ValueError: Layer weight shape (775, 768) not compatible with provided weight shape (775, 100, 768)

Question

20220422: I've read some tutorials on other embeddings and now I guess my question is actually how to create an embedding matrix using Bert word embeddings?

20220420: I want to use BERT for only word embedding and then use LSTM to do text classification. Now, I used 'Bert-as-service' to get the bert word embedding, its shape is (775, 100, 768), that is (batch_size, maxlen, embeded_size). But apparently, it's not compatible with the layer weight.

I used the bert word embedding as the weight in the embedding layer (based on https://www.kaggle.com/code/shujian/blend-of-lstm-and-cnn-with-4-embeddings-1200d/notebook

But I'm new to python, and don't know how to convert the shape of bert word embeddings to (max_feature, embeded_size), so that I can concatenate bert word embeddings with other embeddings like GloVe like this tutorial.

train_trail = pd.read_csv('C:/Users/Ariel/trial/train.csv')
test_trail = pd.read_csv('C:/Users/Ariel/trial/test.csv')

embed_size = 768 # how big is each word vector
max_features = 775 # how many unique words to use (i.e num rows in embedding vector)

####!!!change later!!!
maxlen = 100 # max number of words in a question to use 

## split to train and val
from sklearn.model_selection import train_test_split
train_trail, val_trial = train_test_split(train_trail, test_size=0.1, random_state=2018)

train_trail_X = train_trail["processed_text"]
val_trial_X = val_trial ["processed_text"]
test_trail_X = test_trail["processed_text"]



## Tokenize the sentences
tokenizer = Tokenizer(num_words=max_features)
tokenizer.fit_on_texts(list(train_trail_X))
train_trail_X = tokenizer.texts_to_sequences(train_trail_X)
val_trial_X = tokenizer.texts_to_sequences(val_trial_X)
test_trail_X = tokenizer.texts_to_sequences(test_trail_X)

## Pad the sentences 
train_trail_X = pad_sequences(train_trail_X, maxlen=maxlen)
val_trial_X = pad_sequences(val_trial_X, maxlen=maxlen)
test_trail_X = pad_sequences(test_trail_X, maxlen=maxlen)

## Get the target values
train_trail_y = train_trail['label'].values
val_trial_y = val_trial['label'].values

from bert_serving.client import BertClient
bc = BertClient()
texts = train_trail['processed_text'].tolist()
texts2 = [s.split() for s in texts]
vecs = bc.encode(texts2, is_tokenized=True)

The model

#Building sentiment model
def getModel():
    embedding_layer = Embedding(input_dim = max_features,
                                output_dim = embed_size,
                                weights= [vec_squeeze],
                                trainable=False)

    model = Sequential([
        embedding_layer,
        Bidirectional(LSTM(100, dropout=0.3, return_sequences=True)),
        Bidirectional(LSTM(100, dropout=0.3, return_sequences=True)),
        Conv1D(100, 5, activation='relu'),
        GlobalMaxPool1D(),
        Dense(16, activation='relu'),
        Dense(1, activation='sigmoid'),
    ],
    name="Sentiment_Model")
    return model

training_model = getModel()
training_model.summary()

Then, the error occured:

training_model = getModel()

training_model.summary()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-102-fdbf69968d36> in <module>
----> 1 training_model = getModel()
      2 training_model.summary()

<ipython-input-101-d82f78e4293f> in getModel()
     15         Dense(1, activation='sigmoid'),
     16     ],
---> 17     name="Sentiment_Model")
     18     return model
     19 

D:\Anaconda3\envs\bertuse\lib\site-packages\keras\engine\sequential.py in __init__(self, layers, name)
     90         if layers:
     91             for layer in layers:
---> 92                 self.add(layer)
     93 
     94     @property

D:\Anaconda3\envs\bertuse\lib\site-packages\keras\engine\sequential.py in add(self, layer)
    164                     # and create the node connecting the current layer
    165                     # to the input layer we just created.
--> 166                     layer(x)
    167                     set_inputs = True
    168                 else:

D:\Anaconda3\envs\bertuse\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
    437                 # Load weights that were specified at layer instantiation.
    438                 if self._initial_weights is not None:
--> 439                     self.set_weights(self._initial_weights)
    440 
    441             # Raise exceptions in case the input is not compatible

D:\Anaconda3\envs\bertuse\lib\site-packages\keras\engine\base_layer.py in set_weights(self, weights)
   1070                                  str(pv.shape) +
   1071                                  ' not compatible with '
-> 1072                                  'provided weight shape ' + str(w.shape))
   1073             weight_value_tuples.append((p, w))
   1074         K.batch_set_value(weight_value_tuples)

ValueError: Layer weight shape (775, 768) not compatible with provided weight shape (775, 100, 768)

I'm having a hard time on this task so any help would be greatly appreciated. Many thanks in advance!