Among the most popular use cases for language embeddings is 'text classification,' in which different pieces of text -- blog posts, lyrics, poems, headlines, etc. -- are grouped based on their similarity, their sentiment, or some other property.

Here, we'll discuss how to perform simple text classification tasks with Cohere's `classify` endpoint, and provide links to more information on how to fine-tune this endpoint for more specialized work.

## Few-Shot Classification with Cohere's `classify` Endpoint

Generally, training a text classifier requires a tremendous amount of data. But with large language models, it's now possible to create so-called 'few shot' classification models able to perform well after seeing a far smaller number of samples.

In the next few sections, we'll create a sentiment analysis classifier to sort text into "positive," "negative," and "neutral" categories.

### Setting up the SDK

First, let's import the required tools and set up a Cohere client.

```python PYTHON
import cohere
from cohere import ClassifyExample
```

```python PYTHON
co = cohere.Client("COHERE_API_KEY")  # Your Cohere API key
```

### Preparing the Data and Inputs

With the `classify` endpoint, you can create a text classifier with as few as two examples per class, and each example **must** contain the text itself and the corresponding label (i.e. class). So, if you have two classes you need a minimum of four examples, if you have three classes you need a minimum of six examples, and so on.

Here are examples, created as `ClassifyExample` objects:

```python PYTHON
examples = [
    ClassifyExample(text="I’m so proud of you", label="positive"),
    ClassifyExample(
        text="What a great time to be alive", label="positive"
    ),
    ClassifyExample(text="That’s awesome work", label="positive"),
    ClassifyExample(text="The service was amazing", label="positive"),
    ClassifyExample(text="I love my family", label="positive"),
    ClassifyExample(
        text="They don't care about me", label="negative"
    ),
    ClassifyExample(text="I hate this place", label="negative"),
    ClassifyExample(
        text="The most ridiculous thing I've ever heard",
        label="negative",
    ),
    ClassifyExample(text="I am really frustrated", label="negative"),
    ClassifyExample(text="This is so unfair", label="negative"),
    ClassifyExample(text="This made me think", label="neutral"),
    ClassifyExample(text="The good old days", label="neutral"),
    ClassifyExample(text="What's the difference", label="neutral"),
    ClassifyExample(text="You can't ignore this", label="neutral"),
    ClassifyExample(text="That's how I see it", label="neutral"),
]
```

Besides the examples, you'll also need the 'inputs,' which are the strings of text you want the classifier to sort. Here are the ones we'll be using:

```python PYTHON
inputs = [
    "Hello, world! What a beautiful day",
    "It was a great time with great people",
    "Great place to work",
    "That was a wonderful evening",
    "Maybe this is why",
    "Let's start again",
    "That's how I see it",
    "These are all facts",
    "This is the worst thing",
    "I cannot stand this any longer",
    "This is really annoying",
    "I am just plain fed up",
]
```

### Generate Predictions

Setting up the model is quite straightforward with the `classify` endpoint. We'll use Cohere's `embed-english-v3.0` model, here's what that looks like:

```python PYTHON
def classify_text(inputs, examples):
    """
    Classifies a list of input texts given the examples
    Arguments:
        model (str): identifier of the model
        inputs (list[str]): a list of input texts to be classified
        examples (list[Example]): a list of example texts and class labels
    Returns:
        classifications (list): each result contains the text, labels, and conf values
    """

    # Classify text by calling the Classify endpoint
    response = co.classify(
        model="embed-english-v3.0", inputs=inputs, examples=examples
    )

    classifications = response.classifications

    return classifications


# Classify the inputs
predictions = classify_text(inputs, examples)

print(predictions)
```

Here’s a sample output returned (note that this output has been truncated to make it easier to read, you'll get much more in return if you run the code yourself):

```
[ClassifyResponseClassificationsItem(id='9df6628d-57b2-414c-837e-c8a22f00d3db',
                                     input='hello, world! what a beautiful day',
                                     prediction='positive',
                                     predictions=['positive'],
                                     confidence=0.40137812,
                                     confidences=[0.40137812],
                                     labels={'negative': ClassifyResponseClassificationsItemLabelsValue(confidence=0.23582731),
                                             'neutral': ClassifyResponseClassificationsItemLabelsValue(confidence=0.36279458),
                                             'positive': ClassifyResponseClassificationsItemLabelsValue(confidence=0.40137812)},
                                     classification_type='single-label'),
 ClassifyResponseClassificationsItem(id='ce2c3b0b-ce98-4905-9ef5-fc83c6848fc5',
                                     input='it was a great time with great people',
                                     prediction='positive',
                                     predictions=['positive'],
                                     confidence=0.49054274,
                                     confidences=[0.49054274],
                                     labels={'negative': ClassifyResponseClassificationsItemLabelsValue(confidence=0.19989403),
                                             'neutral': ClassifyResponseClassificationsItemLabelsValue(confidence=0.30956325),
                                             'positive': ClassifyResponseClassificationsItemLabelsValue(confidence=0.49054274)},
                                     classification_type='single-label')
 ....]
```

Most of this is pretty easy to understand, but there are a few things worth drawing attention to.

Besides returning the predicted class in the `prediction` field, the endpoint also returns the `confidence` value of the prediction, which varies between 0 (unconfident) and 1 (completely confident).

Also, these confidence values are split among the classes; since we're using three, the confidence values for the "positive," "negative," and "neutral" classes must add up to a total of 1.

Under the hood, the classifier selects the class with the highest confidence value as the “predicted class.” A high confidence value for the predicted class therefore indicates that the model is very confident of its prediction, and vice versa.

### What If I Need to Fine-Tune the `classify` endpoint?

Cohere has [dedicated documentation](/guides/get-started-fine-tuning-fine-tuning) on fine-tuning the `classify` endpoint for bespoke tasks. You can also read this [blog post](/guides/get-started-archive-llm-university-intro-text-representation-fine-tuning-for-classification), which works out a detailed example.

## Related pages

- [Changelog](../changelog.md)
- [Cohere](../index.md)
- [Cohere API](./cohere-api-index.md)
- [Cohere Labs](./cohere-labs-index.md)
- [Cohere Platform](./cohere-platform-index.md)
- [Cookbooks](./cookbooks-index.md)
- [Deployment Options](./deployment-options-index.md)
- [Embeddings (Vectors, Search, Retrieval)](./embeddings-vectors-search-retrieval-index.md)
- [Get Started](./get-started-index.md)
- [Going to Production](./going-to-production-index.md)

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