Better README.md

README.md

@@ -1,33 +1,30 @@
----
-tags:
-- generated_from_trainer
-model-index:
-- name: model_pretrained
-  results: []
----
+# CamemBERT pretrained on french trade directories from the XIXth century
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+This mdoel is part of the material of the paper
+> Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B. (2022). A
+> Benchmark of Named Entity Recognition Approaches in Historical
+> Documents Application to 19𝑡ℎ Century French Directories. In: Uchida,
+> S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022.
+> Lecture Notes in Computer Science, vol 13237. Springer, Cham.
+> https://doi.org/10.1007/978-3-031-06555-2_30
 
-# model_pretrained
+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/10-camembert_pretraining.ipynb`.
 
-This model is a fine-tuned version of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.5619
 
 ## Model description
-
-More information needed
+This model pre-train the model [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on a set of ~845k entries from Paris trade directories from the XIXth century extracted with OCR.
+Trade directory entries are short and strongly structured texts that giving the name, activity and location of a person or business, e.g: 
+```
+Peynaud, R. de la Vieille Bouclerie, 18. Richard, Joullain et comp., (commission- —Phéâtre Français. naire, (entrepôt), au port de la Rapée-
+```
 
 ## Intended uses & limitations
+This model is intended for reproducibility of the NER evaluation published in the DAS2022 paper.
+Several derived models trained for NER on trade directories are available on HuggingFace, each trained on a different dataset :
+- [das22-10-camembert_pretrained_finetuned_ref](): trained for NER on ~6000 directory entries manually corrected.
+- [das22-10-camembert_pretrained_finetuned_pero](): trained for NER on ~6000 directory entries extracted with PERO-OCR.
+- [das22-10-camembert_pretrained_finetuned_tess](): trained for NER on ~6000 directory entries extracted with Tesseract.
 
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters
 
@@ -55,3 +52,4 @@ The following hyperparameters were used during training:
 - Pytorch 1.10.1+cu102
 - Datasets 1.17.0
 - Tokenizers 0.10.3
+