# Load model directly
from transformers import AutoModelForMaskedLM
model = AutoModelForMaskedLM.from_pretrained("WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True, device_map="auto")Quick Links
WesScivetti/GPT-BERT_Random_Seed2
Custom GPT-BERT checkpoint from the NPN filtered-corpus training experiments.
This repository includes the architecture code required by Transformers. Example:
from transformers import AutoModelForMaskedLM, AutoTokenizer
repo_id = "WesScivetti/GPT-BERT_Random_Seed2"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True)
model.eval()
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="WesScivetti/GPT-BERT_Random_Seed2", trust_remote_code=True)