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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: CamemBERT pretrained on french trade directories from the XIXth century |
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results: [] |
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--- |
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# CamemBERT trained and fine-tuned for NER on french trade directories from the XIXth century [PERO-OCR training set] |
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This mdoel is part of the material of the paper |
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> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A |
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> Benchmark of Named Entity Recognition Approaches in Historical |
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> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida, |
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> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. |
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> Lecture Notes in Computer Science, vol 13237. Springer, Cham. |
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> https://doi.org/10.1007/978-3-031-06555-2_30 |
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The source code to train this model is available on the [GitHub repository](https://github.com/soduco/paper-ner-bench-das22) of the paper as a Jupyter notebook in `src/ner/40_experiment_2.ipynb`. |
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## Model description |
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This model adapts the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for NER on 6004 manually annotated directory entries referred as the "reference dataset" in the paper. |
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Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g: |
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``` |
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Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée- |
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``` |
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## Intended uses & limitations |
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This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper. |
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Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset : |
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- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected. |
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- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR. |
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- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract. |
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### Training hyperparameters |
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### Training results |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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