In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased ELECTRA base model for Turkish 🎉
We release a base ELECTRA model for Turkish, that was trained on the same data as BERTurk.
ELECTRA is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a GAN.
The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer.
The final training corpus has a size of 35GB and 44,04,976,662 tokens.
Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model on a TPU v3-8 for 1M steps.
Transformers compatible weights for both PyTorch and TensorFlow are available.
With Transformers >= 2.8 our ELECTRA base cased model can be loaded like:
from transformers import AutoModelWithLMHead, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator") model = AutoModelWithLMHead.from_pretrained("dbmdz/electra-base-turkish-cased-discriminator")
For results on PoS tagging or NER tasks, please refer to this repository.
All models are available on the Huggingface model hub.
For questions about our ELECTRA models just open an issue here 🤗
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.
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️
Thanks to the generous support from the Hugging Face team, it is possible to download both cased and uncased models from their S3 storage 🤗
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