# Basic Concepts#

Finetuner organizes your training based on two concepts: Experiment and Run.

An Experiment defines the machine learning task you’re fine-tuning for. A Run refers to a single execution of the Experiment with a specific configuration. An Experiment contains a list of Runs, each with different configurations. For example:

• Experiment: Fine-tune a transformer on the QuoraQA dataset.

• Run1: Use bert-based model.

• Run2: Use sentence-transformer model.

• Experiment: Fine-tune ResNet on WILD dataset.

• Run1: Use ResNet18 with learning rate 0.01 and SGD optimizer.

• Run2: Use ResNet50 with learning rate 0.01 and SGD optimizer.

• Run3: Use ResNet50 with learning rate 0.0001 and Adam optimizer.

All information and data produced during using Finetuner is linked to those two concepts. Each Experiment and each Run has a name. The name of the Experiment should be unique and the name of the Run is also required to be unique for each Experiment. Thus, if you want to retrieve the logs of a run or download the fine-tuned model later on, you can do this with the respective experiment and run names, as explained in section Save Artifact.

When you start the fine-tuning job, you can declare the experiment_name and run_name like this:

import finetuner

finetuner.fit(
...,
experiment_name='quora-qa-finetune',
run_name='quora-qa-finetune-bert',
)


Please note that these two arguments are optional. If not supplied, Finetuner will use the current working directory as a default experiment_name, and generate a random run_name for you, e.g., “infallible-colden”.