readme: ---
  language: tr
  license: mit
  ---
  
  # 🤗 + 📚 dbmdz Turkish ConvBERT model
  
  In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
  Library open sources a cased ConvBERT model for Turkish 🎉
  
  # 🇹🇷 ConvBERTurk
  
  ConvBERTurk is a community-driven cased ConvBERT model for Turkish.
  
  In addition to the BERT and ELECTRA based models, we also trained a ConvBERT model. The ConvBERT architecture is presented
  in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper.
  
  We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128
  sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-32 TPU.
  
  ## Stats
  
  The current version of the model is trained on a filtered and sentence
  segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/),
  a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a
  special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/).
  
  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 v3-32!
  
  ## Usage
  
  With Transformers >= 4.3 our cased ConvBERT model can be loaded like:
  
  ```python
  from transformers import AutoModel, AutoTokenizer
  
  model_name = "dbmdz/convbert-base-turkish-cased"
  
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModel.from_pretrained(model_name)
  ```
  
  ## Results
  
  For results on PoS tagging, NER and Question Answering downstream tasks, please refer to
  [this repository](https://github.com/stefan-it/turkish-bert).
  
  # 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 DBMDZ BERT models in general, 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 🤗
  