Update README.md

README.md

@@ -60,7 +60,7 @@ Like its antecessors, MediAlbertina models are distributed under the [MIT licens
 
 # Model Description
 
-MediAlbertina PT-PT 1.5B was created through domain adaptation of [Albertina PT-PT 1.5B](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder) on real European Portuguese EMRs by employing masked language modeling. It underwent evaluation through fine-tuning for the Information Extraction (IE) tasks Named Entity Recognition (NER) and Assertion Status (AStatus) on more than 10k manually annotated entities belonging to the following classes: Diagnosis, Symptom, Vital Sign, Result, Medical Procedure, Medication, Dosage, and Progress.
+**MediAlbertina PT-PT 1.5B** was created through domain adaptation of [Albertina PT-PT 1.5B](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptpt-encoder) on real European Portuguese EMRs by employing masked language modeling. It underwent evaluation through fine-tuning for the Information Extraction (IE) tasks Named Entity Recognition (NER) and Assertion Status (AStatus) on more than 10k manually annotated entities belonging to the following classes: Diagnosis, Symptom, Vital Sign, Result, Medical Procedure, Medication, Dosage, and Progress.
 In both tasks, MediAlbertina achieved superior results to its antecessors, demonstrating the effectiveness of this domain adaptation, and its potential for medical AI in Portugal.
 
 | Model                         | NER Single Model | NER Multi-Models (Diag+Symp) | NER Multi-Models (Med+Dos) | NER Multi-Models (MP+VS+R) | NER Multi-Models (Prog) | Assertion Status (Diag) | Assertion Status (Symp) | Assertion Status (Med) |
@@ -69,13 +69,13 @@ In both tasks, MediAlbertina achieved superior results to its antecessors, demon
 | Albertina PT-PT 900M          |      0.813       |             0.771             |            0.886           |            0.777          |          0.784          |       0.703        |       0.803       |        0.556        |
 | Albertina PT-PT 1.5B          |      0.838       |             0.801             |            0.924           |            0.836          |        **0.877**        |       0.772        |       0.881       |        0.862        |
 | MediAlbertina PT-PT 900M      |      0.832       |             0.801             |            0.916           |            0.810          |          0.864          |       0.722        |       0.823       |        0.723        |
-| MediAlbertina PT-PT 1.5B      |    **0.843**     |           **0.813**           |          **0.926**         |          **0.851**        |          0.858          |     **0.789**      |     **0.886**     |      **0.868**      |
+| **MediAlbertina PT-PT 1.5B**      |    **0.843**     |           **0.813**           |          **0.926**         |          **0.851**        |          0.858          |     **0.789**      |     **0.886**     |      **0.868**      |
 
 
 
 ## Data
 
-MediAlbertina PT-PT 1.5B was trained on more than 15M sentences and 300M tokens from 2.6M fully anonymized and unique Electronic Medical Records (EMRs) from Portugal's largest public hospital. This data was acquired under the framework of the [FCT project DSAIPA/AI/0122/2020 AIMHealth-Mobile Applications Based on Artificial Intelligence](https://ciencia.iscte-iul.pt/projects/aplicacoes-moveis-baseadas-em-inteligencia-artificial-para-resposta-de-saude-publica/1567).
+**MediAlbertina PT-PT 1.5B** was trained on more than 15M sentences and 300M tokens from 2.6M fully anonymized and unique Electronic Medical Records (EMRs) from Portugal's largest public hospital. This data was acquired under the framework of the [FCT project DSAIPA/AI/0122/2020 AIMHealth-Mobile Applications Based on Artificial Intelligence](https://ciencia.iscte-iul.pt/projects/aplicacoes-moveis-baseadas-em-inteligencia-artificial-para-resposta-de-saude-publica/1567).
 
 
 ## How to use