ryota39/luke-japanese-base-lite-reward

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

train loss	eval loss	accuracy	recall	precision	f1-score
0.1427	0.2009	9282	0.9383	0.9198	0.9290

• test split

accuracy	recall	precision	f1-score
0.9310	0.9199	0.9408	0.9302

• 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

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.316	1.0	1479	0.2245	0.9127	0.9027	0.9251	0.9138
0.1696	2.0	2958	0.1869	0.9308	0.9234	0.9395	0.9314
0.1427	3.0	4437	0.2009	0.9283	0.9198	0.9384	0.9290

Framework versions

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