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PleIAs/journaux-lm-v1
journaux-lm-v1 is a machine learning model from PleIAs. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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From the Hugging Face model README

The Journaux-LM is a language model pretrained on historical French newspapers. Technically the model itself is an ELECTRA model, which was pretrained with the TEAMS approach.
Version 1 of the Journaux-LM was pretrained on the following publicly available datasets:
In total, the pretraining corpus has a size of 408GB.
We compare our Zeitungs-LM directly to the French Europeana BERT model (as Zeitungs-LM is supposed to be the successor of it) on various downstream tasks from the hmBench repository, which is focussed on Named Entity Recognition.
We report averaged micro F1-Score over 5 runs with different seeds and use the best hyper-parameter configuration on the development set of each dataset to report the final test score.
The results on the development set can be seen in the following table:
| Model \ Dataset | AjMC | ICDAR | LeTemps | NewsEye | HIPE-2020 | Avg. |
|---|---|---|---|---|---|---|
| Europeana BERT | 85.7 | 77.63 | 67.14 | 82.68 | 85.98 | 79.83 |
| Journaux-LM v1 | 86.25 | 78.51 | 67.76 | 84.07 | 88.17 | 80.95 |
Our Journaux-LM leads to a performance boost of 1.12% compared to the German Europeana BERT model.
The final results on the test set can be seen here:
| Model \ Dataset | AjMC | ICDAR | LeTemps | NewsEye | HIPE-2020 | Avg. |
|---|---|---|---|---|---|---|
| Europeana BERT | 81.06 | 78.17 | 67.22 | 73.51 | 81.00 | 76.19 |
| Journaux-LM v1 | 83.41 | 77.73 | 67.11 | 74.48 | 83.14 | 77.17 |
Our Journaux-LM beats the French Europeana BERT model by 0.98%.
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️
Made from Bavarian Oberland with ❤️ and 🥨.