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--- |
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language: de |
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license: mit |
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--- |
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# 🤗 + 📚 dbmdz German BERT models |
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In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State |
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Library open sources another German BERT models 🎉 |
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# German BERT |
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## Stats |
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In addition to the recently released [German BERT](https://deepset.ai/german-bert) |
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model by [deepset](https://deepset.ai/) we provide another German-language model. |
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The source data for the model consists of a recent Wikipedia dump, EU Bookshop corpus, |
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Open Subtitles, CommonCrawl, ParaCrawl and News Crawl. This results in a dataset with |
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a size of 16GB and 2,350,234,427 tokens. |
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For sentence splitting, we use [spacy](https://spacy.io/). Our preprocessing steps |
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(sentence piece model for vocab generation) follow those used for training |
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[SciBERT](https://github.com/allenai/scibert). The model is trained with an initial |
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sequence length of 512 subwords and was performed for 1.5M steps. |
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This release includes both cased and uncased models. |
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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! |
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| Model | Downloads |
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| `bert-base-german-dbmdz-cased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-cased-vocab.txt) |
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| `bert-base-german-dbmdz-uncased` | [`config.json`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-config.json) • [`pytorch_model.bin`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-pytorch_model.bin) • [`vocab.txt`](https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-german-dbmdz-uncased-vocab.txt) |
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## Usage |
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With Transformers >= 2.3 our German BERT models 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/bert-base-german-cased") |
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model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased") |
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``` |
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## Results |
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For results on downstream tasks like NER or PoS tagging, please refer to |
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[this repository](https://github.com/stefan-it/fine-tuned-berts-seq). |
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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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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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