Should You Mask 15% in Masked Language Modeling?
Paper • 2202.08005 • Published • 1
How to use princeton-nlp/efficient_mlm_m0.50 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="princeton-nlp/efficient_mlm_m0.50") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/efficient_mlm_m0.50")
model = AutoModelForMaskedLM.from_pretrained("princeton-nlp/efficient_mlm_m0.50")This is a model checkpoint for "Should You Mask 15% in Masked Language Modeling" (code). We use pre layer norm, which is not supported by HuggingFace. To use our model, go to our github repo, download our code, and import the RoBERTa class from huggingface/modeling_roberta_prelayernorm.py. For example,
from huggingface.modeling_roberta_prelayernorm import RobertaForMaskedLM, RobertaForSequenceClassification