Summary of Nomic Embed: Training a Reproducible Long Context Text Embedder, by Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar
Nomic Embed: Training a Reproducible Long Context Text Embedder
by Zach Nussbaum, John X. Morris, Brandon Duderstadt, Andriy Mulyar
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new English text embedding model, nomic-embed-text-v1, which surpasses existing models on benchmark datasets. This open-source model is trained on 8192 context lengths and outperforms OpenAI’s Ada-002 and text-embedding-3-small on short-context MTEB and long-context LoCo benchmarks. The model’s training code and weights are released under an Apache 2.0 license, along with the full curated training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new model can be replicated by downloading the training code and data from GitHub. It’s a big step forward in text embedding technology, which is important for many applications like natural language processing and machine learning. |
Keywords
» Artificial intelligence » Embedding » Machine learning » Natural language processing
