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[model_cards] 🇹🇷 Add new (cased, 128k) BERTurk model
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language: turkish | ||
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# 🤗 + 📚 dbmdz Turkish BERT model | ||
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In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State | ||
Library open sources a cased model for Turkish 🎉 | ||
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# 🇹🇷 BERTurk | ||
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BERTurk is a community-driven cased BERT model for Turkish. | ||
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Some datasets used for pretraining and evaluation are contributed from the | ||
awesome Turkish NLP community, as well as the decision for the model name: BERTurk. | ||
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## Stats | ||
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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/). | ||
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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 | ||
on a TPU v3-8 for 2M steps. | ||
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For this model we use a vocab size of 128k. | ||
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## Model weights | ||
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Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers) | ||
compatible weights are available. If you need access to TensorFlow checkpoints, | ||
please raise an issue! | ||
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| Model | Downloads | ||
| ------------------------------------ | --------------------------------------------------------------------------------------------------------------- | ||
| `dbmdz/bert-base-turkish-128k-cased` | [`config.json`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/bert-base-turkish-128k-cased/vocab.txt) | ||
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## Usage | ||
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With Transformers >= 2.3 our BERTurk cased model can be loaded like: | ||
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```python | ||
from transformers import AutoModel, AutoTokenizer | ||
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tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-128k-cased") | ||
model = AutoModel.from_pretrained("dbmdz/bert-base-turkish-128k-cased") | ||
``` | ||
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## Results | ||
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For results on PoS tagging or NER tasks, please refer to | ||
[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 BERT models just open an issue | ||
[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 | ||
additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing | ||
us the Turkish NER dataset for evaluation. | ||
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Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). | ||
Thanks for providing access to the TFRC ❤️ | ||
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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 🤗 |