Quickstart

Usage

The examples below provide a quick overview on the various features supported in setfit. For more examples, check out the notebooks folder.

Training a SetFit model

setfit is integrated with the Hugging Face Hub and provides two main classes:

Here is an end-to-end example using a classification head from scikit-learn:

from datasets import load_dataset
from sentence_transformers.losses import CosineSimilarityLoss

from setfit import SetFitModel, SetFitTrainer, sample_dataset


# Load a dataset from the Hugging Face Hub
dataset = load_dataset("sst2")

# Simulate the few-shot regime by sampling 8 examples per class
train_dataset = sample_dataset(dataset["train"], label_column="label", num_samples=8)
eval_dataset = dataset["validation"]

# Load a SetFit model from Hub
model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")

# Create trainer
trainer = SetFitTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss_class=CosineSimilarityLoss,
    metric="accuracy",
    batch_size=16,
    num_iterations=20, # The number of text pairs to generate for contrastive learning
    num_epochs=1, # The number of epochs to use for contrastive learning
    column_mapping={"sentence": "text", "label": "label"} # Map dataset columns to text/label expected by trainer
)

# Train and evaluate
trainer.train()
metrics = trainer.evaluate()

# Push model to the Hub
trainer.push_to_hub("my-awesome-setfit-model")

# Download from Hub and run inference
model = SetFitModel.from_pretrained("lewtun/my-awesome-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])

Here is an end-to-end example using SetFitHead:

from datasets import load_dataset
from sentence_transformers.losses import CosineSimilarityLoss

from setfit import SetFitModel, SetFitTrainer, sample_dataset


# Load a dataset from the Hugging Face Hub
dataset = load_dataset("sst2")

# Simulate the few-shot regime by sampling 8 examples per class
train_dataset = sample_dataset(dataset["train"], label_column="label", num_samples=8)
eval_dataset = dataset["validation"]

# Load a SetFit model from Hub
model = SetFitModel.from_pretrained(
    "sentence-transformers/paraphrase-mpnet-base-v2",
    use_differentiable_head=True,
    head_params={"out_features": num_classes},
)

# Create trainer
trainer = SetFitTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss_class=CosineSimilarityLoss,
    metric="accuracy",
    batch_size=16,
    num_iterations=20, # The number of text pairs to generate for contrastive learning
    num_epochs=1, # The number of epochs to use for contrastive learning
    column_mapping={"sentence": "text", "label": "label"} # Map dataset columns to text/label expected by trainer
)

# Train and evaluate
trainer.freeze() # Freeze the head
trainer.train() # Train only the body

# Unfreeze the head and freeze the body -> head-only training
trainer.unfreeze(keep_body_frozen=True)
# or
# Unfreeze the head and unfreeze the body -> end-to-end training
trainer.unfreeze(keep_body_frozen=False)

trainer.train(
    num_epochs=25, # The number of epochs to train the head or the whole model (body and head)
    batch_size=16,
    body_learning_rate=1e-5, # The body's learning rate
    learning_rate=1e-2, # The head's learning rate
    l2_weight=0.0, # Weight decay on **both** the body and head. If `None`, will use 0.01.
)
metrics = trainer.evaluate()

# Push model to the Hub
trainer.push_to_hub("my-awesome-setfit-model")

# Download from Hub and run inference
model = SetFitModel.from_pretrained("lewtun/my-awesome-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])

Based on our experiments, SetFitHead can achieve similar performance as using a scikit-learn head. We use AdamW as the optimizer and scale down learning rates by 0.5 every 5 epochs. For more details about the experiments, please check out here. We recommend using a large learning rate (e.g. 1e-2) for SetFitHead and a small learning rate (e.g. 1e-5) for the body in your first attempt.

Training on multilabel datasets

To train SetFit models on multilabel datasets, specify the multi_target_strategy argument when loading the pretrained model:

Example using a classification head from scikit-learn: 

from setfit import SetFitModel

model = SetFitModel.from_pretrained(
    model_id,
    multi_target_strategy="one-vs-rest",
)

This will initialise a multilabel classification head from sklearn - the following options are available for multi_target_strategy:

From here, you can instantiate a SetFitTrainer using the same example above, and train it as usual.

Example using the differentiable SetFitHead: 

from setfit import SetFitModel

model = SetFitModel.from_pretrained(
    model_id,
    multi_target_strategy="one-vs-rest"
    use_differentiable_head=True,
    head_params={"out_features": num_classes},
)

Note: If you use the differentiable SetFitHead classifier head, it will automatically use BCEWithLogitsLoss for training. The prediction involves a sigmoid after which probabilities are rounded to 1 or 0. Furthermore, the "one-vs-rest" and "multi-output" multi-target strategies are equivalent for the differentiable SetFitHead.

Zero-shot text classification

SetFit can also be applied to scenarios where no labels are available. To do so, create a synthetic dataset of training examples:

from datasets import Dataset
from setfit import get_templated_dataset

candidate_labels = ["negative", "positive"]
train_dataset = get_templated_dataset(candidate_labels=candidate_labels, sample_size=8)

This will create examples of the form "This sentence is {}", where the {} is filled in with one of the candidate labels. From here you can train a SetFit model as usual:

from setfit import SetFitModel, SetFitTrainer

model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")
trainer = SetFitTrainer(
    model=model,
    train_dataset=train_dataset
)
trainer.train()

We find this approach typically outperforms the zero-shot pipeline in 🤗 Transformers (based on MNLI with Bart), while being 5x faster to generate predictions with.

Running hyperparameter search

SetFitTrainer provides a hyperparameter_search() method that you can use to find good hyperparameters for your data. To use this feature, first install the optuna backend:

python -m pip install setfit[optuna]

To use this method, you need to define two functions:

Here is an example of a model_init() function that we’ll use to scan over the hyperparameters associated with the classification head in SetFitModel:

from setfit import SetFitModel

def model_init(params):
    params = params or {}
    max_iter = params.get("max_iter", 100)
    solver = params.get("solver", "liblinear")
    params = {
        "head_params": {
            "max_iter": max_iter,
            "solver": solver,
        }
    }
    return SetFitModel.from_pretrained("sentence-transformers/paraphrase-albert-small-v2", **params)

Similarly, to scan over hyperparameters associated with the SetFit training process, we can define a hp_space() function as follows:

def hp_space(trial):  # Training parameters
    return {
        "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
        "num_epochs": trial.suggest_int("num_epochs", 1, 5),
        "batch_size": trial.suggest_categorical("batch_size", [4, 8, 16, 32, 64]),
        "seed": trial.suggest_int("seed", 1, 40),
        "num_iterations": trial.suggest_categorical("num_iterations", [5, 10, 20]),
        "max_iter": trial.suggest_int("max_iter", 50, 300),
        "solver": trial.suggest_categorical("solver", ["newton-cg", "lbfgs", "liblinear"]),
    }

Note: In practice, we found num_iterations to be the most important hyperparameter for the contrastive learning process.

The next step is to instantiate a SetFitTrainer and call hyperparameter_search():

from datasets import Dataset
from setfit import SetFitTrainer

dataset = Dataset.from_dict(
            {"text_new": ["a", "b", "c"], "label_new": [0, 1, 2], "extra_column": ["d", "e", "f"]}
        )

trainer = SetFitTrainer(
    train_dataset=dataset,
    eval_dataset=dataset,
    model_init=model_init,
    column_mapping={"text_new": "text", "label_new": "label"},
)
best_run = trainer.hyperparameter_search(direction="maximize", hp_space=hp_space, n_trials=20)

Finally, you can apply the hyperparameters you found to the trainer, and lock in the optimal model, before training for a final time.

trainer.apply_hyperparameters(best_run.hyperparameters, final_model=True)
trainer.train()

Compressing a SetFit model with knowledge distillation

If you have access to unlabeled data, you can use knowledge distillation to compress a trained SetFit model into a smaller version. The result is a model that can run inference much faster, with little to no drop in accuracy. Here’s an end-to-end example (see our paper for more details):

from datasets import load_dataset
from sentence_transformers.losses import CosineSimilarityLoss

from setfit import SetFitModel, SetFitTrainer, DistillationSetFitTrainer, sample_dataset

# Load a dataset from the Hugging Face Hub
dataset = load_dataset("ag_news")

# Create a sample few-shot dataset to train the teacher model
train_dataset_teacher = sample_dataset(dataset["train"], label_column="label", num_samples=16)
# Create a dataset of unlabeled examples to train the student
train_dataset_student = dataset["train"].shuffle(seed=0).select(range(500))
# Dataset for evaluation
eval_dataset = dataset["test"]

# Load teacher model
teacher_model = SetFitModel.from_pretrained(
    "sentence-transformers/paraphrase-mpnet-base-v2"
)

# Create trainer for teacher model
teacher_trainer = SetFitTrainer(
    model=teacher_model,
    train_dataset=train_dataset_teacher,
    eval_dataset=eval_dataset,
    loss_class=CosineSimilarityLoss,
)

# Train teacher model
teacher_trainer.train()

# Load small student model
student_model = SetFitModel.from_pretrained("paraphrase-MiniLM-L3-v2")

# Create trainer for knowledge distillation
student_trainer = DistillationSetFitTrainer(
    teacher_model=teacher_model,
    train_dataset=train_dataset_student,
    student_model=student_model,
    eval_dataset=eval_dataset,
    loss_class=CosineSimilarityLoss,
    metric="accuracy",
    batch_size=16,
    num_iterations=20,
    num_epochs=1,
)

# Train student with knowledge distillation
student_trainer.train()