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--- |
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language: tr |
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license: mit |
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--- |
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# 🤗 + 📚 dbmdz Turkish ConvBERT model |
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In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State |
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Library open sources a cased ConvBERT model for Turkish 🎉 |
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# 🇹🇷 ConvBERTurk |
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ConvBERTurk is a community-driven cased ConvBERT model for Turkish. |
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In addition to the BERT and ELECTRA based models, we also trained a ConvBERT model. The ConvBERT architecture is presented |
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in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper. |
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We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 |
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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. |
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## Stats |
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The current version of the model is trained on a filtered and sentence |
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segmented version of the Turkish [OSCAR corpus](https://traces1.inria.fr/oscar/), |
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a recent Wikipedia dump, various [OPUS corpora](http://opus.nlpl.eu/) and a |
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special corpus provided by [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/). |
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The final training corpus has a size of 35GB and 44,04,976,662 tokens. |
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Thanks to Google's TensorFlow Research Cloud (TFRC) we could train a cased model |
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on a TPU v3-32! |
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## Usage |
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With Transformers >= 4.3 our cased ConvBERT model can be loaded like: |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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model_name = "dbmdz/convbert-base-turkish-cased" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModel.from_pretrained(model_name) |
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``` |
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## Results |
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For results on PoS tagging, NER and Question Answering downstream tasks, please refer to |
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[this repository](https://github.com/stefan-it/turkish-bert). |
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# Huggingface model hub |
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All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). |
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# Contact (Bugs, Feedback, Contribution and more) |
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For questions about our DBMDZ BERT models in general, just open an issue |
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[here](https://github.com/dbmdz/berts/issues/new) 🤗 |
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# Acknowledgments |
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Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us |
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additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing |
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us the Turkish NER dataset for evaluation. |
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Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). |
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Thanks for providing access to the TFRC ❤️ |
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Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, |
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it is possible to download both cased and uncased models from their S3 storage 🤗 |
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