need urgent help for ner deberta training

[u/Fragrant-Courage3548]

hi,
i am trying to train a deberta model for NER detection

this is my first time doing it so i would love any guidance on it.

my current pipeline looks like this,

dapt + lora for pretrianing, hpo with optuna (which consists both the stages of training data), and then a 2 stage finetuning which helps in generalization and then target data.

i am trying to reach a really good score for f1 on my use case (which i want to keep private for now)

i have few questions as well
1) do i need a two stage hpo as well cuase of the 2 stage finetuning
2) is it better if the hpo training set is a subset of the actual training set?

if you think anything can be improved and made better, or you think the pipeline is outright wrong, please mention your reasonings and thoughts :)

ps: lora was used cause of gpu budget constraints

[u/PLBjt]

I'd run Optuna on stage-1 only, then freeze those hyperparameters for stage-2, unless you've got GPU budget to burn. If you HPO across both stages you can accidentally tune to the target domain early and then your "generalization" numbers look fake. Hold out a target-domain slice before any search and never touch it until the final eval. Also watch learning rate and LoRA rank first — those move NER F1 more than most of the fancy knobs.