# Prepare Training Data#

Finetuner accepts training data and evaluation data in the form of DocumentArray objects. Because Finetuner follows a supervised-learning scheme, you should assign a label to each Document inside your DocumentArray as follows:

from docarray import Document, DocumentArray

train_da = DocumentArray([
Document(
content='pencil skirt slim fit available for sell',
tags={'finetuner_label': 'skirt'}
),
Document(
content='stripped over-sized shirt for sell',
tags={'finetuner_label': 't-shirt'}
),
...,
])

from docarray import Document, DocumentArray

train_da = DocumentArray([
Document(
uri='https://...skirt-1.png',
tags={'finetuner_label': 'skirt'},
),
Document(
uri='https://...t-shirt-1.png',
tags={'finetuner_label': 't-shirt'},
),
...,
])

from docarray import Document, DocumentArray

train_da = DocumentArray([
Document(
chunks=[
Document(
content='pencil skirt slim fit available for sell',
modality='text',
),
Document(
uri='https://...skirt-1.png',
modality='image',
),
],
),
Document(
chunks=[
Document(
content='stripped over-sized shirt for sell',
modality='text',
),
Document(
uri='https://...shirt-1.png',
modality='image',
),
],
),
])


As was shown in the above code blocks, when fine-tuning a model with a single modality (e.g. image), you only need to create a Document with content and tags with the finetuner_label.

For cross-modality (text-to-image) fine-tuning with CLIP, you should create a root Document which wraps two chunks with the image and text modality. The image and text form a pair. Evaluation data should be created the same way as above.

CLIP model explained

OpenAI CLIP model wraps two models: a vision transformer and a text transformer. During fine-tuning, we’re optimizing two models in parallel.

At the model saving time, you will discover, we are saving two models to your local directory.

If you need to evaluate metrics on separate evaluation data, It is recommended to create another DocumentArray as above only for evaluation purposes.

Carry on, you’re almost there!