feat: structure day 5

one_exercise_per_file/week03/day05/ex01/audit/readme.md

@@ -0,0 +1,12 @@
+1. This question is validated if the embedding's shape is `(96,)` 
+and the vector 20 first values are: 
+
+```
+array([ 1.0522802e+00,  1.4806499e+00,  7.7402556e-01,  1.0373484e+00,
+        4.1474584e-01, -5.7604712e-01,  3.0856287e+00,  1.4814860e-01,
+       -3.0170975e+00,  3.4453702e+00,  6.3330579e-01,  1.1655847e+00,
+        3.8489954e+00,  2.3469532e+00,  5.0532556e-01, -1.9386177e+00,
+        9.7954911e-01,  2.3573284e+00, -1.9812435e-03,  5.5679207e+00],
+      dtype=float32)
+
+```

one_exercise_per_file/week03/day05/ex01/readme.md

@@ -0,0 +1,5 @@
+# Exercise 1  Embedding 1
+
+The goal of this exercise is to learn to load an embedding on SpaCy. 
+
+1. Install and load `en_core_web_sm` embedding. Compute the embedding of `car`. 

one_exercise_per_file/week03/day05/ex02/audit/readme.md

@@ -0,0 +1,13 @@
+1. The question is validated if the tokens printed are: 
+
+    ```
+    Tokenize
+    this
+    sentence
+    .
+    And
+    this
+    one
+    too
+    .
+    ```

one_exercise_per_file/week03/day05/ex02/readme.md

@@ -0,0 +1,11 @@
+# Exercise 2: Tokenization
+
+
+The goal of this exercise is to learn to tokenize a document using Spacy. We did this using NLTK yesterday. 
+
+1. Tokenize the text below and print the tokens
+
+    ```
+        text = "Tokenize this sentence. And this one too."
+
+    ```

one_exercise_per_file/week03/day05/ex03/audit/readme.md

@@ -0,0 +1,15 @@
+1. This question is validated if the embeddings of each word has a shape of `(300,)` and if the first 20 values of the embedding of laptop are:
+
+    ```
+    array([-0.37639 , -0.075521,  0.4908  ,  0.19863 , -0.11088 , -0.076145,
+        -0.30367 , -0.69663 ,  0.87048 ,  0.54388 ,  0.42523 ,  0.18045 ,
+        -0.4358  , -0.32606 , -0.70702 , -0.069127, -0.42674 ,  2.4147  ,
+            0.26806 ,  0.46584 ], dtype=float32)
+
+    ```
+
+2. This question is validated if the output is 
+
+![alt text][logo]
+
+[logo]: w3day05ex1_plot.png "Plot"

one_exercise_per_file/week03/day05/ex03/readme.md

@@ -0,0 +1,17 @@
+## Exercise 3  Embeddings 2
+
+The goal of this exercise is to learn to use SpaCy embedding on a document. 
+
+1. Compute the embedding of all the words in this sentence. The language model considered is `en_core_web_md`
+
+``` 
+"laptop computer coffee tea water liquid dog cat kitty"
+``` 
+
+2. Plot the pairwise cosine distances between all the words in a HeatMap.  
+ 
+![alt text][logo]
+
+[logo]: images/w3day05ex1_plot.png "Plot"
+
+https://medium.com/datadriveninvestor/cosine-similarity-cosine-distance-6571387f9bf8

one_exercise_per_file/week03/day05/ex04/audit/readme.md

@@ -0,0 +1,8 @@
+1. This question is validated if the similarities between the sentences are: 
+
+    ```
+    sentence_1 <=> sentence 2 : 0.7073220863266589
+    sentence_1 <=> sentence 3: 0.42663743263528325
+    sentence_2 <=> sentence 3: 0.3336274235605957
+
+    ```

one_exercise_per_file/week03/day05/ex04/readme.md

@@ -0,0 +1,13 @@
+# Exercise 4 Sentences' similarity 
+
+The goal of this exerice is to learn to compute the similarity between two sentences. As explained in the documentation: **The word embedding of a full sentence is simply the average over all different words**. This is how `similarity` works in SpaCy. This small use case is very interesting because if we build a corpus of sentences that express an intention as **buy shoes**, then we can detect this intention and use it to propose shoes advertisement for customers. The language model used in this exercise is `en_core_web_sm`.
+
+
+1. Compute the similarities (3 in total) between these sentences:
+
+    ```
+    sentence_1 = "I want to buy shoes"
+    sentence_2 = "I would love to purchase running shoes"
+    sentence_3 = "I am in my room"
+
+    ```

one_exercise_per_file/week03/day05/ex05/audit/readme.md

@@ -0,0 +1,34 @@
+1. This question is validated if the ouptut of the NER is
+
+    ```
+    Apple Inc. ORG
+    American NORP
+    Cupertino GPE
+    California GPE
+    Five CARDINAL
+    U.S. GPE
+    Amazon ORG
+    Google ORG
+    Microsoft ORG
+    Facebook ORG
+    Apple ORG
+    Steve Jobs PERSON
+    Steve Wozniak PERSON
+    Ronald Wayne PERSON
+    April 1976 DATE
+    Wozniak PERSON
+    Apple ORG
+    Wayne PERSON
+    12 days DATE
+    Apple Computer, Inc. ORG
+    January 1977 DATE
+    Apple ORG
+    Apple II ORG
+    ```
+2. This question is validated if the output shows that the first occurence of apple is not a named entity. In my case here is what the NER returns: 
+
+    ```
+    Paul 1 5 PERSON
+    Apple 50 55 ORG
+
+    ```

one_exercise_per_file/week03/day05/ex05/readme.md

@@ -0,0 +1,17 @@
+# Exercise 5: NER
+
+The goal of this exercise is to learn to use a Named entity recognition algorithm to detect entities. 
+
+```
+Apple Inc. is an American multinational technology company headquartered in Cupertino, California, that designs, develops, and sells consumer electronics, computer software, and online services. It is considered one of the Big Five companies in the U.S. information technology industry, along with Amazon, Google, Microsoft, and Facebook.
+Apple was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976 to develop and sell Wozniak's Apple I personal computer, though Wayne sold his share back within 12 days. It was incorporated as Apple Computer, Inc., in January 1977, and sales of its computers, including the Apple I and Apple II, grew quickly.
+ ```
+
+1. Extract all named entities in the text as well as the label of the named entity.
+
+2. The NER is also useful to remove ambigous entities. From a conceptual standpoint, disambiguation is the process of determining the most probable meaning of a specific phrase. For example in the sentence below, the word `apple` is present twice in the sentence. The first time to mention the fruit and the second to mention a company. Run the NER on this sentence and print the named entity, the `start_char`, the `end_char` and the label of the named entity. 
+
+```
+Paul eats an apple while watching a movie on his Apple device.
+```
+https://en.wikipedia.org/wiki/Named-entity_recognition

one_exercise_per_file/week03/day05/ex06/audit/readme.md

@@ -0,0 +1,18 @@
+1. This question is validated if the sentences outputed are: 
+
+    ```
+    INFO:  Bezos PROPN NNP
+    Sentence:  Amazon (AMZN) enters 2021 with plenty of big opportunities, but is losing its lauded Chief Executive Jeff Bezos, who announced his plan to step aside in the third quarter.
+
+
+    INFO:  Bezos PROPN NNP
+    Sentence:  Bezos will hand off his role as chief executive to Andy Jassy, the CEO of its cloud computing unit.
+
+
+    INFO:  Bezos PROPN NNP
+    Sentence:  He's not leaving, as Bezos will transition to the role of Executive Chairman and remain active.
+
+
+    INFO:  Bezos PROPN NNP
+    Sentence:  "When you look at our financial results, what you're actually seeing are the long-run cumulative results of invention," Bezos said in written remarks with the Amazon earnings release.
+    ```

one_exercise_per_file/week03/day05/ex06/readme.md

@@ -0,0 +1,18 @@
+# Exercise 6 Part-of-speech tags 
+
+The goal od this exercise is to learn to use the Part-of-speech tags (**POS TAG**) using Spacy. As explained in wikipedia, the POS TAG is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
+
+Example
+
+The sentence: **"Heat water in a large vessel"** is tagged this way after the POS TAG: 
+- heat verb (noun)
+- water noun (verb)
+- in prep (noun, adv)
+- a det (noun)
+- large adj (noun)
+- vessel noun
+
+
+The data `news_amazon.txt` used is a news paper about Amazon. 
+
+1. Return all sentences mentioning **Bezos** as a NNP (tag). 

one_exercise_per_file/week03/day05/readme.md

@@ -0,0 +1,20 @@
+# W3D2  Piscine AI - Data Science 
+
+
+# Table of Contents:
+
+
+# Introduction
+
+Embeddings ... 
+
+Library: Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start.
+
+There are many type of language models pre-trained in Spacy. Each has its specificities depending on the hypothesis taken. 
+## Historical
+
+
+
+## Rules
+
+## Ressources 

one_md_per_day_format/piscine/Week3/w3day05.md

@@ -122,7 +122,7 @@ The goal of this exerice is to learn to compute the similarity between two sente
 
 ## Correction 
 
-2. This question is validated if the similarities between the sentences are: 
+1. This question is validated if the similarities between the sentences are: 
 
     ```
     sentence_1 <=> sentence 2 : 0.7073220863266589