In this section, we will walk through how you can prepare your data for fine-tuning models for Classification.

For classification fine-tunes we can choose between two types of datasets:

1. Single-label data
2. Multi-label data

To be able to start a fine-tune you need at least **40** examples. Each label needs to have at least **5** examples and there should be at least **2** unique labels.

### Single-label Data

Single-label data consists of a text and a label. Here's an example:

- **text**: This movie offers that rare combination of entertainment and education
- **label**: positive

Please notice that both text and label are required fields. When it comes to single-label data, you have the option to save your information in either a `.jsonl` or `.csv` format.

```json JSONL
{"text":"This movie offers that rare combination of entertainment and education", "label":"positive"}
{"text":"Boring movie that is not as good as the book", "label":"negative"}
{"text":"We had a great time watching it!", "label":"positive"}
```

```txt CSV
text,label
This movie offers that rare combination of entertainment and education,positive
Boring movie that is not as good as the book,negative
We had a great time watching it!,positive
```

### Multi-label Data

Multi-label data differs from single-label data in the following ways:

- We only accept `jsonl` format
- An example might have more than one label
- An example might also have 0 labels

```json JSONL
{"text":"About 99% of the mass of the human body is made up of six elements: oxygen, carbon, hydrogen, nitrogen, calcium, and phosphorus.", "label":["biology", "physics"]}
{"text":"The square root of a number is defined as the value, which gives the number when it is multiplied by itself", "label":["mathematics"]}
{"text":"Hello world!", "label":[]}
```

### Clean your Dataset

To achieve optimal results, we suggest cleaning your dataset _before_ beginning the fine-tuning process. Here are some things you might want to fix:

- Make sure that your dataset does not contain duplicate examples.
- Make sure that your examples are utf-8 encoded

If some of your examples don't pass our validation checks, we'll filter them out so that your fine-tuning job can start without interruption. As long as you have a sufficient number of valid training examples, you're good to go.

### Evaluation Datasets

Evaluation data is utilized to calculate metrics that depict the performance of your fine-tuned model. You have the option of generating a validation dataset yourself, or you can opt instead to allow us to divide your training file into separate train and evaluation datasets on our end.

### Create a Dataset with the Python SDK

If you intend to fine-tune through our UI you can skip to the next chapter. Otherwise continue reading to learn how to create datasets for fine-tuning via our [Python SDK](/guides/get-started-fine-tuning-fine-tuning). Before you start, we recommend that you read about the [dataset](https://docs.cohere.com/fern/pages/get-started/datasets.mdx) API. Below you will find some code samples on how create datasets via the SDK:

```python PYTHON
import cohere

# instantiate the Cohere client
co = cohere.ClientV2("YOUR_API_KEY")


## single-label dataset
single_label_dataset = co.datasets.create(
    name="single-label-dataset",
    data=open("path/to/train.csv", "rb"),
    type="single-label-classification-finetune-input",
)

print(co.wait(single_label_dataset))

## multi-label dataset
multi_label_dataset = co.datasets.create(
    name="multi-label-dataset",
    data=open("path/to/train.jsonl", "rb"),
    type="multi-label-classification-finetune-input",
)

print(co.wait(multi_label_dataset))

## add an evaluation dataset
multi_label_dataset_with_eval = co.datasets.create(
    name="multi-label-dataset-with-eval",
    data=open("path/to/train.jsonl", "rb"),
    eval_data=open("path/to/eval.jsonl", "rb"),
    type="multi-label-classification-finetune-input",
)

print(co.wait(multi_label_dataset_with_eval))
```

## 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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