script locally to measure semantic similarity.
2. Use the “all-MiniLM-L6-v2” model for comparison.
3. Check the script `029_ia_sentence_transformers_import.py` for implementation details.

We’ll also introduce the concept of “sentence embeddings” as a solution for comparing sentences based on meaning rather than just words. Additionally, we’ll present our approach for validating the quality of AI-generated text using the “spacy-llm” package, along with other tools like “pytextrank” and “pysentence-similarity.”

You can read the article on my blog: Content Quality: How Sentence Embeddings Can Save AI-Generated Content and some other concerns on AI: Environmental Impact, Job Loss
https://wp.me/p3Vuhl-3mv

You can listen to the “podcast” of Blog Post Audio made with NotebookLM on this post: https://on.soundcloud.com/61ufT6BNt4WzhvwX7

The code is available on my github account: https://github.com/bflaven/ia_usages/tree/main/ia_spacy_llm

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