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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 Distilled Turkish BERT 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) distilled model for Turkish 🎉 |
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# 🇹🇷 DistilBERTurk |
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DistilBERTurk is a community-driven cased distilled BERT model for Turkish. |
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DistilBERTurk was trained on 7GB of the original training data that was used |
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for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats), |
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using the cased version of BERTurk as teacher model. |
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*DistilBERTurk* was trained with the official Hugging Face implementation from |
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[here](https://github.com/huggingface/transformers/tree/master/examples/distillation) |
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for 5 days on 4 RTX 2080 TI. |
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More details about distillation can be found in the |
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["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) |
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paper by Sanh et al. (2019). |
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## Model weights |
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Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) |
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compatible weights are available. If you need access to TensorFlow checkpoints, |
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please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository! |
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| Model | Downloads |
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| --------------------------------- | --------------------------------------------------------------------------------------------------------------- |
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| `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) |
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## Usage |
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With Transformers >= 2.3 our DistilBERTurk model can be loaded like: |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased") |
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model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased") |
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``` |
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## Results |
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For results on PoS tagging or NER tasks, please refer to |
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[this repository](https://github.com/stefan-it/turkish-bert). |
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For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model. |
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The overall performance difference between DistilBERTurk and the original |
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(teacher) BERTurk model is ~1.18%. |
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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 BERT models 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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