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reranker.train()

Fine-tunes a pre-trained Reranker model on a user-specified domain. No training data is needed, as the model uses synthex.jobs.generate_data() under the hood to generate a synthetic training dataset based on the provided domain.

Both the base Reranker model cross-encoder/mmarco-mMiniLMv2-L12-H384-v1 and previously trained models can be further trained using this method:

  • Fine-tune the base Reranker model: 
    Artifex().reranker.train()
  • Fine-tune a model that was previously trained with Artifex, in order to train it further: 
    Artifex().reranker.load("trained/model/path").train()

Arguments

  • domain
    str

    A string which specifies the domain or area that the model will be specialized in.
  • output_path
    str
    optional

    A string which specifies the path where the output files will be generated. The output files consist of:
    • The training dataset
    • The output model safetensor and configuration files
  • num_samples
    int
    optional
    default: 500

    An integer which specifies the number of datapoints that the synthetic training dataset should consist of, and that the model will be trained on. The maximum number of datapoints you can train your model on depends on whether you are on a free or paid plan.
  • num_epochs
    str
    optional
    default: 3

    An integer which specifies the number of epochs to train the model for.
from artifex import Artifex

reranker = Artifex().reranker

reranker.train(
    domain="e-commerce product search",
)
None