This repo contains code examples and jupyter notebooks for you to get started with the Cohere Platform

Create a simple sentiment classifier using Cohere's embeddings: [Notebook | Colab]

Summarize or paraphrase text using Cohere's Generate endpoint. [Notebook | Colab]

Build search products that search by meaning and go beyond keyword matching. [Notebook | Colab]

Extract name entities from text using only a few examples. [Notebook | Colab]

Recommend articles with via text embedding, classification, and extraction. [Notebook | Colab]

Intuition behind text embeddings, what use cases they are good for, and how they can be customized using finetuning. [Notebook | Colab]

Combing for insight in 10,000 Hacker News posts with text clustering [Notebook | Colab]

A quick tour of what’s possible with language AI via Cohere’s Large Language Model (LLM) API [Notebook | Colab]

Semantic search and clustering of papers in Journal of Artificial Intelligence Research [Notebook | Colab]

Helping you evaluate which of these options best suits your objectives [Notebook | Colab]

Describe your story in two sentences, then guide Cohere's language model as it turns it into a bigger story.

[Notebook | Colab | Colab - v2]

Explore the Generate endpoint, one of the endpoints available from the Cohere API. [Notebook | Colab]

Create a custom Generate model and evaluate the generation quality. [Notebook | Colab]

Use Langchain to efficiently build semantic search applications on top of Cohere’s multilingual model. [Notebook | Colab]

Build a content idea generator that is backed by keyword research. [Notebook | Colab]

Understand the range of use cases that you can build with the Cohere Command model. [Notebook | Colab]

Tips and ideas for constructing prompts for the Command Model [Notebook | Colab]