Home Community Nomic AI Releases the First Fully Open-Source Long Context Text Embedding Model that Surpasses OpenAI Ada-002 Performance on Various Benchmarks

Nomic AI Releases the First Fully Open-Source Long Context Text Embedding Model that Surpasses OpenAI Ada-002 Performance on Various Benchmarks

Nomic AI Releases the First Fully Open-Source Long Context Text Embedding Model that Surpasses OpenAI Ada-002 Performance on Various Benchmarks

Within the evolving landscape of natural language processing (NLP), the flexibility to know and process extensive textual contexts is paramount. Recent advancements, as highlighted by Lewis et al. (2021), Izacard et al. (2022), and Ram et al. (2023), have significantly propelled the capabilities of language models, particularly through the event of text embeddings. These embeddings serve because the backbone for a plethora of applications, including retrieval-augmented generation for big language models (LLMs) and semantic search. They transform sentences or documents into low-dimensional vectors, capturing the essence of semantic information, which in turn facilitates tasks like clustering, classification, and data retrieval.

Nevertheless, a glaring limitation has been the context length that these models can handle. The vast majority of well known open-source models on the MTEB benchmark, akin to E5 by Wang et al. (2022), GTE by Li et al. (2023), and BGE by Xiao et al. (2023), are confined to a context length of 512 tokens. This restriction undermines their utility in scenarios where understanding the broader document context is crucial. In contrast, models able to surpassing a context length of 2048, like Voyage-lite-01-instruct by Voyage (2023) and text-embedding-ada-002 by Neelakantan et al. (2022), remain behind closed doors.

Amid this backdrop, the introduction of nomicembed-text-v1 marks a big milestone. This model isn’t only open-source but in addition boasts a formidable sequence length of 8192, outperforming its predecessors in each short and long-context evaluations. What sets it apart is its comprehensive approach, merging the strengths of open weights, open data, and a 137M parameter design under an Apache-2 license, ensuring accessibility and transparency.

The journey to achieving such a feat involved meticulous stages of information preparation and model training. Initially, a Masked Language Modeling Pretraining phase utilized resources like BooksCorpus and a Wikipedia dump from 2023, employing the bert-base-uncased tokenizer to create data chunks suited to long-context training. This was followed by Unsupervised Contrastive Pretraining, leveraging an enormous collection of 470 million pairs across diverse datasets to refine the model’s understanding through consistency filtering and selective embedding. 

The architecture of nomicembed-text-v1 reflects a thoughtful adaptation of BERT to accommodate the prolonged sequence length. Innovations akin to rotary positional embeddings, SwiGLU activation, and the combination of Flash Attention highlight a strategic overhaul to reinforce performance and efficiency. The model’s training regimen, characterised by a 30% masking rate and optimized settings, further underscores the rigorous effort to attain optimal results.

When subjected to the pains of benchmarks like GLUE, MTEB, and specialized long-context assessments, nomicembed-text-v1 demonstrated exceptional prowess. Notably, its performance within the JinaAI Long Context Benchmark and the LoCo Benchmark underscores its superiority in handling extensive texts, an area where many predecessors faltered.

Yet, the journey of nomicembed-text-v1 extends beyond mere performance metrics. Its development process, emphasizing end-to-end auditability and the potential for replication, sets a brand new standard for transparency and openness within the AI community. By releasing the model weights, codebase, and a curated training dataset, the team behind nomicembed-text-v1 invites ongoing innovation and scrutiny.

In conclusion, nomicembed-text-v1 emerges not only as a technological breakthrough but as a beacon for the open-source movement in AI. It dismantles barriers to entry within the domain of long-context text embeddings, promising a future where the depth of understanding matches the breadth of human discourse. 

Take a look at the Paper and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and Google News. Join our 37k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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Vineet Kumar is a consulting intern at MarktechPost. He’s currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He’s a Machine Learning enthusiast. He’s obsessed with research and the newest advancements in Deep Learning, Computer Vision, and related fields.

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