Topic Feed Lab

A small open model, fastino/GLiNER2.5-Decide, reads each item once and gives it a score from 0 to 1 for each of 48 topics, in one call. It runs on CPU. It is a classifier, not an LLM: it does not write text.

• What is scored: new and updated public Hub models and datasets that have a real card (at least a few sentences), and papers from Hugging Face Daily Papers. The first week also includes arXiv computer-science papers from a one-off backfill. Quantized and converted models take their base model's topics. Items with no README or an empty template card are skipped.
• When it shows up: a worker process in this Space follows the Hub, checks whether each card changed, scores changed items and stores the scores, usually within a minute or two of the change. The page reads the stored scores.
• Thresholds: an item is tagged with a topic when its score reaches that topic's threshold. The defaults are set so each topic tags about as many items as a larger LLM did on a reference sample.
• Your confirmations: tick or untick the topics on an item and press Confirm. After 3 ✓ for a topic, its threshold is fitted to your confirmations. You can also set it by hand under calibrate. This is stored in your browser only.
• Caveats: the topics are zero-shot, so some tags are wrong, and broad topics catch loosely related items. Likes and downloads are a snapshot from when the item was scored.