
# 🤗 + 📚 dbmdz German BERT models

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models 🎉

# German BERT

## Stats

In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [deepset](https://deepset.ai/) 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](https://spacy.io/). Our preprocessing steps
(sentence piece model for vocab generation) follow those used for training
[SciBERT](https://github.com/allenai/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.

## Model weights

Currently only PyTorch-[Transformers](https://github.com/huggingface/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`](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)
| `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)

## Usage

With Transformers >= 2.3 our German BERT models can be loaded like:

```python
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-german-cased")
model = AutoModel.from_pretrained("dbmdz/bert-base-german-cased")
```

## Results

For results on downstream tasks like NER or PoS tagging, please refer to
[this repository](https://github.com/stefan-it/fine-tuned-berts-seq).

# Huggingface model hub

All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).

# Contact (Bugs, Feedback, Contribution and more)

For questions about our BERT models just open an issue
[here](https://github.com/dbmdz/berts/issues/new) 🤗

# Acknowledgments

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](https://huggingface.co/) team,
it is possible to download both cased and uncased models from their S3 storage 🤗

{"language": "de", "license": "mit"}
transformers pytorch tf jax safetensors bert fill-mask de license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

+ dbmdz German BERT models
==========================


In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models


German BERT
===========


Stats
-----


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.


Model weights
-------------


Currently only PyTorch-Transformers
compatible weights are available. If you need access to TensorFlow checkpoints,
please raise an issue!



Usage
-----


With Transformers >= 2.3 our German BERT models can be loaded like:


Results
-------


For results on downstream tasks like NER or PoS tagging, please refer to
this repository.


Huggingface model hub
=====================


All models are available on the Huggingface model hub.


Contact (Bugs, Feedback, Contribution and more)
===============================================


For questions about our BERT models just open an issue
here


Acknowledgments
===============


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



TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #de #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
