# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("procesaur/gpt2-srlat")
model = AutoModelForCausalLM.from_pretrained("procesaur/gpt2-srlat")Quick Links
Model is developed in support of the University of Belgrade doctoral dissertation "Composite pseudogrammars based on parallel language models of Serbian" by Mihailo Škorić.
This small gpt-2 model was trained on several corpora for Serbian, including "The corpus of Contemporary Serbian", SrpELTeC and WikiKorpus by JeRTeh – Society for Language Resources and Technologies.
This model is purely experimental! For actual models for Serbian see GPT2-ORAO and GPT2-VRABAC
If you use this model for your reseach please cite: https://doi.org/10.3390/math11224660
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="procesaur/gpt2-srlat")