# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("LeverageX/scibert-wechsel-korean")
model = AutoModelForMaskedLM.from_pretrained("LeverageX/scibert-wechsel-korean")Quick Links
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Check out the documentation for more information.
scibert-wechsel-korean
Scibert(🇺🇸) converted into Korean(🇰🇷) using WECHSEL technique.
Description
- SciBERT is trained on papers from the corpus of semanticscholar.org. Corpus size is 1.14M papers, 3.1B tokens.
- Wechsel is converting embedding layer's subword tokens from source language to target language.
- SciBERT trained with English language is converted into Korean langauge using Wechsel technique.
- Korean tokenizer is selected with KLUE PLMs' tokenizers due to its similar vocab size(32000) and performance.
Reference
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LeverageX/scibert-wechsel-korean")