From 7df12d7bf8fa14e0daf39ad9efad6a47e94b1cda Mon Sep 17 00:00:00 2001
From: Manuel Romero <mrm8488@gmail.com>
Date: Thu, 20 Feb 2020 18:30:54 +0100
Subject: [PATCH] Update README.md

- I added an example using the model with pipelines to show that we have set```{"use_fast": False}``` in the tokenizer.
- I added a Colab to play with the model and pipelines
- I added a Colab to discover Huggingface pipelines at the end of the document
---
 .../README.md                                 | 41 ++++++++++++++++---
 1 file changed, 35 insertions(+), 6 deletions(-)

diff --git a/model_cards/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md b/model_cards/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md
index 001d1bdd13..09a8894637 100644
--- a/model_cards/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md
+++ b/model_cards/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md
@@ -89,7 +89,37 @@ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following com
 
 So, yes, this version is even more accurate.
 
-### Model in action (in a Colab Notebook)
+### Model in action
+
+Fast usage with **pipelines**:
+
+```python
+from transformers import *
+
+# Important!: By now the QA pipeline is not compatible with fast tokenizer, but they are working on it. So that pass the object to the tokenizer {"use_fast": False} as in the following example:
+
+nlp = pipeline(
+    'question-answering', 
+    model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
+    tokenizer=(
+        'mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',  
+        {"use_fast": False}
+    )
+)
+
+nlp(
+    {
+        'question': '¿Para qué lenguaje está trabajando?',
+        'context': 'Manuel Romero está colaborando activamente con huggingface/transformers ' +
+                    'para traer el poder de las últimas técnicas de procesamiento de lenguaje natural al idioma español'
+    }
+)
+# Output: {'answer': 'español', 'end': 169, 'score': 0.67530957344621, 'start': 163}
+```
+
+Play with this model and ```pipelines``` in a Colab:
+
+<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Using_Spanish_BERT_fine_tuned_for_Q%26A_pipelines.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
 
 <details>
 
@@ -100,13 +130,12 @@ So, yes, this version is even more accurate.
 2.  Run predictions:
 
 ![Run the model](https://media.giphy.com/media/WT453aptcbCP7hxWTZ/giphy.gif)
-
-3. Using **Pipelines**
-
-![qa-pipelines](https://media.giphy.com/media/L4YiWhinC3P1GF31t0/giphy.gif)
-
 </details>
 
+More about ``` Huggingface pipelines```? check this Colab out:
+
+<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Huggingface_pipelines_demo.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
+
 > Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
 
 > Made with <span style="color: #e25555;">&hearts;</span> in Spain