ELMo vs Terracotta

Embeddings from Language Models (ELMo) is a groundbreaking language representation model that helps machines understand complex characteristics of word usage, including both syntax and semantics, and recognizes the variations of word use across different linguistic contexts. ELMo achieves this by utilizing word vectors that are learned as functions of the internal states of a pre-trained deep bidirectional language model (biLM). BiLM uniquely models both forward and backward language model likelihoods, and when adding ELMo to a task-specific model, the weights of the biLM are frozen. The ELMo vector is then concatenated with a baseline representation of tokens and fed into a task RNN, enhancing the model's performance by providing rich, context-aware word embeddings.

Terracotta is a cutting-edge platform designed to enhance the workflow for developers and researchers working with large language models (LLMs). This intuitive and user-friendly platform allows you to manage, iterate, and evaluate your fine-tuned models with ease. With Terracotta, you can securely upload data, fine-tune models for various tasks like classification and text generation, and create comprehensive evaluations to compare model performance using both qualitative and quantitative metrics. Our tool supports connections to major providers like OpenAI and Cohere, ensuring you have access to a broad range of LLM capabilities. Terracotta is the creation of Beri Kohen and Lucas Pauker, AI enthusiasts and Stanford graduates, who are dedicated to advancing LLM development. Join our email list to stay informed on the latest updates and features that Terracotta has to offer.