Update README.md

- Add example of usage
- Update metrics

model_cards/mrm8488/bert-spanish-cased-finetuned-ner/README.md

@@ -19,23 +19,30 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
 | Dev                    | 2.2 K |
 
 
-- [Fine-tune on NER script](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
-
-```bash
-!export NER_DIR='/content/ner_dataset'
-!python /content/transformers/examples/run_ner.py \
-  --model_type bert \
-  --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
-  --do_train \
-  --do_eval \
-  --data_dir '/content/ner_dataset' \
-  --num_train_epochs 15.0 \
-  --max_seq_length 384 \
-  --output_dir /content/model_output \
-  --save_steps 5000 \
+- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/run_ner.py)
 
+- Labels covered:
+
+```
+B-LOC
+B-MISC
+B-ORG
+B-PER
+I-LOC
+I-MISC
+I-ORG
+I-PER
+O
 ```
 
+## Metrics on evaluation set:
+
+|                                                      Metric                                                       |  # score  |
+| :------------------------------------------------------------------------------------: | :-------: |
+| F1                                       | **90.17**  
+| Precision                                | **89.86** | 
+| Recall                                   | **90.47** |    
+
 ## Comparison:
 
 |                                                      Model                                                       |  # score  |
@@ -44,13 +51,24 @@ I preprocessed the dataset and splitted it as train / dev (80/20)
 | [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **89.65** |
 |                                              Best Multilingual BERT                                              |   87.38   |
 
-```
- ***** All metrics on Eval results  *****
+## Model in action
+
+Fast usage with **pipelines**:
+
+```python
+from transformers import pipeline
+
+nlp_ner = pipeline(
+    "ner",
+    model="mrm8488/bert-spanish-cased-finetuned-ner",
+    tokenizer=(
+        'mrm8488/bert-spanish-cased-finetuned-ner',  
+        {"use_fast": False}
+))
+
+nlp_ner(text)
 
-f1 = 0.8965040489828165
-loss = 0.11504213575173258
-precision = 0.893679858239811
-recall = 0.8993461462254805
+#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
 ```
 
 > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)