add readme

README.md

@@ -1,3 +1,64 @@
 ---
 license: mit
 ---
+# bertimbau-NER
+This model card aims to simplify the use of the [portuguese Bert, a.k.a, Bertimbau](https://github.com/neuralmind-ai/portuguese-bert) for the Named Entity Recognition task. 
+
+For this model card the we used the BERT-CRF (selective scenario, 5 classes) model available in the [ner_evalutaion](https://github.com/neuralmind-ai/portuguese-bert/tree/master/ner_evaluation) folder of the original Bertimbau repo.
+
+## Usage
+
+```{python}
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+
+tokenizer = AutoTokenizer.from_pretrained("marquesafonso/bertimbau-large-ner")
+model = AutoModelForTokenClassification.from_pretrained("marquesafonso/bertimbau-large-ner")
+
+```
+
+## Example
+
+```{python}
+from transformers import pipeline
+
+pipe = pipeline("token-classification", model="marquesafonso/bertimbau-large-ner")
+
+sentence = "Acima de Ederson, abaixo de Rúben Dias. É entre os dois jogadores do Manchester City que se vai colocar Gonçalo Ramos no ranking de vendas mais avultadas do Benfica."
+
+result = pipe([sentence])
+
+print(f"{sentence}\n{result}")
+
+# Acima de Ederson, abaixo de Rúben Dias. É entre os dois jogadores do Manchester City que se vai colocar Gonçalo Ramos no ranking de vendas mais avultadas do Benfica.
+# [[{'entity': 'B-PESSOA', 'score': 0.99976975, 'index': 4, 'word': 'Ed', 'start': 9, 'end': 11}, {'entity': 'I-PESSOA', 'score': 0.9941182, 'index': 5, 'word': '##erson', 'start': 11, 'end': 16}, {'entity': 'B-PESSOA', 'score': 0.9998306, 'index': 9, 'word': 'R', 'start': 28, 'end': 29}, {'entity': 'I-PESSOA', 'score': 0.9737293, 'index': 10, 'word': '##ú', 'start': 29, 'end': 30}, {'entity': 'I-PESSOA', 'score': 0.9944133, 'index': 11, 'word': '##ben', 'start': 30, 'end': 33}, {'entity': 'I-PESSOA', 'score': 0.9994117, 'index': 12, 'word': 'Dias', 'start': 34, 'end': 38}, {'entity': 'B-ORGANIZACAO', 'score': 0.94043595, 'index': 20, 'word': 'Manchester', 'start': 69, 'end': 79}, {'entity': 'I-ORGANIZACAO', 'score': 0.9870952, 'index': 21, 'word': 'City', 'start': 80, 'end': 84}, {'entity': 'B-PESSOA', 'score': 0.9997652, 'index': 26, 'word': 'Gonçalo', 'start': 104, 'end': 111}, {'entity': 'I-PESSOA', 'score': 0.9989994, 'index': 27, 'word': 'Ramos', 'start': 112, 'end': 117}, {'entity': 'B-ORGANIZACAO', 'score': 0.9033079, 'index': 37, 'word': 'Benfica', 'start': 157, 'end': 164}]]
+```
+
+## Acknowledgements
+
+This work is an adaptation of [portuguese Bert, a.k.a, Bertimbau](https://github.com/neuralmind-ai/portuguese-bert). You may check and/or cite their [work](http://arxiv.org/abs/1909.10649):
+
+```
+@InProceedings{souza2020bertimbau,
+    author="Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto",
+    editor="Cerri, Ricardo and Prati, Ronaldo C.",
+    title="BERTimbau: Pretrained BERT Models for Brazilian Portuguese",
+    booktitle="Intelligent Systems",
+    year="2020",
+    publisher="Springer International Publishing",
+    address="Cham",
+    pages="403--417",
+    isbn="978-3-030-61377-8"
+}
+
+
+@article{souza2019portuguese,
+    title={Portuguese Named Entity Recognition using BERT-CRF},
+    author={Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
+    journal={arXiv preprint arXiv:1909.10649},
+    url={http://arxiv.org/abs/1909.10649},
+    year={2019}
+}
+```
+
+Note that the authors - Fabio Capuano de Souza, Rodrigo Nogueira, Roberto de Alencar Lotufo - have used an MIT LICENSE for their work.