Update README.md

README.md

@@ -8,11 +8,21 @@ widget:
     * ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut
     Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44.
 - text: >-
-    HILPERHAUSEN, (Géog.) ville d'Allemagne en Franconie, sur la Werra, au comté de Henneberg, entre Cobourg & Smalcalde ; elle appartient à une branche de la maison de Saxe-Gotha. Long. 28. 15. lat. 50. 35. (D. J.)
+    HILPERHAUSEN, (Géog.) ville d'Allemagne en Franconie, sur la Werra, au comté
+    de Henneberg, entre Cobourg & Smalcalde ; elle appartient à une branche de
+    la maison de Saxe-Gotha. Long. 28. 15. lat. 50. 35. (D. J.)
+datasets:
+- GEODE/GeoEDdA
+metrics:
+- f1
+- precision
+- recall
+base_model:
+- almanach/camembert-base
 ---
 
 
-# bert-base-french-cased-edda-ner
+# camembert-base-edda-span-classification
 
 
 <!-- Provide a quick summary of what the model is/does. -->
@@ -72,7 +82,7 @@ import torch
 from datasets import load_dataset
 
 
-pipe = pipeline("token-classification", model="GEODE/bert-base-french-cased-edda-ner", aggregation_strategy="simple", device=device)
+pipe = pipeline("token-classification", model="GEODE/camembert-base-edda-span-classification", aggregation_strategy="simple", device=device)
 
 content = "* ALBI, (Géog.) ville de France, capitale de l'Albigeois, dans le haut Languedoc : elle est sur le Tarn. Long. 19. 49. lat. 43. 55. 44."
 
@@ -197,28 +207,29 @@ For full training details and results please see the GitHub repository: [https:/
 ### Evaluation
 
 
-* Overall model performances (Test set)
+* Overall micro-average model performances (token-based)
 
 
 |   | Precision | Recall | F-score |
 |---|:---:|:---:|:---:|
-|    | 90.1   | 93.7   | 91.9 | 
+|    | 91.5   | 94.8   | 93.1 | 
 
 
 
-* Model performances by entity (Test set)
+* Token-based model performances (Test set)
 
-|   | Precision | Recall | F-score |
+|   | Precision | Recall | F-score | Support
 |---|:---:|:---:|:---:|
-| NC-Spatial    |  91.6  |  95.3  |  93.4 |
-| NP-Spatial    |  95.9  |  95.5  |  95.7 |
-| Relation      |  89.4  |  94.7  |  91.9 |
-| Latlong       |  98.1  |  96.8  |  97.4 |
-| NC-Person     |  67.5  |  84.0  |  74.9 |
-| NP-Person     |  87.4  |  89.2  |  88.3 |
-| NP-Misc       |  72.4  |  76.6  |  74.4 |
-| Head          |  97.6  |  97.2  |  97.4 |
-| Domain-mark   |  99.2  |  100.0  |  99.6 |
+| NC-Spatial    |  96.7  |  95.1  |  95.9 | 592
+| NP-Spatial    |  95.9  |  95.5  |  95.7 | 717
+| Relation      |  89.8  |  95.6  |  92.6 | 452
+| Latlong       |  97.0  |  98.5  |  97.7 | 789
+| NC-Person     |  70.4  |  78.4  |  74.2 | 222
+| NP-Person     |  88.6  |  90.4  |  89.5 | 198
+| NP-Misc       |  69.0  |  82.9  |  75.3 | 175
+| Head          |  97.3  |  98.0  |  97.6 | 254
+| Domain-mark   |  99.0  |  100.0  |  99.5 | 392
+
 
 
 
@@ -228,4 +239,4 @@ For full training details and results please see the GitHub repository: [https:/
 
 
 The authors are grateful to the [ASLAN project](https://aslan.universite-lyon.fr) (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).
-Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago.
+Data courtesy the [ARTFL Encyclopédie Project](https://artfl-project.uchicago.edu), University of Chicago.