# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Andrija/SRoBERTa")
model = AutoModelForMaskedLM.from_pretrained("Andrija/SRoBERTa", device_map="auto")Quick Links
Transformer language model for Croatian and Serbian
Trained on 0.7GB dataset Croatian and Serbian language for one epoch. Dataset from Leipzig Corpora.
Information of dataset
| Model | #params | Arch. | Training data |
|---|---|---|---|
Andrija/SRoBERTa |
120M | First | Leipzig Corpus (0.7 GB of text) |
How to use in code
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Andrija/SRoBERTa")
model = AutoModelForMaskedLM.from_pretrained("Andrija/SRoBERTa")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Andrija/SRoBERTa")