Edit model card

πŸ€— + πŸ“š dbmdz ELECTRA models

In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources French Europeana ELECTRA models πŸŽ‰

French Europeana ELECTRA

We extracted all French texts using the language metadata attribute from the Europeana corpus.

The resulting corpus has a size of 63GB and consists of 11,052,528,456 tokens.

Based on the metadata information, texts from the 18th - 20th century are mainly included in the training corpus.

Detailed information about the data and pretraining steps can be found in this repository.

Model weights

ELECTRA model weights for PyTorch and TensorFlow are available.

  • French Europeana ELECTRA (discriminator): dbmdz/electra-base-french-europeana-cased-discriminator - model hub page
  • French Europeana ELECTRA (generator): dbmdz/electra-base-french-europeana-cased-generator - model hub page

Results

For results on Historic NER, please refer to this repository.

Usage

With Transformers >= 2.3 our French Europeana ELECTRA model can be loaded like:

from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("dbmdz/electra-base-french-europeana-cased-discriminator")
model = AutoModel.from_pretrained("dbmdz/electra-base-french-europeana-cased-discriminator")

Huggingface model hub

All models are available on the Huggingface model hub.

Contact (Bugs, Feedback, Contribution and more)

For questions about our ELECTRA models just open an issue here πŸ€—

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 our models from their S3 storage πŸ€—

Downloads last month
9
Safetensors
Model size
34.9M params
Tensor type
I64
Β·
F32
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.