# Model Cards

SetFit comes with extensive automatically generated model cards/READMEs. In this how-to guide, we will explore how to make the most of this automatic generation.

As an example, the [tomaarsen/setfit-all-MiniLM-L6-v2-sst2-32-shot](https://huggingface.co/tomaarsen/setfit-all-MiniLM-L6-v2-sst2-32-shot) model has followed all steps from this guide to produce the most extensive automatically generated model card.

## Specifying Metadata

Although SetFit can infer a lot of information about your model through its training and configuration, some metadata can often not be (trivially) inferred. For example:

* **language**: The model language, e.g. "en" for English.
* **license**: The model license, e.g. "mit" or "apache-2.0".
* **dataset_name**: The pretty name of a dataset, e.g. "Amazon Counterfactual".
* **dataset_id**: The dataset ID of the dataset, e.g. "dair-ai/emotion".

It is recommended to specify this information to the [SetFitModel](/docs/setfit/main/en/reference/main#setfit.SetFitModel) upon calling `SetFitModel.from_pretrained()`, to allow this information to be included in the model card and its metadata. This can be done using an [SetFitModelCardData](/docs/setfit/main/en/reference/main#setfit.SetFitModelCardData) instance and the `model_card_data` key-word argument, e.g. like so:

```py
from setfit import SetFitModel

model = SetFitModel.from_pretrained(
    "BAAI/bge-small-en-v1.5",
    model_card_data=SetFitModelCardData(
        language="en",
        license="apache-2.0",
        dataset_id="sst2",
        dataset_name="SST2",
    )
)
```

See the [SetFitModelCardData](/docs/setfit/main/en/reference/main#setfit.SetFitModelCardData) documentation for more information that you can specify to be used in the README.

## Labels

If the labels from your training dataset are all integers, then you are recommended to provide your [SetFitModel](/docs/setfit/main/en/reference/main#setfit.SetFitModel) with labels. These labels can then 1) be used in inference and 2) be used in your model card. For example, if your training labels are `0` and `1` for negative and positive, respectively, then you can load your model like so:

```py
model = SetFitModel.from_pretrained(
    "BAAI/bge-small-en-v1.5",
    labels=["negative", "positive"],
    model_card_data=SetFitModelCardData(
        language="en",
        license="apache-2.0",
        dataset_id="sst2",
        dataset_name="SST2",
    )
)
```

When calling [SetFitModel.predict()](/docs/setfit/main/en/reference/main#setfit.SetFitModel.predict), the trained model will now output strings or lists of strings, rather than your integer labels:

```py
model.predict([
    "It's a charming and often affecting journey.",
    "It's slow -- very, very slow.",
    "A sometimes tedious film.",
])
# => ['positive', 'negative', 'negative']
```

Additionally, the model card will include the labels, e.g. it will use the following table:

| Label    | Examples                                                                                                                                                                               |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | 'a tough pill to swallow and ''indignation ''that the typical hollywood disregard for historical truth and realism is at work here '               |
| positive | "a moving experience for people who have n't read the book "'in the best possible senses of both those words ''to serve the work especially well ' |

Rather than this one:

| Label    | Examples                                                                                                                                                                               |
|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 'a tough pill to swallow and ''indignation ''that the typical hollywood disregard for historical truth and realism is at work here '               |
| 1 | "a moving experience for people who have n't read the book "'in the best possible senses of both those words ''to serve the work especially well ' |

And the following table:

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 32                    |
| positive | 32                    |

Rather than this one:

| Label | Training Sample Count |
|:------|:----------------------|
| 0     | 32                    |
| 1     | 32                    |

## Emissions Tracking

The [``codecarbon``](https://github.com/mlco2/codecarbon) Python package can be installed to automatically track carbon emissions during training. This information will be included in the model card, e.g. in a list [like so](https://huggingface.co/tomaarsen/setfit-all-MiniLM-L6-v2-sst2-32-shot#environmental-impact):

Environmental Impact

Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).

- **Carbon Emitted**: 0.003 kg of CO2
- **Hours Used**: 0.072 hours

## Custom Metrics

If you use custom metrics, then these will be included in your model card as well! For example, if you use the following `metric` function:

```py
from setfit import SetFitModel, Trainer, TrainingArguments

...

def compute_metrics(y_pred, y_test):
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    return { 'accuracy': accuracy, 'precision': precision, 'recall': recall, 'f1': f1}

...

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    metric=compute_metrics,
)
trainer.train()

model.save_pretrained("setfit-bge-small-v1.5-sst2-8-shot")
```

Then the final model card will contain your special metrics! For example, the metadata will include e.g.:

```yaml
    metrics:
    - type: accuracy
      value: 0.8061504667764964
      name: Accuracy
    - type: precision
      value: 0.7293729372937293
      name: Precision
    - type: recall
      value: 0.9724972497249725
      name: Recall
    - type: f1
      value: 0.8335690711928335
      name: F1
```

Additionally, the Evaluation section will display your metrics:

Metrics

| Label   | Accuracy | Precision | Recall | F1     |
|:--------|:---------|:----------|:-------|:-------|
| **all** | 0.8062   | 0.7294    | 0.9725 | 0.8336 |

