RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 10
How to use iarfmoose/roberta-base-bulgarian with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="iarfmoose/roberta-base-bulgarian") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("iarfmoose/roberta-base-bulgarian")
model = AutoModelForMaskedLM.from_pretrained("iarfmoose/roberta-base-bulgarian")The RoBERTa model was originally introduced in this paper. This is a version of RoBERTa-base pretrained on Bulgarian text.
This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
The training data is unfiltered text from the internet and may contain all sorts of biases.
This model was trained on the following data:
The model was pretrained using a masked language-modeling objective with dynamic masking as described here
It was trained for 200k steps. The batch size was limited to 8 due to GPU memory limitations.