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JCLS_model_card.md

@@ -6,7 +6,7 @@ tags:
 - camembert
 - literary-texts
 - nested-entities
-- BookNLP-fr
+- propp-fr
 license: apache-2.0
 metrics:
 - f1
@@ -18,10 +18,10 @@ pipeline_tag: token-classification
 ---
 
 ## INTRODUCTION:
-This model, developed as part of the [BookNLP-fr project](https://github.com/lattice-8094/fr-litbank), is a **NER model** built on top of [camembert-large](https://huggingface.co/almanach/camembert-large) embeddings, trained to predict nested entities in french, specifically for literary texts.
+This model, developed as part of the [propp-fr project](https://github.com/lattice-8094/fr-litbank), is a **NER model** built on top of [camembert-large](https://huggingface.co/almanach/camembert-large) embeddings, trained to predict nested entities in french, specifically for literary texts.
 
 The predicted entities are:
-- mentions of characters (PER): pronouns (je, tu, il, ...), possessive pronouns (mon, ton, son, ...), common nouns (le capitaine, la princesse, ...) and proper nouns (Indiana Delmare, Honoré de Pardaillan, ...) 
+- mentions of characters (PER): pronouns (je, tu, il, ...), possessive pronouns (mon, ton, son, ...), common nouns (le capitaine, la princesse, ...) and proper nouns (Indiana Delmare, Honoré de Pardaillan, ...)
 - facilities (FAC): chatêau, sentier, chambre, couloir, ...
 - time (TIME): le règne de Louis XIV, ce matin, en juillet, ...
 - geo-political entities (GPE): Montrouge, France, le petit hameau, ...

final_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d2ff3d5ec56dfac2790b1e096e3d28b6ece42931bc4ce4628d1d524d789c877b
-size 386227868
+oid sha256:2eb1ed440b59cc3d4d97ccd49a68c825cc633c247ceb6b6b885dbdc58bf1aed2
+size 386227870