模型:

racai/distilbert-base-romanian-uncased

中文

Romanian DistilBERT

This repository contains the uncased Romanian DistilBERT (named Distil-RoBERT-base in the paper). The teacher model used for distillation is: readerbench/RoBERT-base .

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-uncased")
model = AutoModel.from_pretrained("racai/distilbert-base-romanian-uncased")

# 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 RoBERT-base .

Model Size (MB) Params (Millions)
RoBERT-base 441 114
distilbert-base-romanian-cased 282 72

Evaluation

We evaluated the model in comparison with the RoBERT-base 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
RoBERT-base 98.02 97.15 85.14 98.30 79.40 96.07 81.18
distilbert-base-romanian-uncased 97.12 95.79 83.11 98.01 79.58 96.11 79.80

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}
}