# Using iris finetune

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<mark style="color:green;">Short cut! If you'd rather use the GUI than the command line you can find the command builder on the web app at app.titanml.co</mark>

*Remember to ensure you have the latest version of iris installed before running any command! You can check this by running `pip install update titan-iris.`*

You can now fine-tune a model on a particular dataset on the TitanML platform by using `iris finetune`. `iris finetune` sends a request to the backend based on your specified model and dataset, along with some information about your desired task. For example:

```bash
iris finetune \
	--model google/electra-large-discriminator \
	--dataset squad_v2 \
	--task question_answering \
	--name my_test_squadv2 \
	--has-negative
```

This will fine-tune an ELECTRA Large model on the SQuAD v2 question-answering dataset using the default values for batch size, learning rate and number of training epochs (16, 2e-5 and 1, respectively). To specify your own values for these hyperparameters, you can include any or all of them as arguments:

```bash
iris finetune \
	--model google/electra-large-discriminator \
	--dataset squad_v2 \
	--task question_answering \
	--name test_finetune_squad \
	--has-negative \
	--batch-size 32 \
	--learning-rate 3e-5 \
	--num-epochs 10
```

Or in short form:

```
iris finetune -m TitanML/Electra-Large-SQUADV2 -d squad_v2 -t question_answering -hn -n test_finetune_squad -bs 31 -lr 3e-5 -ep 10
```

The same applies to sequence and token classification tasks:

```bash
iris finetune \
	--task sequence_classification \
	--dataset glue \
	--subset mrpc \
	--model TitanML/Electra-Large-MRPC \
	--name test_finetune_mrpc \
	--text-fields sentence1 \
	--text-fields sentence2 \
	--num-labels 2 \
	--batch-size 32 \
	--learning-rate 3e-5 \
	--num-epochs 10
```

```bash
iris finetune \
        --model TitanML/Electra-Large-CONLL2003 \
        --dataset conll2003 \
        --subset conll2003 \
        --task token_classification \
        --name test_finetune_conll \
        -ln 0:O \
        -ln 1:B-PER -ln 2:I-PER \
        -ln 3:B-ORG -ln 4:I-ORG \
        -ln 5:B-LOC -ln 6:I-LOC \
        -ln 7:B-MISC -ln 8:I-MISC \
        --labels-column ner_tags \
        --batch-size 32 \
	--learning-rate 3e-5 \
	--num-epochs 10
```