Update README.md

README.md

@@ -38,13 +38,17 @@ Our methodology is described in a blog post available in [English](https://blog.
 
 
 ## Results (french QA test split)
-| Model       | Exact_match | F1-score    | Answer_f1 | NoAnswer_f1 |
+| Model | Parameters | Context       | Exact_match | F1-score    | Answer_f1 | NoAnswer_f1 |
 | ----------- | ----------- | ----------- | ----------- | ----------- |
-| [QAmembert](https://huggingface.co/CATIE-AQ/QAmembert) (110M, 512 tokens)   |  77.14       | 86.88       | 75.66 | 98.11
-| [QAmembert2](https://huggingface.co/CATIE-AQ/QAmembert2)  (112M, 1024 tokens)   | 76.47       | 88.25       | 78.66 |   97.84
-| [QAmembert-large](https://huggingface.co/CATIE-AQ/QAmembert-large) (336M, 512 tokens)   | 77.14       | 88.74       | 78.83 | **98.65**
-| QAmemberta (111M, 1024 tokens) (this version)  |  **78.18**       | **89.53**       | **81.40** | 97.64
+| [etalab/camembert-base-squadFR-fquad-piaf](https://huggingface.co/AgentPublic/camembert-base-squadFR-fquad-piaf) | 110M | 512 tokens  |  39.30       | 51.55       | 79.54 | 23.58
+| [QAmembert](https://huggingface.co/CATIE-AQ/QAmembert)|  110M | 512 tokens   |  77.14       | 86.88       | 75.66 | 98.11
+| [QAmembert2](https://huggingface.co/CATIE-AQ/QAmembert2) (this version) | 112M | 1024 tokens   | 76.47       | 88.25       | 78.66 |   97.84
+| [QAmembert-large](https://huggingface.co/CATIE-AQ/QAmembert-large)| 336M |  512 tokens   | 77.14       | 88.74       | 78.83 | **98.65**
+| [QAmemberta](https://huggingface.co/CATIE-AQ/QAmemberta) | 111M | 1024 tokens   |  **78.18**       | **89.53**       | **81.40** | 97.64
 
+Looking at the “Answer_f1” column, Etalab's model appears to be competitive on texts where the answer to the question is indeed in the text provided (it does better than QAmemBERT-large, for example). However, the fact that it doesn't handle texts where the answer to the question is not in the text provided is a drawback.  
+In all cases, whether in terms of metrics, number of parameters or context size, QAmemBERTa achieves the best results.  
+We therefore invite the reader to choose this model.
 
 ### Usage
 
@@ -84,14 +88,14 @@ A Space has been created to test the model. It is available [here](https://huggi
 
 ### QAmemBERT2 & QAmemBERTa
 ```
-@misc {qamembert2023,  
+@misc {qamemberta2024,
     author       = { {BOURDOIS, Loïck} },  
     organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
-    title        = { QAmemberta },  
-    year         = 2024,  
-    url          = { https://huggingface.co/CATIE-AQ/QAmemberta},  
-    doi          = { 10.57967/hf/0821 },  
-    publisher    = { Hugging Face }  
+	title        = { QAmemberta (Revision 976a70b) },
+	year         = 2024,
+	url          = { https://huggingface.co/CATIE-AQ/QAmemberta },
+	doi          = { 10.57967/hf/3639 },
+	publisher    = { Hugging Face }
 }
 ```