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@@ -161,10 +161,6 @@ This model aims to solve the following common issues in NER context:
 - **Within document clustering**: Cluster within the same document mentions of the same entity in different languages (e.g., "Cologne" and "Köln").
 - **Long context handling**: Most NER models are limited to `512` tokens, which can be insufficient for documents with multiple entities or complex structures. This model was trained with a context of `4096` tokens.
-Want to quickly check the model's performance? Use the space: [https://huggingface.co/spaces/pierre-tassel/rapido-ner-space](https://huggingface.co/spaces/pierre-tassel/rapido-ner-space)
-![HuggingFace Space Example of the NER + Entity Linking model usage](space.png)
 ## Model Overview
 - **Architecture**: Full finetuned MLM Encoder backbone (`Alibaba-NLP/gte-multilingual-mlm-base`) + token-classification head + attention pooling + per entity-type projection head + CRF

 - **Within document clustering**: Cluster within the same document mentions of the same entity in different languages (e.g., "Cologne" and "Köln").
 - **Long context handling**: Most NER models are limited to `512` tokens, which can be insufficient for documents with multiple entities or complex structures. This model was trained with a context of `4096` tokens.
 ## Model Overview
 - **Architecture**: Full finetuned MLM Encoder backbone (`Alibaba-NLP/gte-multilingual-mlm-base`) + token-classification head + attention pooling + per entity-type projection head + CRF