Create README.md (#8630)

* Create README.md

* correct metrics id

cc @lhoestq
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/monilouise/ner_pt_br/README.md

+---
+language: 
+- pt
+tags:
+- ner
+metrics:
+- f1
+- accuracy
+- precision
+- recall
+---
+
+# RiskData Brazilian Portuguese NER
+
+## Model description
+
+This is a finetunned version from [Neuralmind BERTimbau] (https://github.com/neuralmind-ai/portuguese-bert/blob/master/README.md) for Portuguese language.
+
+For more details, please see, (https://github.com/SecexSaudeTCU/noticias_ner).
+
+## Intended uses & limitations
+
+#### How to use
+
+#### Limitations and bias
+
+- The finetunned model was trained on a corpus with around 180 news articles crawled from Google News.  The original project's purpose was to recognize named entities in news 
+related to fraud and corruption, classifying these entities in four classes: PERSON, ORGANIZATION, PUBLIC INSITUITION and LOCAL (PESSOA, ORGANIZAÇÃO, INSTITUIÇÃO PÚBLICA and LOCAL).
+
+## Training data
+
+The training data can be found at (https://github.com/SecexSaudeTCU/noticias_ner/blob/master/dados/labeled_4_labels.jsonl).
+
+
+## Training procedure
+
+
+## Eval results
+
+accuracy: 0.98, 
+precision: 0.86 
+recall: 0.91
+f1: 0.88
+
+
+The score was calculated using this code:
+
+```python
+    def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
+        preds = np.argmax(predictions, axis=2)
+        batch_size, seq_len = preds.shape
+        out_label_list = [[] for _ in range(batch_size)]
+        preds_list = [[] for _ in range(batch_size)]
+
+        for i in range(batch_size):
+            for j in range(seq_len):
+                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
+                    out_label_list[i].append(id2tag[label_ids[i][j]])
+                    preds_list[i].append(id2tag[preds[i][j]])
+
+        return preds_list, out_label_list
+
+    def compute_metrics(p: EvalPrediction) -> Dict:
+        preds_list, out_label_list = align_predictions(p.predictions, p.label_ids)
+        return {
+            "accuracy_score": accuracy_score(out_label_list, preds_list),
+            "precision": precision_score(out_label_list, preds_list),
+            "recall": recall_score(out_label_list, preds_list),
+            "f1": f1_score(out_label_list, preds_list),
+        }
+```
+
+### BibTeX entry and citation info
+
+For further information about BERTimbau language model:
+
+```bibtex
+@inproceedings{souza2020bertimbau,
+    author    = {Souza, F{\'a}bio and Nogueira, Rodrigo and Lotufo, Roberto},
+    title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
+    booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
+    year      = {2020}
+}
+
+@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}
+}
+```