from transformers import AutoTokenizer, AutoModelForMaskedLM
from peft import PeftModel

base = AutoModelForMaskedLM.from_pretrained("TextMachineProject/NewsBERT_1800-1920")
tokenizer = AutoTokenizer.from_pretrained("TextMachineProject/NewsBERT_1800-1920")
model = PeftModel.from_pretrained(base, "TextMachineProject/NewsBERT_post_1900_lora_1epoch")

Notes

Period: post-1900
Base model: TextMachineProject/NewsBERT_1800-1920
Method: LoRA (PEFT), target modules: query , value , word_embeddings
LoRA rank: 16, alpha: 32, dropout: 0.05
Task: Masked Language Modelling (15% masking probability)
Sequence length: 128 tokens (sliding window, stride 96)
Epochs: 1
Batch size: 256

This is a 1-epoch checkpoint uploaded for evaluation purposes. Further training is ongoing; updated adapters will be released separately.