Update README.md

Author: pdrm83

README.md

@@ -1,8 +1,7 @@
 [![license](https://img.shields.io/badge/license-MIT-success)](https://github.com/pdrm83/Sent2Vec/blob/master/LICENSE.md)
 [![doc](https://img.shields.io/badge/docs-Medium-blue)](https://towardsdatascience.com/how-to-compute-sentence-similarity-using-bert-and-word2vec-ab0663a5d64)
 
-# Sent2Vec 
-## How to Compute Sentence Embedding Fast and Flexible
+# Sent2Vec - How to Compute Sentence Embedding Fast and Flexible
 
 In the past, we mostly encode text data using, for example, one-hot, term frequency, or TF-IDF (normalized term 
 frequency). There are many challenges to these techniques. In recent years, the latest advancements give us the
@@ -14,7 +13,7 @@ flexible sentence embedding library is needed to prototype fast and contextualiz
 package gives you the opportunity to do so. You currently have access to the standard encoders. More advanced 
 techniques will be added in the later releases. Hope you can use this library in your exciting NLP projects.
 
-## Install
+## 🔓 Install
 The `sent2vec` is developed to help you prototype faster. That is why it has many dependencies on other libraries. The 
 module requires the following libraries:
 
@@ -29,24 +28,27 @@ Then, it can be installed using pip:
 pip3 install sent2vec
 ```
 
-## Documentation
-
-*class* **sent2vec.vectorizer.Vectorizer**(pretrained_weights='distilbert-base-uncased', ensemble_method='average')
+## 📚 Documentation
+```python
+class sent2vec.vectorizer.Vectorizer(pretrained_weights='distilbert-base-uncased', ensemble_method='average')
+```
 
 ### **Parameters**
 
-- **pretrained_weights**: str, *default*='distilbert-base-uncased' - If the string does not include an extension .txt, .gz or .bin, then Bert vectorizer is loaded using the specified weights. *Example: pass 'distilbert-base-multilingual-cased' to load Bert base multilingual model.* <br/> To load word2vec vectorizer pass a valid path to the weights file (.txt, .gz or .bin). *Example: pass 'glove-wiki-gigaword-300.gz' to load the Wiki vectors (when saved in the same folder you are running the code).*
-- **ensemble_method**: str, *default*='average' - How word vectors are computed into sentece vectors.
+- `pretrained_weights`: str, *default*='distilbert-base-uncased' - How word embeddings are computed. => You can pass other BERT models into this parameter such as base multilingual model, i.e., `distilbert-base-multilingual-cased`. Basically, the vectorizer uses the BERT vectorizer with specified weights unless you pass a file path with extensions `.txt`, `.gz` or `.bin` to this parameter. In that case, the Gensim library will load the provided word2ved model (pretrained weights). For example, you can pass `glove-wiki-gigaword-300.gz` to load the Wiki vectors (when saved in the same folder you are running the code).
+- `ensemble_method`: str, *default*='average' - How word vectors are aggregated into sentece vectors. 
 
 ### **Methods**
-
+```python
 run(sentences, remove_stop_words = ['not'], add_stop_words = [])
-- **sentences**: list, - List of sentences.
-- **remove_stop_words**: list, *default*=['not'] - When using sent2vec, list of words to remove from *stop words* when splitting sentences.
-- **add_stop_words**: list, *default*=[] - When using sent2vec, list of words to add to *stop words* when splitting sentences.
+```
+- `sentences`: list, - List of sentences.
+- `remove_stop_words`: list, *default*=['not'] - When using sent2vec, list of words to remove from *stop words* when splitting sentences.
+- `add_stop_words`: list, *default*=[] - When using sent2vec, list of words to add to *stop words* when splitting sentences.
 
-## Usage
-If you want to use the `BERT` language model (more specifically, `distilbert-base-uncased`) to encode sentences for 
+## 🧰 Usage
+### 1. How to use BERT model?
+If you want to use the BERT language model (more specifically, `distilbert-base-uncased`) to encode sentences for 
 downstream applications, you must use the code below. 
 ```python
 from sent2vec.vectorizer import Vectorizer
@@ -60,13 +62,6 @@ vectorizer = Vectorizer()
 vectorizer.run(sentences)
 vectors = vectorizer.vectors
 ```
-Default Vectorizer weights are `distilbert-base-uncased` but it's possible to pass the argument `pretrained_weights` to chose another `BERT` model.
-
-For example, to load `BERT base multilingual model`:
-
-```python
-vectorizer = Vectorizer(pretrained_weights='distilbert-base-multilingual-cased')
-```
 
 Now it's possible to compute distance among sentences by using their vectors. In the example, as expected, the distance between
 `vectors[0]` and `vectors[1]` is less than the distance between `vectors[0]` and `vectors[2]`.
@@ -80,10 +75,15 @@ print('dist_1: {0}, dist_2: {1}'.format(dist_1, dist_2))
 assert dist_1 < dist_2
 # dist_1: 0.043, dist_2: 0.192
 ```
+Note: The default vectorizer for the BERT model is `distilbert-base-uncased` but it's possible to pass the argument `pretrained_weights` to chose another BERT model. For example, you can use the code below to load the base multilingual model.
 
-If you want to use a word2vec approach instead, you must pass a valid path to the model weights. Under the hood the sentences will be splitted into lists of words using the `sent2words` method from the `Splitter` class. It is possible to customize the list of stop-words by adding or removing to/from the default list. Two additional arguments (both lists) must be passed when the vectorizer's method .run is called: `remove_stop_words` and `add_stop_words`. 
+```python
+vectorizer = Vectorizer(pretrained_weights='distilbert-base-multilingual-cased')
+```
+### 2. How to use Word2Vec model?
+If you want to use a Word2Vec approach instead, you must pass a valid path to the model weights. Under the hood, the sentences will be split into lists of words using the `sent2words` method from the `Splitter` class. It is possible to customize the list of stop-words by adding or removing to/from the default list. Two additional arguments (both lists) must be passed when the vectorizer's method .run is called: `remove_stop_words` and `add_stop_words`. 
 
-NOTE: When you extract the most important words in sentences, by default `Vectorizer` computes the sentence embeddings using the average of vectors corresponding to the remaining words. 
+NOTE: The default method to computes the sentence embeddings after extracting list of vectors is average of vectors corresponding to the remaining words. 
 
 ```python
 from sent2vec.vectorizer import Vectorizer
@@ -98,4 +98,4 @@ vectorizer.run(sentences, remove_stop_words=['not'], add_stop_words=[])
 vectors = vectorizer.vectors
 ```
 
-And, that's pretty much it!
+And, that's pretty much it!