shortening

README.md

@@ -6,12 +6,10 @@ colorTo: indigo
 sdk: static
 pinned: false
 ---
-Hi there 👋 ! 
+[**Impresso - Media Monitoring of the Past**](https://impresso-project.ch) is an interdisciplinary research project that uses machine learning to pursue a paradigm shift in the processing, semantic enrichment, representation, exploration and study of historical media across modalities, temporal, linguistic, and national borders. We develop the 🚀 [**Impresso Web App**](https://impresso-project.ch/app/) and the 🔬 [**Impresso Datalab**](https://impresso-project.ch/datalab/) (coming soon), providing search, exploratory analysis, and programmatic access to an unprecedented corpus of multilingual historical newspapers and radio broadcasts collections. Our work sits at the intersection of Natural Language Processing, Design, and History. 
 
-[**Impresso - Media Monitoring of the Past**](https://impresso-project.ch) is an interdisciplinary research project that uses machine learning to pursue a paradigm shift in the processing, semantic enrichment, representation, exploration and study of historical media across modalities, temporal, linguistic, and national borders. We design and develop the 🚀 [**Impresso Web App**](https://impresso-project.ch/app/) and the 🔬 [**Impresso Datalab**](https://impresso-project.ch/datalab/) (coming soon), providing search, exploratory analysis, and programmatic access to an unprecedented corpus of multilingual historical newspapers and radio broadcasts collections. Our work sits at the intersection of Natural Language Processing, Design, and History. 
-
-We hope these resources will be useful to you — here you will find:
+We share:
 - 🤖 **[Impresso models](https://huggingface.co/impresso-project/models)** tailored for historical, multilingual documents and include language identification, OCR quality assessment, topic inference, NER and NEL.
-- 📚 **[Impresso datasets](https://huggingface.co/impresso-project/datasets)** curated from digitized historical media sources, designed to support ML development and evaluation. Datasets are currently in preparation and will soon be released, including a NER and NEL benchmark developed as part of the [HIPE evaluation campaign](https://hipe-eval.github.io/HIPE-2022/), an image type classification dataset (e.g., article vs. advertisement vs. illustration) and more.
+- 📚 **[Impresso datasets](https://huggingface.co/impresso-project/datasets)** curated from digitized historical media sources, designed to support ML development and evaluation. Datasets are currently in preparation and will soon be released, including a NER and NEL benchmark developed as part of the [HIPE evaluation campaign](https://hipe-eval.github.io/HIPE-2022/), an image type classification dataset, and more.
 
 Impresso gratefully acknowledges the continued support of its cultural heritage 🏛️ [partners](https://impresso-project.ch/consortium/associated-partners/) as well as funding from the SNSF (Grant No. [CRSII5_173719](https://data.snf.ch/grants/grant/173719) and [CRSII5_213585](https://data.snf.ch/grants/grant/213585)) and the FNR (Grant No. 17498891).