RoBERT-base

RoBERT-base

Pretrained BERT model for Romanian

Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective. It was introduced in this paper. Three BERT models were released: RoBERT-small, RoBERT-base and RoBERT-large, all versions uncased.

Model Weights L H A MLM accuracy NSP accuracy
RoBERT-small 19M 12 256 8 0.5363 0.9687
RoBERT-base 114M 12 768 12 0.6511 0.9802
RoBERT-large 341M 24 1024 24 0.6929 0.9843

All models are available:

How to use

# tensorflow
from transformers import AutoModel, AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-base")
model = TFAutoModel.from_pretrained("readerbench/RoBERT-base")
inputs = tokenizer("exemplu de propoziție", return_tensors="tf")
outputs = model(inputs)

# pytorch
from transformers import AutoModel, AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-base")
model = AutoModel.from_pretrained("readerbench/RoBERT-base")
inputs = tokenizer("exemplu de propoziție", return_tensors="pt")
outputs = model(**inputs)

Training data

The model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.

Corpus Words Sentences Size (GB)
Oscar 1.78B 87M 10.8
RoTex 240M 14M 1.5
RoWiki 50M 2M 0.3
Total 2.07B 103M 12.6

Downstream performance

Sentiment analysis

We report Macro-averaged F1 score (in %)

Model Dev Test
multilingual-BERT 68.96 69.57
XLM-R-base 71.26 71.71
BERT-base-ro 70.49 71.02
RoBERT-small 66.32 66.37
RoBERT-base 70.89 71.61
RoBERT-large 72.48 72.11

Moldavian vs. Romanian Dialect and Cross-dialect Topic identification

We report results on VarDial 2019 Moldavian vs. Romanian Cross-dialect Topic identification Challenge, as Macro-averaged F1 score (in %).

Model Dialect Classification MD to RO RO to MD
2-CNN + SVM 93.40 65.09 75.21
Char+Word SVM 96.20 69.08 81.93
BiGRU 93.30 70.10 80.30
multilingual-BERT 95.34 68.76 78.24
XLM-R-base 96.28 69.93 82.28
BERT-base-ro 96.20 69.93 78.79
RoBERT-small 95.67 69.01 80.40
RoBERT-base 97.39 68.30 81.09
RoBERT-large 97.78 69.91 83.65

Diacritics Restoration

Challenge can be found here. We report results on the official test set, as accuracies in %.

Model word level char level
BiLSTM 99.42 -
CharCNN 98.40 99.65
CharCNN + multilingual-BERT 99.72 99.94
CharCNN + XLM-R-base 99.76 99.95
CharCNN + BERT-base-ro 99.79 99.95
CharCNN + RoBERT-small 99.73 99.94
CharCNN + RoBERT-base 99.78 99.95
CharCNN + RoBERT-large 99.76 99.95

BibTeX entry and citation info

@inproceedings{masala2020robert,
  title={RoBERT--A Romanian BERT Model},
  author={Masala, Mihai and Ruseti, Stefan and Dascalu, Mihai},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={6626--6637},
  year={2020}
}
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