metadata
{}
The L2AI-dictionary model is fine-tuned checkpoint of klue/bert-base for multiple choice, specifically for selecting the best dictionary definition of a given word in a sentence. Below is an example usage:
import numpy as np
import torch
from transformers import AutoModelForMultipleChoice, AutoTokenizer
model_name = "JesseStover/L2AI-dictionary-klue-bert-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMultipleChoice.from_pretrained(model_name)
model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
prompts = "\"κ°μμ§λ λ½μ‘λ½μ‘νλ€.\"μ μλ \"κ°μμ§\"μ μ μλ "
candidates = [
"\"(λͺ
μ¬) κ°μ μλΌ\"μμ.",
"\"(λͺ
μ¬) λΆλͺ¨λ ν μλ²μ§, ν λ¨Έλκ° μμμ΄λ μμ£Όλ₯Ό κ·μ¬μνλ©΄μ λΆλ₯΄λ λ§\"μ΄μμ."
]
inputs = tokenizer(
[[prompt, candidate] for candidate in candidates],
return_tensors="pt",
padding=True
)
labels = torch.tensor(0).unsqueeze(0)
with torch.no_grad():
outputs = model(
**{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels
)
print({i: float(x) for i, x in enumerate(outputs.logits.softmax(1)[0])})
Training data was procured under Creative Commons CC BY-SA 2.0 KR DEED from the National Institute of Korean Language's Basic Korean Dictionary and Standard Korean Dictionary.