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Found 5 papers, 4 papers with code
Nomic Embed: Training a Reproducible Long Context Text Embedder
This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding model that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short and long-context tasks.
GPT4All: An Ecosystem of Open Source Compressed Language Models
It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem.
Clinical Concept Linking with Contextualized Neural Representations
We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name.
MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning
Clinical notes contain an abundance of important but not-readily accessible information about patients.
Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models
Clinical notes contain an extensive record of a patient's health status, such as smoking status or the presence of heart conditions.
Ranked #1 on
Clinical Note Phenotyping
on I2B2 2006: Smoking
