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#5

by Ezi - opened Aug 22, 2023

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@@ -115,6 +115,8 @@ A subset of this dataset `train`, of ~50k was evaluated using the Data Measuremn
 While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.
 We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations.
 ## Opted-out content

 While there was a positive skew of words relating occupations e.g _`government`_, _`jobs`_ towards she, her, and similar attributions of the masculine and feminine words to they and them, more harmful words attributions such as _`escort`_ and even _`colour`_ presented with greater attributions to she, her and him, his, respectively.
+[![Data Measurement Tool Associations Eval](assets/DMT_eval.png)]
 We welcome users to explore the [Data Measurements nPMI Visualitons for OBELICS](https://huggingface.co/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool) further and to see the [idefics-9b model card](https://huggingface.co/HuggingFaceM4/idefics-9b) for further Bias considerations.
 ## Opted-out content