racai/distilbert-base-romanian-cased

Romanian DistilBERT

This repository contains the uncased Romanian DistilBERT (named Distil-BERT-base-ro in the paper). The teacher model used for distillation is: dumitrescustefan/bert-base-romanian-cased-v1.

The model was introduced in this paper. The adjacent code can be found here.

Usage

from transformers import AutoTokenizer, AutoModel

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained("racai/distilbert-base-romanian-cased")
model = AutoModel.from_pretrained("racai/distilbert-base-romanian-cased")

# tokenize a test sentence
input_ids = tokenizer.encode("Aceasta este o propoziție de test.", add_special_tokens=True, return_tensors="pt")

# run the tokens trough the model
outputs = model(input_ids)

print(outputs)

Model Size

It is 35% smaller than its teacher bert-base-romanian-cased-v1.

Model	Size (MB)	Params (Millions)
bert-base-romanian-cased-v1	477	124
distilbert-base-romanian-cased	312	81

Evaluation

We evaluated the model in comparison with its teacher on 5 Romanian tasks:

• UPOS: Universal Part of Speech (F1-macro)
• XPOS: Extended Part of Speech (F1-macro)
• NER: Named Entity Recognition (F1-macro)
• SAPN: Sentiment Anlaysis - Positive vs Negative (Accuracy)
• SAR: Sentiment Analysis - Rating (F1-macro)
• DI: Dialect identification (F1-macro)
• STS: Semantic Textual Similarity (Pearson)

Model	UPOS	XPOS	NER	SAPN	SAR	DI	STS
bert-base-romanian-cased-v1	98.00	96.46	85.88	98.07	79.61	95.58	80.30
distilbert-base-romanian-cased	97.97	97.08	83.35	98.20	80.51	96.31	80.57

BibTeX entry and citation info

@article{avram2021distilling,
  title={Distilling the Knowledge of Romanian BERTs Using Multiple Teachers},
  author={Andrei-Marius Avram and Darius Catrina and Dumitru-Clementin Cercel and Mihai Dascălu and Traian Rebedea and Vasile Păiş and Dan Tufiş},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.12650}
}