Update README.md

README.md

@@ -8,4 +8,58 @@ language:
 metrics:
 - f1
 pipeline_tag: token-classification
----
+---
+
+
+# Portuguese NER BERT-CRF HAREM Default
+
+This model is a fine-tuned BERT model adapted for Named Entity Recognition (NER) tasks. It utilizes Conditional Random Fields (CRF) as the decoder.
+
+The model follows the HAREM Default labeling scheme for NER. Additionally, it provides options for HAREM Selective and Conll-2003 labeling schemes.
+
+## How to Use
+
+You can employ this model using the Transformers library's *pipeline* for NER, or incorporate it as a conventional Transformer in the HuggingFace ecosystem.
+
+```python
+from transformers import pipeline
+import torch
+import nltk
+
+ner_classifier = pipeline(
+    "ner",
+    model="arubenruben/NER-PT-BERT-CRF-HAREM-Default",
+    device=torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu"),
+    trust_remote_code=True
+)
+
+text = "FCPorto vence o Benfica por 5-0 no Estádio do Dragão"
+tokens = nltk.wordpunct_tokenize(text)
+result = ner_classifier(tokens)
+```
+
+## Demo
+
+There is a [Notebook](https://github.com/arubenruben/PT-Pump-Up/blob/master/BERT-CRF.ipynb) available to test our code.
+
+## PT-Pump-Up
+
+This model is integrated in the project [PT-Pump-Up](https://github.com/arubenruben/PT-Pump-Up)
+
+
+## Evaluation
+
+#### Testing Data
+
+The model was tested on the Miniharem Testset.
+
+### Results
+
+F1-Score: 0.787
+
+## Citation
+
+Citation will be made available soon.
+
+**BibTeX:**
+:(