dbmdz

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources a cased model for Turkish 🎉 BERTurk is a community-driven cased BERT model for Turkish. 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. The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer. 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 v3-8 for 2M steps. Currently only PyTorch-Transformers compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | --------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-turkish-cased` | `config.json` • `pytorchmodel.bin` • `vocab.txt` With Transformers >= 2.3 our BERTurk cased model can be loaded like: For results on PoS tagging or NER tasks, please refer to this repository. All models are available on the Huggingface model hub. For questions about our BERT models just open an issue here 🤗 Thanks to Kemal Oflazer 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 team, it is possible to download both cased and uncased models from their S3 storage 🤗

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources an uncased model for Turkish 🎉 BERTurk is a community-driven uncased BERT model for Turkish. 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. The current version of the model is trained on a filtered and sentence segmented version of the Turkish OSCAR corpus, a recent Wikipedia dump, various OPUS corpora and a special corpus provided by Kemal Oflazer. 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 an uncased model on a TPU v3-8 for 2M steps. Currently only PyTorch-Transformers compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | -------------------------------------- | --------------------------------------------------------------------------------------------------------------- | `dbmdz/bert-base-turkish-128k-uncased` | `config.json` • `pytorchmodel.bin` • `vocab.txt` With Transformers >= 2.3 our BERTurk uncased model can be loaded like: For results on PoS tagging or NER tasks, please refer to this repository. All models are available on the Huggingface model hub. For questions about our BERT models just open an issue here 🤗 Thanks to Kemal Oflazer 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 team, it is possible to download both cased and uncased models from their S3 storage 🤗

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources another German BERT models 🎉 In addition to the recently released German BERT model by deepset we provide another German-language model. The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with a size of 16GB and 2,350,234,427 tokens. For sentence splitting, we use spacy. Our preprocessing steps (sentence piece model for vocab generation) follow those used for training SciBERT. The model is trained with an initial sequence length of 512 subwords and was performed for 1.5M steps. This release includes both cased and uncased models. Currently only PyTorch-Transformers compatible weights are available. If you need access to TensorFlow checkpoints, please raise an issue! | Model | Downloads | -------------------------------- | --------------------------------------------------------------------------------------------------------------- | `bert-base-german-dbmdz-cased` | `config.json` • `pytorchmodel.bin` • `vocab.txt` | `bert-base-german-dbmdz-uncased` | `config.json` • `pytorchmodel.bin` • `vocab.txt` With Transformers >= 2.3 our German BERT models can be loaded like: For results on downstream tasks like NER or PoS tagging, please refer to this repository. All models are available on the Huggingface model hub. For questions about our BERT models just open an issue here 🤗 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 team, it is possible to download both cased and uncased models from their S3 storage 🤗