How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="taeminlee/kogpt2")
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taeminlee/kogpt2")
model = AutoModelForCausalLM.from_pretrained("taeminlee/kogpt2", device_map="auto")
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KoGPT2-Transformers

KoGPT2 on Huggingface Transformers

KoGPT2-Transformers

Demo

Example

from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast

model = GPT2LMHeadModel.from_pretrained("taeminlee/kogpt2")
tokenizer = PreTrainedTokenizerFast.from_pretrained("taeminlee/kogpt2")

input_ids = tokenizer.encode("μ•ˆλ…•", add_special_tokens=False, return_tensors="pt")
output_sequences = model.generate(input_ids=input_ids, do_sample=True, max_length=100, num_return_sequences=3)
for generated_sequence in output_sequences:
    generated_sequence = generated_sequence.tolist()
    print("GENERATED SEQUENCE : {0}".format(tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)))
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