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readerbench/
RoBERT-large

Name: RoBERT-large
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Model card for RoBERT-large

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RoBERT-large

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

python

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

# pytorch
from transformers import AutoModel, AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("readerbench/RoBERT-large")
model = AutoModel.from_pretrained("readerbench/RoBERT-large")
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

bibtex

@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}
}