mluke-large-lite-reward

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Fine-tuning

• this model was trained to classify whether input text comes from "chosen sentence" or "rejected sentence"
• the probability (logits after passing softmax function) in last layer of this model can be used to quantify the preference from user input
• fine-tuned studio-ousia/mluke-large-lite via full parameter tuning using open-preference-v0.3
• trained on bf16 format
• Label 0 stands for rejected sentence
• Label 1 stands for chosen sentence
• Note that this model can handle only 512 tokens in maximum
  • The limitation arises from Luke-based pre-trained model

Metric

• train and validation split

• test split

• confusion matrix when test split

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.0+cu118
• Datasets 2.20.0
• Tokenizers 0.19.1