Cross-Encoders

SentenceTransformers also supports the option to train Cross-Encoder for sentence pair score and sentence pair classification tasks. For the what Cross-Encoders are and the difference between Cross- and Bi-Encoders, see Cross-Encoders.

Examples

See the following examples how to train Cross-Encoders:

  • training_stsbenchmark.py - Example how to train for Semantic Textual Similarity (STS) on the STS benchmark dataset.

  • training_quora_duplicate_questions.py - Example how to train a Cross-Encoder to predict if two questions are duplicates. Uses Quora Duplicate Questions as training dataset.

  • training_nli.py - Example for a multilabel classification task for Natural Language Inference (NLI) task.

Training CrossEncoders

The CrossEncoder class is a wrapper around Huggingface AutoModelForSequenceClassification, but with some methods to make training and predicting scores a little bit easier. The saved models are 100% compatible with Huggingface and can also be loaded with their classes.

First, you need some sentence pair data. You can either have a continious score, like:

from sentence_transformers import InputExample
train_samples = [
  InputExample(texts=['sentence1', 'sentence2'], label=0.3),
  InputExample(texts=['Another', 'pair'], label=0.8),
]

Or you have distinct classes as in the training_nli.py example:

from sentence_transformers import InputExample
label2int = {"contradiction": 0, "entailment": 1, "neutral": 2}
train_samples = [
  InputExample(texts=['sentence1', 'sentence2'], label=label2int['neutral']),
  InputExample(texts=['Another', 'pair'], label=label2int['entailment']),
]

Then, you define the base model and the number of labels. You can take any Huggingface pre-trained model that is compatible with AutoModel:

model = CrossEncoder('distilroberta-base', num_labels=1)

For binary tasks and tasks with continious scores (like STS), we set num_labels=1. For classification tasks, we set it to the number of labels we have.

We start the training by calling model.fit():

model.fit(train_dataloader=train_dataloader,
          evaluator=evaluator,
          epochs=num_epochs,
          warmup_steps=warmup_steps,
          output_path=model_save_path)