Use the Cohere platform to build natural language understanding and generation into your product with a few lines of code. Cohere’s large language models can solve a broad spectrum of natural language use cases, including classification, semantic search, paraphrasing, summarization, and content generation. Through finetuning, users can create massive models customized to their use case and trained on their data.
- Playground overview - start experimenting with the models and the various endpoints.
- Using the Cohere API - install the Python, Node.js, or Go SDKs.
- Intro to Large Language Models with Cohere - visual overview of large language models and some of their applications.
- Model cards - learn about Cohere’s generation and representation models and their intended use.
- Co:mmunity - get help and share your experience!
Text classification is one of the most useful applications of Large Language Models (LLMs). They can classify text using a small number of examples (few-shot classification).
LLMs can write coherent text like no other human technology before them could. This can be used for creative copy, but also for summarization and paraphrasing. We tune the inputs using prompt engineering techniques that get the model to produce useful outputs. Important text generations parameters include top-k and top-p.
Learn how to use embeddings to build semantic search capabilities.
The Cohere Platform endpoints are:
- Generate: Generate text from a model in response to an input prompt
- Embed: Retrieve the sentence embeddings from a representation model
- Classify: Perform classification by using a few examples
The Command Line Tool is an alternative to our web interface, which allows you to login to your Cohere account, manage API Keys, and run finetunes.
The Responsible Use documentation aims to guide developers in using language models constructively and ethically. Toward this end, we've published guidelines for using our API safely, statistics regarding the environmental impact of pre-training our language models, as well as our processes around harm prevention.