Cohere offers an API to add cutting-edge language processing to any system. Cohere trains massive language models and puts them behind a simple API. Moreover, through training, users can create massive models customized to their use case and trained on their data. This way, Cohere handles the complexities of collecting massive amounts of text data, the ever evolving neural network architectures, distributed training, and serving models around the clock.
Two major categories of large language models are generative language models (like GPT2 and GPT3) and representation language models (like BERT). Cohere offers variants of both types.
Here are a few examples of language understanding systems that can be built on top of large language models.
Large language models present a breakthrough in text generation. For the first time in history, we have software programs that can write text that sounds like it’s written by humans. These capabilities open doors to use cases like summarization or paraphrasing.
A summarization prompt in the Cohere playground shows this output (in bold):
Large language models can be adapted to new tasks with impressive speed. For tasks which appear in the training data (i.e. documents on the web), language models can successfully summarize text without being shown any examples at all.
Summarization and paraphrasing both use the generate endpoint.
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Classification is one of the most common use cases in language processing. Building systems on top of language models can automate language-based tasks and save time and energy.
There's more than one way to build a classifier on top of Cohere's language models. It's worth experimenting to see which method works best for your use case. The simpler methods can get you quick results, while the more advanced methods need more data and will lead to better results.
On the simpler side are methods like using the Classify endpoint for classification. More industrial-grade classifiers can be built by fitting a classifier on top of the embed endpoint (see: Embedding endpoint for classification).
Think of how many repeated questions have to be answered by a customer service agent every day. Language models are capable of judging text similarity and determining if an incoming question is similar to questions already answered in the FAQ section.
There are multiple things your system can do once it receives the similarity scores — one possible next action is to simply show the answer to the most similar question (if above a certain similarity threshold). Another possible next action is to make that suggestion to a customer service agent.
Updated 29 days ago