ryota39/luke-japanese-base-lite-reward

工事中

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

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