Importing the WordEmbedder can simplify a lot of necessary preprocessing tasks that need to be done when working on NLP Projects.

Tasks like:

1. Getting embeddings of words in the dataset

2. Generating vocabulary for your tokenized sentences

3. Converting tokenized sentences in number sequences

4. Retrieving the tokenized sentences back from the number sequences

5. Generate an embedding matrix for the vocabulary that is demanded by the Embedding Layer from tensorflow.keras.layers as the weights parameter when using pre-trained embeddings.

consume a lot of our time and lines of code.

All these tasks can individually be done in a single line of code by importing the WordEmbedder script.

Other than the above mentioned tasks there are few other features that the script provides as well:

1. Getting K most similar words to a given word
2. Getting K top words that satisfy an analogy
3. Getting cosine similarity between two words
4. Getting the cosine similarity score for a 4-word analogy

The notebook how_to_use_WordEmbedder.ipynb in the repository specifies all the details on how to use the WordEmbedder.

#### Initiating an object of Embedder class
emb = Embedder(dimensions=50) # dimensions can be 50, 100, 200 or 300 # This line take a while to execute when run for the first time because the embeddings are downloaded

#### This embedder maps words to their embeddings

embedder = emb.get_embedder()
print(embedder['beautiful']) 

#### Generating Vocabulary
vocab, vocab_dict = generate_vocabulary(tokenized_sentences) # To generate vocabulary for the tokenized sentences

#### Generating the embedding_matrix
embedding_matrix, id2token, token2id = emb.get_embedding_matrix(vocab, num_words=10)

#### Generating number sequences from tokenized sentences
num_sequences = token_seq_to_num_seq(tokenized_sentences, token2id, oov_token='<oov>')

#### Retrieving  tokenized sentences from number sequences
retrieved_tokenized_sequences = num_seq_to_token_seq(num_sequences, id2token)

Refer how_to_use_WordEmbedder.ipynb for the other features.

Open to contributions! :)