readme: ---
  language: tr
  license: mit
  ---
  
  # 🤗 + 📚 dbmdz Distilled Turkish BERT model
  
  In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
  Library open sources a (cased) distilled model for Turkish 🎉
  
  # 🇹🇷 DistilBERTurk
  
  DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
  
  DistilBERTurk was trained on 7GB of the original training data that was used
  for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats),
  using the cased version of BERTurk as teacher model.
  
  *DistilBERTurk* was trained with the official Hugging Face implementation from
  [here](https://github.com/huggingface/transformers/tree/master/examples/distillation)
  for 5 days on 4 RTX 2080 TI.
  
  More details about distillation can be found in the
  ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108)
  paper by Sanh et al. (2019).
  
  ## Model weights
  
  Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
  compatible weights are available. If you need access to TensorFlow checkpoints,
  please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository!
  
  | Model                             | Downloads
  | --------------------------------- | ---------------------------------------------------------------------------------------------------------------
  | `dbmdz/distilbert-base-turkish-cased`   | [`config.json`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/vocab.txt)
  
  ## Usage
  
  With Transformers >= 2.3 our DistilBERTurk model can be loaded like:
  
  ```python
  from transformers import AutoModel, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased")
  model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased")
  ```
  
  ## Results
  
  For results on PoS tagging or NER tasks, please refer to
  [this repository](https://github.com/stefan-it/turkish-bert).
  
  For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model.
  
  The overall performance difference between DistilBERTurk and the original
  (teacher) BERTurk model is ~1.18%.
  
  # Huggingface model hub
  
  All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
  
  # Contact (Bugs, Feedback, Contribution and more)
  
  For questions about our BERT models just open an issue
  [here](https://github.com/dbmdz/berts/issues/new) 🤗
  
  # Acknowledgments
  
  Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) 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](https://huggingface.co/) team,
  it is possible to download both cased and uncased models from their S3 storage 🤗
  