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README.md

README.md

🤗 + 📚 dbmdz BERT models

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

Changelog

  • 20.01.2019: Public release of cased and uncased XXL BERT models for Italian. They can be downloaded from the Huggingface model hub.
  • 30.12.2019: Public release of cased and uncased BERT models for Italian.
  • 08.12.2019: If you consider using our model for the upcoming GermEval 2020 shared task, please read at least this blog post by Emily Bender on ethical issues!
  • 10.10.2019: Public release
  • 24.09.2019: Initial version

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!

Model Downloads
bert-base-german-dbmdz-cased config.jsonpytorch_model.binvocab.txt
bert-base-german-dbmdz-uncased config.jsonpytorch_model.binvocab.txt

Usage

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

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.

Italian BERT

The source data for the Italian BERT model consists of a recent Wikipedia dump and various texts from the OPUS corpora collection. The final training corpus has a size of 13GB and 2,050,057,573 tokens.

For sentence splitting, we use NLTK (faster compared to spacy). Our cased and uncased models are training with an initial sequence length of 512 subwords for ~2-3M steps.

For the XXL Italian models, we use the same training data from OPUS and extend it with data from the Italian part of the OSCAR corpus. Thus, the final training corpus has a size of 81GB and 13,138,379,147 tokens.

Model weights

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

Model Downloads
dbmdz/bert-base-italian-cased config.jsonpytorch_model.binvocab.txt
dbmdz/bert-base-italian-uncased config.jsonpytorch_model.binvocab.txt
dbmdz/bert-base-italian-xxl-cased config.jsonpytorch_model.binvocab.txt
dbmdz/bert-base-italian-xxl-uncased config.jsonpytorch_model.binvocab.txt

Results

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

Usage

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

from transformers import AutoModel, AutoTokenizer

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

To load the (recommended) Italian XXL BERT models, just use:

from transformers import AutoModel, AutoTokenizer

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

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 🤗

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