Update README.md

README.md

@@ -44,13 +44,13 @@ The first publicly available medical language model trained with real European P
 
 MediAlbertina is a family of encoders from the Bert family, DeBERTaV2-based, resulting from the continuation of the pre-training of [PORTULAN's Albertina](https://huggingface.co/PORTULAN) models with Electronic Medical Records shared by Portugal's largest public hospital.
 
-Like its antecessors, MediAlbertina models are distributed under the [MIT license](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m_NER_all/blob/main/LICENSE).
+Like its antecessors, MediAlbertina models are distributed under the [MIT license](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m_NER/blob/main/LICENSE).
 
 
 
 # Model Description
 
-**MediAlbertina PT-PT 900M NER all** was created through fine-tuning of [MediAlbertina PT-PT 900M](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m) on real European Portuguese EMRs that have been hand-annotated for the following entities:
+**MediAlbertina PT-PT 900M NER** was created through fine-tuning of [MediAlbertina PT-PT 900M](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m) on real European Portuguese EMRs that have been hand-annotated for the following entities:
 - **Diagnostico (D)**: All types of diseases and conditions following the ICD-10-CM guidelines.
 - **Sintoma (S)**: Any complaints or evidence from healthcare professionals indicating that a patient is experiencing a medical condition.
 - **Medicamento (M)**: Something that is administrated to the patient (through any route), including drugs, specific food/drink, vitamins, or blood for transfusion.
@@ -60,7 +60,7 @@ Like its antecessors, MediAlbertina models are distributed under the [MIT licens
 - **Resultado (R)**: Results can be associated with Medical Procedures and Vital Signs. It can be a numerical value if something was measured (e.g., the value associated with blood pressure) or a descriptor to indicate the result (e.g., positive/negative, functional).
 - **Progresso (P)**: Describes the progress of patient’s condition. Typically, it includes verbs like improving, evolving, or regressing and mentions to patient’s stability. 
   
-**MediAlbertina PT-PT 900M NER all** achieved superior results to the same adaptation made on a non-medical Portuguese language model, demonstrating the effectiveness of this domain adaptation, and its potential for medical AI in Portugal.
+**MediAlbertina PT-PT 900M NER** achieved superior results to the same adaptation made on a non-medical Portuguese language model, demonstrating the effectiveness of this domain adaptation, and its potential for medical AI in Portugal.
 
 | Model                   | B-D | I-D | B-S | I-S | B-PM | I-PM | B-SV | I-SV | B-R | I-R | B-M | I-M | B-DO | I-DO | B-P | I-P | 
 |-------------------------|:----:|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
@@ -74,7 +74,7 @@ Like its antecessors, MediAlbertina models are distributed under the [MIT licens
 
 ## Data
 
-**MediAlbertina PT-PT 900M NER all** was fine-tuned on about 10k hand-annotated medical entities from about 4k fully anonymized medical sentences 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 900M NER** was fine-tuned on about 10k hand-annotated medical entities from about 4k fully anonymized medical sentences 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
@@ -82,7 +82,7 @@ Like its antecessors, MediAlbertina models are distributed under the [MIT licens
 ```Python
 from transformers import pipeline
 
-ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER_all', aggregation_strategy='average')
+ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER', aggregation_strategy='average')
 sentence = 'Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.'
 entities = ner_pipeline(sentence)
 for entity in entities: