GitHub - AnswerDotAI/rerankers

Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more

GitHub - arthur-ai/bench: A tool for evaluating LLMs

Boosting annotator efficiency with Large Language Models

In July and August, we released LLM-assisted recipes for data annotation and prompt engineering:

• NER: ner.llm.correct, ner.llm.fetch


• Spancat: spans.llm.correct, spans.llm.fetch


• Textcat: textcat.llm.correct, textcat.llm.fetch


• Prompt engineering and terms: ab.llm.tournament terms.llm.fetch

Prodigy's... See more