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This release consists of a major refactor that overhauls the reranker a.k.a. Cross Encoder training approach (introducing multi-gpu training, bf16, loss logging, callbacks, and much more), including all new Training Overview, Loss Overview, API Reference docs, training examples and more!

Install this version with

:::bash
# Training + Inference
pip install sentence-transformers[train]==4.0.1

# Inference only, use one of:
pip install sentence-transformers==4.0.1
pip install sentence-transformers[onnx-gpu]==4.0.1
pip install sentence-transformers[onnx]==4.0.1
pip install sentence-transformers[openvino]==4.0.1

[!TIP] My Training and Finetuning Reranker Models with Sentence Transformers v4 blogpost is an excellent place to learn 1) why finetuning rerankers makes sense and 2) how you can do it, too!

Reranker (Cross Encoder) training refactor (#3222)

The v4.0 release centers around this huge modernization of the training approach for CrossEncoder models, following v3.0 which introduced the same for SentenceTransformer models. Whereas training before v4.0 used to be all about InputExample, DataLoader and model.fit, the new training approach relies on 5 components. You can learn more about these components in our Training and Finetuning Embedding Models with Sentence Transformers v4 blogpost. Additionally, you can read the new Training Overview, check out the Training Examples, or read this summary:

  1. Dataset A training Dataset or DatasetDict. This class is much more suited for sharing & efficient modifications than lists/DataLoaders of InputExample instances. A Dataset can contain multiple text columns that will be fed in order to the corresponding loss function. So, if the loss expects (anchor, positive, negative) triplets, then your dataset should also have 3 columns. The names of these columns are irrelevant. If there is a "label" or "score" column, it is treated separately, and used as the labels during training. A DatasetDict can be used to train with multiple datasets at once, e.g.: python DatasetDict({ natural_questions: Dataset({ features: ['anchor', 'positive'], num_rows: 392702 }) gooaq: Dataset({ features: ['anchor', 'positive', 'negative'], num_rows: 549367 }) stsb: Dataset({ features: ['sentence1', 'sentence2', 'label'], num_rows: 5749 }) }) When a DatasetDict is used, the loss parameter to the CrossEncoderTrainer must also be a dictionary with these dataset keys, e.g.: python { 'natural_questions': CachedMultipleNegativesRankingLoss(...), 'gooaq': CachedMultipleNegativesRankingLoss(...), 'stsb': BinaryCrossEntropyLoss(...), }
  2. Loss Function A loss function, or a dictionary of loss functions like described above.
  3. Training Arguments A CrossEncoderTrainingArguments instance, subclass of a TrainingArguments instance. This powerful class controls the specific details of the training.
  4. Evaluator An optional SentenceEvaluator instance. Unlike before, models can now be evaluated both on an evaluation dataset with some loss function and/or a SentenceEvaluator instance.
  5. Trainer The new CrossEncoderTrainer instance based on the transformers Trainer. This instance can be initialized with a CrossEncoder model, a CrossEncoderTrainingArguments class, a SentenceEvaluator, a training and evaluation Dataset/DatasetDict and a loss function/dict of loss functions. Most of these parameters are optional. Once provided, all you have to do is call trainer.train().

Some of the major features that are now implemented include: * MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP)) * bf16 training support * Loss logging * Evaluation datasets + evaluation loss * Improved callback support (built-in via Weights and Biases, TensorBoard, CodeCarbon, etc., as well as custom callbacks) * Gradient checkpointing * Gradient accumulation * Improved model card generation * Warmup ratio * Pushing to the Hugging Face Hub on every model checkpoint * Resuming from a training checkpoint * Hyperparameter Optimization

This script is a minimal example (no evaluator, no training arguments) of training mpnet-base on a part of the sentence-transformers/hotpotqa dataset using BinaryCrossEntropyLoss:

:::python
from datasets import load_dataset
from sentence_transformers import CrossEncoder, CrossEncoderTrainer
from sentence_transformers.cross_encoder.losses import BinaryCrossEntropyLoss

# 1. Define the model. Either from scratch of by loading a pre-trained model
model = CrossEncoder("microsoft/mpnet-base")

# 2. Load a dataset to finetune on
dataset = load_dataset("sentence-transformers/hotpotqa", "triplet", split="train")

def triplet_to_labeled_pair(batch):
    anchors = batch["anchor"]
    positives = batch["positive"]
    negatives = batch["negative"]
    return {
        "sentence_A": anchors * 2,
        "sentence_B": positives + negatives,
        "labels": [1] * len(positives) + [0] * len(negatives),
    }

dataset = dataset.map(triplet_to_labeled_pair, batched=True, remove_columns=dataset.column_names)
train_dataset = dataset.select(range(10_000))
eval_dataset = dataset.select(range(10_000, 11_000))

# 3. Define a loss function
loss = BinaryCrossEntropyLoss(model)

# 4. Create a trainer & train
trainer = CrossEncoderTrainer(
    model=model,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=loss,
)
trainer.train()

# 5. Save the trained model
model.save_pretrained("models/mpnet-base-hotpotqa")
# model.push_to_hub("mpnet-base-hotpotqa")

Additionally, trained models now automatically produce extensive model cards. Each of the following models were trained using some script from the Training Examples, and the model cards were not edited manually whatsoever: * tomaarsen/reranker-MiniLM-L12-gooaq-bce * tomaarsen/reranker-msmarco-MiniLM-L12-H384-uncased-lambdaloss * tomaarsen/reranker-distilroberta-base-nli

Prior to the Sentence Transformer v4 release, all reranker models would be trained using the CrossEncoder.fit method. Rather than deprecating this method, starting from v4.0, this method will use the CrossEncoderTrainer behind the scenes. This means that your old training code should still work, and should even be upgraded with the new features such as multi-gpu training, loss logging, etc. That said, the new training approach is much more powerful, so it is recommended to write new training scripts using the new approach.

To help you out, all of the Cross Encoder (a.k.a. reranker) training scripts were updated to use the new Trainer-based approach.

Is finetuning worth it?

Finetuning reranker models on your data is very valuable. Consider for example these 2 models that I finetuned on 100k samples from the GooAQ dataset in 30 minutes and 1 hour, respectively. After finetuning, my models heavily outperformed general-purpose reranker models, even though GooAQ is a very generic dataset/domain!

Model size vs NDCG for Rerankers on GooAQ

Read my Training and Finetuning Reranker Models with Sentence Transformers v4 blogpost for many more details on these models and how they were trained.

Resources:

Refactor Stats

Small Features

  • Introduce show_progress_bar for the InformationRetrievalEvaluator (#3227)
  • Replace SubsetRandomSampler with RandomSampler in the default batch sampler, should result in reduced memory usage and increased training speed! (#3261)
  • Allow resuming from checkpoint when training with the deprecated SentenceTransformer.fit (#3269)
  • Allow truncation and setting model.max_seq_length for CLIP models (#2969)

Bug Fixes

  • Fixed MatryoshkaLoss with n_dims_per_step and an unsorted matryoshka_dims crashing (#3203)
  • Fixed GISTEmbedLoss failing with some base models whose tokenizers don't have the vocab attribute (#3219, [#3226])
  • Fixed support of Asym-based SentenceTransformer models (#3220, [#3244])
  • Fixed some evaluator outputs not being converted to a Python float, i.e. staying as numpy or torch (#3277)

Examples

  • Improved TSDAE examples (#3263, [#3265])

Note

The v4.0.0 version did not include the model_card_template.md in the package, this has been resolved in v4.0.1 via ba1260d58c8804e97989e5af06ac90a0f4be8594.

What's Changed

New Contributors

A special shoutout to @milistu for contributing the LambdaLoss & ListNetLoss and @yjoonjang for contributing the ListMLELoss, PListMLELoss, and RankNetLoss. Much appreciated, you really helped improve this release!

Full Changelog: https://github.com/UKPLab/sentence-transformers/compare/v3.4.1...v4.0.1

Source: README.md, updated 2025-03-26