Introduction of electra-base-turkish-mc4-cased-generator
Model Details of electra-base-turkish-mc4-cased-generator
🇹🇷 Turkish ELECTRA model
We present community-driven BERT, DistilBERT, ELECTRA and ConvBERT models for Turkish 🎉
Some datasets used for pretraining and evaluation are contributed from the
awesome Turkish NLP community, as well as the decision for the BERT model name: BERTurk.
We've also trained an ELECTRA (cased) model on the recently released Turkish part of the
multiligual C4 (mC4) corpus
from the AI2 team.
After filtering documents with a broken encoding, the training corpus has a size of 242GB resulting
in 31,240,963,926 tokens.
We used the original 32k vocab (instead of creating a new one).
mC4 ELECTRA
In addition to the ELEC
TR
A base model, we also trained an ELECTRA model on the Turkish part of the mC4 corpus. We use a
sequence length of 512 over the full training time and train the model for 1M steps on a v3-32 TPU.
Model usage
All trained models can be used from the
DBMDZ
Hugging Face
model hub page
using their model name.
Example usage with 🤗/Transformers:
tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator")
model = AutoModel.from_pretrained("dbmdz/electra-base-turkish-mc4-cased-generator")
Citation
You can use the following BibTeX entry for citation:
@software{stefan_schweter_2020_3770924,
author = {Stefan Schweter},
title = {BERTurk - BERT models for Turkish},
month = apr,
year = 2020,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.3770924},
url = {https://doi.org/10.5281/zenodo.3770924}
}
Acknowledgments
Thanks to
Kemal Oflazer
for providing us
additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
us the Turkish NER dataset for evaluation.
We would like to thank
Merve Noyan
for the
awesome logo!
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
Thanks for providing access to the TFRC ❤️
Runs of dbmdz electra-base-turkish-mc4-cased-generator on huggingface.co
3
Total runs
0
24-hour runs
0
3-day runs
-45
7-day runs
-53
30-day runs
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