๐ค Models & Datasets | ๐ Blog | ๐ Paper
SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples ๐คฏ!
Compared to other few-shot learning methods, SetFit has several unique features:
- ๐ฃ No prompts or verbalisers: Current techniques for few-shot fine-tuning require handcrafted prompts or verbalisers to convert examples into a format that's suitable for the underlying language model. SetFit dispenses with prompts altogether by generating rich embeddings directly from text examples.
- ๐ Fast to train: SetFit doesn't require large-scale models like T0 or GPT-3 to achieve high accuracy. As a result, it is typically an order of magnitude (or more) faster to train and run inference with.
- ๐ Multilingual support: SetFit can be used with any Sentence Transformer on the Hub, which means you can classify text in multiple languages by simply fine-tuning a multilingual checkpoint.
Download and install setfit
by running:
python -m pip install setfit
setfit
is integrated with the Hugging Face Hub and provides two main classes:
SetFitModel
: a wrapper that combines a pretrained body fromsentence_transformers
and a classification head fromscikit-learn
SetFitTrainer
: a helper class that wraps the fine-tuning process of SetFit.
Here is an end-to-end example:
from datasets import load_dataset
from sentence_transformers.losses import CosineSimilarityLoss
from setfit import SetFitModel, SetFitTrainer
# Load a dataset from the Hugging Face Hub
dataset = load_dataset("sst2")
# Simulate the few-shot regime by sampling 8 examples per class
num_classes = 2
train_dataset = dataset["train"].shuffle(seed=42).select(range(8 * num_classes))
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,
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 constrastive 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 ๐คฎ"])
For more examples, check out the notebooks/
folder.
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:
model_init()
: A function that instantiates the model to be used. If provided, each call totrain()
will start from a new instance of the model as given by this function.hp_space()
: A function that defines the hyperparameter search space.
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(trial): # Model head parameters
if trial is not None:
max_iter = trial.suggest_int("max_iter", 50, 300)
solver = trial.suggest_categorical("solver", ["newton-cg", "lbfgs", "liblinear"])
else:
max_iter = 100
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]),
}
Note: In practice, we found num_iterations
to be the most important hyperparameter for the contrastive learning process.
The final 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"},
)
trainer.hyperparameter_search(direction="minimize", hp_space=hp_space, n_trials=4)
We provide scripts to reproduce the results for SetFit and various baselines presented in Table 2 of our paper. Check out the setup and training instructions in the scripts/
directory.
To run the code in this project, first create a Python virtual environment using e.g. Conda:
conda create -n setfit python=3.9 && conda activate setfit
Then install the base requirements with:
python -m pip install -e '.[dev]'
This will install datasets
and packages like black
and isort
that we use to ensure consistent code formatting.
We use black
and isort
to ensure consistent code formatting. After following the installation steps, you can check your code locally by running:
make style && make quality
โโโ LICENSE
โโโ Makefile <- Makefile with commands like `make style` or `make tests`
โโโ README.md <- The top-level README for developers using this project.
โโโ notebooks <- Jupyter notebooks.
โโโ final_results <- Model predictions from the paper
โโโ scripts <- Scripts for training and inference
โโโ setup.cfg <- Configuration file to define package metadata
โโโ setup.py <- Make this project pip installable with `pip install -e`
โโโ src <- Source code for SetFit
โโโ tests <- Unit tests
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}}