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--- |
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language: fr |
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license: mit |
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tags: |
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- "historic french" |
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--- |
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# 🤗 + 📚 dbmdz ELECTRA 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 French Europeana ELECTRA models 🎉 |
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# French Europeana ELECTRA |
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We extracted all French texts using the `language` metadata attribute from the Europeana corpus. |
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The resulting corpus has a size of 63GB and consists of 11,052,528,456 tokens. |
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Based on the metadata information, texts from the 18th - 20th century are mainly included in the |
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training corpus. |
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Detailed information about the data and pretraining steps can be found in |
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[this repository](https://github.com/stefan-it/europeana-bert). |
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## Model weights |
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ELECTRA model weights for PyTorch and TensorFlow are available. |
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* French Europeana ELECTRA (discriminator): `dbmdz/electra-base-french-europeana-cased-discriminator` - [model hub page](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-discriminator/tree/main) |
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* French Europeana ELECTRA (generator): `dbmdz/electra-base-french-europeana-cased-generator` - [model hub page](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-generator/tree/main) |
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## Results |
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For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert). |
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## Usage |
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With Transformers >= 2.3 our French Europeana ELECTRA model 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/electra-base-french-europeana-cased-discriminator") |
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model = AutoModel.from_pretrained("dbmdz/electra-base-french-europeana-cased-discriminator") |
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``` |
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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 ELECTRA 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 our models from their S3 storage 🤗 |
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