Hyperparameter Optimization¶

The SentenceTransformerTrainer supports hyperparameter optimization using transformers, which in turn supports four hyperparameter search backends: optuna, sigopt, raytune, and wandb. You should install your backend of choice before using it:

pip install optuna/sigopt/wandb/ray[tune]

On this page, we’ll show you how to use the hyperparameter optimization feature with the optuna backend. The other backends are similar to use, but you should refer to their respective documentation or the transformers HPO documentation for more information.

HPO Components¶

The hyperparameter optimization process consists of the following components:

Specify ranges for hyperparameter values. Initialize a SentenceTransformer model for a trial. Initialize a loss function given a model. Determines the value to be minimized or maximized.

Hyperparameter Search Space¶

The hyperparameter search space is defined by a function that returns a dictionary of hyperparameters and their respective search spaces. Here’s an example using optuna of a search space function that defines the hyperparameters for a SentenceTransformer model:

def hpo_search_space(trial):
    return {
        "num_train_epochs": trial.suggest_int("num_train_epochs", 1, 2),
        "per_device_train_batch_size": trial.suggest_int("per_device_train_batch_size", 32, 128),
        "warmup_ratio": trial.suggest_float("warmup_ratio", 0, 0.3),
        "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
    }

Model Initialization¶

The model initialization function is a function that takes the hyperparameters of the current “trial” as input and returns a SentenceTransformer model. Generally, this function is quite simple. Here’s an example of a model initialization function:

def hpo_model_init(trial):
    return SentenceTransformer("distilbert-base-uncased")

Loss Initialization¶

The loss initialization function is a function that takes the model initialized for the current trial and returns a loss function. Here’s an example of a loss initialization function:

def hpo_loss_init(model):
    return losses.CosineSimilarityLoss(model)

Compute Objective¶

The compute objective function is a function that takes the evaluation metrics and returns the float value to be minimized or maximized. Here’s an example of a compute objective function:

def hpo_compute_objective(metrics):
    return metrics["eval_sts-dev_spearman_cosine"]

Putting It All Together¶

You can perform HPO on any regular training loop, the only difference being that you don’t call SentenceTransformerTrainer.train, but SentenceTransformerTrainer.hyperparameter_search instead. Here’s an example of how to put it all together:

from sentence_transformers import losses
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, SentenceTransformerTrainingArguments
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator, SimilarityFunction
from sentence_transformers.training_args import BatchSamplers
from datasets import load_dataset

# 1. Load the AllNLI dataset: https://huggingface.co/datasets/sentence-transformers/all-nli, only 10k train and 1k dev
train_dataset = load_dataset("sentence-transformers/all-nli", "triplet", split="train[:10000]")
eval_dataset = load_dataset("sentence-transformers/all-nli", "triplet", split="dev[:1000]")

# 2. Create an evaluator to perform useful HPO
stsb_eval_dataset = load_dataset("sentence-transformers/stsb", split="validation")
dev_evaluator = EmbeddingSimilarityEvaluator(
    sentences1=stsb_eval_dataset["sentence1"],
    sentences2=stsb_eval_dataset["sentence2"],
    scores=stsb_eval_dataset["score"],
    main_similarity=SimilarityFunction.COSINE,
    name="sts-dev",
)

# 3. Define the Hyperparameter Search Space
def hpo_search_space(trial):
    return {
        "num_train_epochs": trial.suggest_int("num_train_epochs", 1, 2),
        "per_device_train_batch_size": trial.suggest_int("per_device_train_batch_size", 32, 128),
        "warmup_ratio": trial.suggest_float("warmup_ratio", 0, 0.3),
        "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
    }

# 4. Define the Model Initialization
def hpo_model_init(trial):
    return SentenceTransformer("distilbert-base-uncased")

# 5. Define the Loss Initialization
def hpo_loss_init(model):
    return losses.MultipleNegativesRankingLoss(model)

# 6. Define the Objective Function
def hpo_compute_objective(metrics):
    """
    Valid keys are: 'eval_loss', 'eval_sts-dev_pearson_cosine', 'eval_sts-dev_spearman_cosine',
    'eval_sts-dev_pearson_manhattan', 'eval_sts-dev_spearman_manhattan', 'eval_sts-dev_pearson_euclidean',
    'eval_sts-dev_spearman_euclidean', 'eval_sts-dev_pearson_dot', 'eval_sts-dev_spearman_dot',
    'eval_sts-dev_pearson_max', 'eval_sts-dev_spearman_max', 'eval_runtime', 'eval_samples_per_second',
    'eval_steps_per_second', 'epoch'

    due to the evaluator that we're using.
    """
    return metrics["eval_sts-dev_spearman_cosine"]

# 7. Define the training arguments
args = SentenceTransformerTrainingArguments(
    # Required parameter:
    output_dir="checkpoints",
    # Optional training parameters:
    # max_steps=10000, # We might want to limit the number of steps for HPO
    fp16=True,  # Set to False if you get an error that your GPU can't run on FP16
    bf16=False,  # Set to True if you have a GPU that supports BF16
    batch_sampler=BatchSamplers.NO_DUPLICATES,  # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
    # Optional tracking/debugging parameters:
    eval_strategy="no", # We don't need to evaluate/save during HPO
    save_strategy="no",
    logging_steps=10,
    run_name="hpo",  # Will be used in W&B if `wandb` is installed
)

# 8. Create the trainer with model_init rather than model
trainer = SentenceTransformerTrainer(
    model=None,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    evaluator=dev_evaluator,
    model_init=hpo_model_init,
    loss=hpo_loss_init,
)

# 9. Perform the HPO
best_trial = trainer.hyperparameter_search(
    hp_space=hpo_search_space,
    compute_objective=hpo_compute_objective,
    n_trials=20,
    direction="maximize",
    backend="optuna",
)
print(best_trial)

[I 2024-05-17 15:10:47,844] Trial 0 finished with value: 0.7889856589698055 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 123, 'warmup_ratio': 0.07380948785410107, 'learning_rate': 2.686331417509812e-06}. Best is trial 0 with value: 0.7889856589698055.
[I 2024-05-17 15:12:13,283] Trial 1 finished with value: 0.7927780672090986 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 69, 'warmup_ratio': 0.2927897848007451, 'learning_rate': 5.885372118095137e-06}. Best is trial 1 with value: 0.7927780672090986.
[I 2024-05-17 15:12:43,896] Trial 2 finished with value: 0.7684829743509601 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 114, 'warmup_ratio': 0.0739429232666916, 'learning_rate': 7.344415188959276e-05}. Best is trial 1 with value: 0.7927780672090986.
[I 2024-05-17 15:14:49,730] Trial 3 finished with value: 0.7873032743147989 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 43, 'warmup_ratio': 0.15184370143796674, 'learning_rate': 9.703232080395476e-06}. Best is trial 1 with value: 0.7927780672090986.
[I 2024-05-17 15:15:39,597] Trial 4 finished with value: 0.7759251781929949 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 127, 'warmup_ratio': 0.263946220093495, 'learning_rate': 1.231454337152625e-06}. Best is trial 1 with value: 0.7927780672090986.
[I 2024-05-17 15:17:02,191] Trial 5 finished with value: 0.7964580509886684 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 34, 'warmup_ratio': 0.2276865359631089, 'learning_rate': 7.889007438884571e-06}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:18:55,559] Trial 6 finished with value: 0.7901878917859169 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 48, 'warmup_ratio': 0.23228838664572948, 'learning_rate': 2.883013292682523e-06}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:20:27,027] Trial 7 finished with value: 0.7935671067660925 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 62, 'warmup_ratio': 0.22061123927198237, 'learning_rate': 2.95413457610349e-06}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:22:23,147] Trial 8 finished with value: 0.7848123114933252 and parameters: {'num_train_epochs': 2, 'per_device_train_batch_size': 45, 'warmup_ratio': 0.23071701022961139, 'learning_rate': 9.793681667449783e-06}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:22:52,826] Trial 9 finished with value: 0.7909708416168918 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 121, 'warmup_ratio': 0.22440506724181647, 'learning_rate': 4.0744671365843346e-05}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:23:30,395] Trial 10 finished with value: 0.7928991732385567 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 89, 'warmup_ratio': 0.14607293301068847, 'learning_rate': 2.5557492055039498e-05}. Best is trial 5 with value: 0.7964580509886684.
[I 2024-05-17 15:24:18,024] Trial 11 finished with value: 0.7991870087507459 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 66, 'warmup_ratio': 0.16886154348739527, 'learning_rate': 3.705926066938032e-06}. Best is trial 11 with value: 0.7991870087507459.
[I 2024-05-17 15:25:44,198] Trial 12 finished with value: 0.7923304174306207 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 33, 'warmup_ratio': 0.15953772535423974, 'learning_rate': 1.8076298025704224e-05}. Best is trial 11 with value: 0.7991870087507459.
[I 2024-05-17 15:26:20,739] Trial 13 finished with value: 0.8020260244040395 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 90, 'warmup_ratio': 0.18105202625281253, 'learning_rate': 5.513908793512551e-06}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:26:57,783] Trial 14 finished with value: 0.7571110256860063 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 95, 'warmup_ratio': 0.00122391151793258, 'learning_rate': 1.0432486633629492e-06}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:27:32,581] Trial 15 finished with value: 0.8009013936824717 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 101, 'warmup_ratio': 0.1761274711346081, 'learning_rate': 4.5918293464430035e-06}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:28:05,850] Trial 16 finished with value: 0.8017668050806169 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 103, 'warmup_ratio': 0.10766501647726355, 'learning_rate': 5.0309795522333e-06}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:28:37,393] Trial 17 finished with value: 0.7769412380909586 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 108, 'warmup_ratio': 0.1036610178950246, 'learning_rate': 1.7747598626081271e-06}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:29:19,340] Trial 18 finished with value: 0.8011921300048339 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 80, 'warmup_ratio': 0.117014165550441, 'learning_rate': 1.238558867958792e-05}. Best is trial 13 with value: 0.8020260244040395.
[I 2024-05-17 15:29:59,508] Trial 19 finished with value: 0.8027501854704168 and parameters: {'num_train_epochs': 1, 'per_device_train_batch_size': 84, 'warmup_ratio': 0.014601112207929548, 'learning_rate': 5.627813947769514e-06}. Best is trial 19 with value: 0.8027501854704168.

BestRun(run_id='19', objective=0.8027501854704168, hyperparameters={'num_train_epochs': 1, 'per_device_train_batch_size': 84, 'warmup_ratio': 0.014601112207929548, 'learning_rate': 5.627813947769514e-06}, run_summary=None)

As you can see, the strongest hyperparameters reached 0.802 Spearman correlation on the STS (dev) benchmark. For context, training with the default training arguments (per_device_train_batch_size=8, learning_rate=5e-5) results in 0.736, and hyperparameters chosen based on experience (per_device_train_batch_size=64, learning_rate=2e-5) results in 0.783 Spearman correlation. Consequently, HPO proved quite effective here in improving the model performance.

Example Scripts¶

hpo_nli.py - An example script that performs hyperparameter optimization on the AllNLI dataset.

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