Articles for tag machine-learning

• 
  Would Fine-Tuning Have Beaten Jev? 16 Oct 2026

  The third post in my series on describing a classifier with Jev versus training one. This time I fine-tuned BERT on the same Jigsaw toxicity comments. With enough labels it ranks toxic comments better than Jev and the cloud-embedding models, but it needs about 500 labels to pass Jev, a GPU to train, and it misses almost every comment on the two rarest labels.

  • jev
  • classification
  • machine-learning
  • pytorch
  • bert
  • fine-tuning
• 
  The Bouncer's Dilemma: Jev, k-NN, and MLP for Comment Moderation 13 Oct 2026

  In my last post, I skipped training a spam classifier and used Jev instead. Here, I compare Jev with a neural network and k-NN built from thousands of labeled comments in the Jigsaw toxicity dataset: how accurate each one is, how many labels they need, and what they cost to run.

  • jev
  • system-one
  • decision-model
  • classification
  • machine-learning
  • pytorch
  • vector-search
• C
  Count tokens with the Gemma 2 Tokenizer in Rust 22 Nov 2024

  Quickly count tokens in Large Language Models (LLMs) like Gemini and Gemma using Rust. This efficient method avoids slow network calls, leveraging the `tokenizers` crate for local processing. The code example demonstrates token counting with minimal dependencies, even handling `aarch64` architecture challenges. Get started with fast, local token counting now!

  • rust
  • gemma
  • tokenizer
  • artificial-intelligence
  • machine-learning