Update README.md

README.md

@@ -305,7 +305,7 @@ Alongside this evaluation, we also computed the classification accuracy on FairF
 
 | Model | Shots | <nobr>FairFaceGender<br>acc. (std*)</nobr> | <nobr>FairFaceRace<br>acc. (std*)</nobr> | <nobr>FairFaceAge<br>acc. (std*)</nobr> |
 | :--------------------- | --------: | ----------------------------: | --------------------------: | -------------------------: |
-| IDEFICS-1 9B (Instructed) | 0 | 92.7 (6.3) | 59.6 (22.2) | 43.9 (3.9) |
+| IDEFICS-1 80B (Instructed) | 0 | 92.7 (6.3) | 59.6 (22.2) | 43.9 (3.9) |
 | IDEFICS-2 8B (Instructed) | 0 |  96.3 (3.0) |  41.6 (40.9) | 53.5 (3.0) |
 
 *Per bucket standard deviation. Each bucket represents a combination of ethnicity and gender from the [FairFace](https://huggingface.co/datasets/HuggingFaceM4/FairFace) dataset. The standard deviation within each demographic group indicates the disparity in the model's ability to recognize gender, ethnicity, or age across different groups. Specifically, for the IDEFICS-2 model, we notice a notably higher standard deviation in predicting ethnicity. This is evident in its near-zero accuracy for images depicting individuals of Middle Eastern, Latino/Hispanic, and Southeast Asian descent.