Training a Neural Network for Word Embedding - keras

Question

Attached is the link file for Entities. I want to train a Neural Network to represent each entity into a vector. Attach is my code for training
import pandas as pd
import numpy as np
from numpy import array
from keras.preprocessing.text import one_hot
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Input
from keras.layers.embeddings import Embedding
from sklearn.model_selection import train_test_split
file_path = '/content/drive/My Drive/Colab Notebooks/Deep Learning/NLP/Data/entities.txt'
df = pd.read_csv(file_path, delimiter = '\t', engine='python', quoting = 3, header = None)
df.columns = ['Entity']
Entity = df['Entity']
X_train, X_test = train_test_split(Entity, test_size = 0.10)
print('Total Entities: {}'.format(len(Entity)))
print('Training Entities: {}'.format(len(X_train)))
print('Test Entities: {}'.format(len(X_test)))
vocab_size = len(Entity)
X_train_encode = [one_hot(d, vocab_size,lower=True, split=' ') for d in X_train]
X_test_encode = [one_hot(d, vocab_size,lower=True, split=' ') for d in X_test]
model = Sequential()
model.add(Embedding(input_length=1,input_dim=vocab_size, output_dim=100))
model.add(Flatten())
model.add(Dense(vocab_size, activation='softmax'))
model.compile(optimizer='adam', loss='mse', metrics=['acc'])
print(model.summary())
model.fit(X_train_encode, X_train_encode, epochs=20, batch_size=1000, verbose=1)
The following error encountered when I am trying to execute the code.
Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 array(s), but instead got the following list of 34826 arrays:

Answer 1

You are passing list of numpy arrays for model.fit. The following code produces list of arrays for x_train_encode and X_test_encode.
X_train_encode = [one_hot(d, vocab_size,lower=True, split=' ') for d in X_train]
X_test_encode = [one_hot(d, vocab_size,lower=True, split=' ') for d in X_test]
Change these lists into numpy array when passing to model.fit method.
X_train_encode = np.array(X_train_encode)
X_test_encode = np.array(X_test_encode)
And I don't see the need to one_hot encode the X_train and X_test, embedding layer expects integer(in your case word indexes) not one hot encoded value of the the words' indexes. So if X_train and X_test are array of the indexes of the words then you can directly feed this into the model.fit method.
EDIT:
Currently 'mse' loss is being used. Since the last layer is softmax layer cross entropy loss is more applicable here. And also the outputs are integer values of a class(words) sparse categorical should be used for loss.
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])