Co.Summarize

In this example, we want to summarize a passage from a news article into its main point.

1. Set up

Install the SDK, if you haven't already.

$ pip install cohere

Next, set up the Cohere client.

import cohere
co = cohere.Client(api_key)

2. Create prompt

Store the document you want to summarize into a variable

text ="""Ice cream is a sweetened frozen food typically eaten as a snack or dessert. It may be made from milk or cream and is flavoured with a sweetener, either sugar or an alternative, and a spice, such as cocoa or vanilla, or with fruit such as strawberries or peaches. It can also be made by whisking a flavored cream base and liquid nitrogen together. Food coloring is sometimes added, in addition to stabilizers. The mixture is cooled below the freezing point of water and stirred to incorporate air spaces and to prevent detectable ice crystals from forming. The result is a smooth, semi-solid foam that is solid at very low temperatures (below 2 °C or 35 °F). It becomes more malleable as its temperature increases.The meaning of the name "ice cream" varies from one country to another. In some countries, such as the United States, "ice cream" applies only to a specific variety, and most governments regulate the commercial use of the various terms according to the relative quantities of the main ingredients, notably the amount of cream. Products that do not meet the criteria to be called ice cream are sometimes labelled "frozen dairy dessert" instead. In other countries, such as Italy and Argentina, one word is used fo all variants. Analogues made from dairy alternatives, such as goat's or sheep's milk, or milk substitutes (e.g., soy, cashew, coconut, almond milk or tofu), are available for those who are lactose intolerant, allergic to dairy protein or vegan."""

3. Define model settings

The endpoint has a number of settings you can use to control the kind of output it generates. The full list is available in the API reference, but let’s look at a few:

model - summarize-xlarge or summarize-medium. Generally, medium models are faster while larger models will perform better.
temperature - Ranges from 0 to 5. Controls the randomness of the output. Higher values tend to generate more creative outcomes, and gives you the opportunity of generating various summaries for the same input text. It also might include more hallucinations. Use a higher value if for example you plan to perform a selection of various summaries afterwards
length - You can choose between short, medium and long. Short summaries are roughly up to 2 sentences long, medium between 3 and 5 and long might have more 6 or more sentences.
format - You can choose between paragraph and bullet. Paragraph generates a coherent sequence of sentences, while bullet outputs the summary in bullet points
extractiveness -low, medium, high
additional_command- You can input additional direction for a command.

4. Generate the summary

Call the endpoint via the co.summarize() method, specifying the prompt and the rest of the model settings.

Python

response = co.summarize( 
    model='summarize-xlarge', 
    length='medium',
    abstractiveness='medium'
)

summary = response.summary