Update README.md

README.md

@@ -4,4 +4,91 @@ license: mit
 inference:
   parameters:
     aggregation_strategy: "average"
+
+language:
+  - pt
+pipeline_tag: fill-mask
+tags:
+  - medialbertina-ptpt
+  - deberta
+  - portuguese
+  - european portuguese
+  - medical
+  - clinical
+  - healthcare
+  - NER
+  - Named Entity Recognition
+  - IE
+  - Information Extraction
+widget:
+  - text: Durante a cirurgia ortopédica para corrigir a fratura no tornozelo, os sinais vitais do utente, incluindo a pressão arterial, com leitura de 120/87 mmHg, a frequência cardíaca, de 80 batimentos por minuto, e SpO2 a 98%, foram monitorizados. Após a cirurgia o utente apresentava  dor intensa no local e inchaço no tornozelo, mas os resultados dos exames de radiografia revelaram uma recuperação satisfatória.
+    example_title: Example 1
+  - text: Durante o procedimento endoscópico, foram encontrados pólipos no cólon do paciente.
+    example_title: Example 2
+  - text: Foi recomendada aspirina de 500mg a cada 4 horas, durante 3 dias.
+    example_title: Example 3
+  - text: Após as sessões de fisioterapia o paciente apresenta recuperação de mobilidade.
+    example_title: Example 4
+  - text: O paciente está em Quimioterapia com uma dosagem específica de Cisplatina para o tratamento do cancro do pulmão.
+    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.
+    example_title: Example 7
+  - text:  A paciente foi diagnosticada com esclerose múltipla e iniciou terapia com imunomoduladores.
 ---
+
+# MediAlbertina
+The first publicly available medical language models 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).
+
+
+
+# Model Description
+
+MediAlbertina PT-PT 900M NER was created through domain adaptation 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 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                   | 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).
+
+
+## How to use
+
+```Python
+from transformers import pipeline
+
+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:
+  print(f"{entity['entity_group']} - {sentence[entity['start']:entity['end']]}")
+```
+
+## Citation
+
+MediAlbertina is developed by a joint team from [ISCTE-IUL](https://www.iscte-iul.pt/), Portugal, and [Select Data](https://selectdata.com/), CA USA. For a fully detailed description, check the respective publication:
+
+```latex
+In publishing process. Reference will be added soon.
+```
+Please use the above cannonical reference when using or citing this model.