Update README.md

README.md

@@ -33,43 +33,48 @@ widget:
     example_title: Example 5
   - text: Monitorização da  Freq. cardíaca com 90 bpm. P Arterial de 120-80 mmHg
     example_title: Example 6
-  - text: A ressonância magnética da utente revelou uma ruptura no menisco lateral do joelho.
+  - text: A ressonância magnética da utente revelou uma rotura no menisco lateral do joelho.
     example_title: Example 7
   - text:  A paciente foi diagnosticada com esclerose múltipla e iniciou terapia com imunomoduladores.
+    example_title: Example 8
 ---
 
 # MediAlbertina
-The first publicly available medical language models trained with real European Portuguese data.
+The first publicly available medical language model trained with real European Portuguese data.
 
 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/blob/main/LICENSE).
+Like its antecessors, MediAlbertina models are distributed under the [MIT license](https://huggingface.co/portugueseNLP/medialbertina_pt-pt_900m_NER_all/blob/main/LICENSE).
 
 
 
 # Model Description
 
-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
-- Sintoma
-- Medicamento
-- Dosagem
-- ProcedimentoMedico
-- SinalVital
-- Resultado
-- Progresso
+**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:
+- **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.
+- **Dosagem (D)**: Dosage and frequency of medication administration.
+- **ProcedimentoMedico (PM)**: Anything healthcare professionals do related to patients, including exams, moving patients, administering something, or even surgeries.
+- **SinalVital (SV)**: Quantifiable indicators in a patient that can be measured, always associated with a specific result. Examples include cholesterol levels, diuresis, weight, or glycaemia.
+- **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 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 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.
+
+| 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 | 
+|-------------------------|:----:|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+|                         | F1   | F1   | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  | F1  |
+| albertina-900m-portuguese-ptpt-encoder|0.721|0.786|0.734|0.775|0.737|0.805|0.859|0.811|0.803|0.816|0.913|0.871|**0.853**|**0.895**|0.769|0.785|
+| **medialbertina_pt-pt_900m** | **0.799**| **0.832**| **0.754**| **0.782**| **0.786**| **0.813**| **0.916**| **0.788**| **0.821**| **0.83**| **0.926**| **0.895**|0.85|0.885| **0.779**| **0.807**|
+
+
+
 
-| Model                   | NER single-model | NER multi-models | Assertion Status |
-|-------------------------|:----------------:|:----------------:|:----------------:|
-|                         |    F1-score      |    F1-score      |    F1-score      |
-|albertina-900m-portuguese-ptpt-encoder         |      0.813       |      0.811       |      0.687       |
-| **medialbertina_pt-pt_900m** |    **0.832**     |    **0.848**     |    **0.755**     |
 
 ## Data
 
-MediAlbertina PT-PT 900M NER was fine-tuned on more than 10k hand-annotated entities from more than a thousand 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 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).
 
 
 ## How to use
@@ -77,11 +82,11 @@ MediAlbertina PT-PT 900M NER was fine-tuned on more than 10k hand-annotated enti
 ```Python
 from transformers import pipeline
 
-ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER', aggregation_strategy='average')
+ner_pipeline = pipeline('ner', model='portugueseNLP/medialbertina_pt-pt_900m_NER_all', aggregation_strategy='average')
 sentence = 'Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.'
 entities = ner_pipeline(sentence)
 for entity in entities:
-  print(f"{entity['entity_group']} - {sentence[entity['start']:entity['end']]}")
+    print(f"{entity['entity_group']} - {sentence[entity['start']:entity['end']]}")
 ```
 
 ## Citation
@@ -91,4 +96,4 @@ MediAlbertina is developed by a joint team from [ISCTE-IUL](https://www.iscte-iu
 ```latex
 In publishing process. Reference will be added soon.
 ```
-Please use the above cannonical reference when using or citing this model.
+Please use the above cannonical reference when using or citing this model.