How to get Non-contextual Word Embeddings in BERT?
I am already installed BERT, But I don't know how to get Non-contextual word embeddings.
For example:

input: 'Apple'
output: [1,2,23,2,13,...] #embedding of 'Apple'



How can i get these word embeddings?
Thank you.
I search some method, but no blogs have written the way.

Sloved.
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained(&quot;bert-base-uncased&quot;)

model = AutoModel.from_pretrained(&quot;bert-base-uncased&quot;)

# get the word embedding from BERT
def get_word_embedding(word:str):
    input_ids = torch.tensor(tokenizer.encode(word)).unsqueeze(0)  # Batch size 1
    # print(input_ids)
    outputs = model(input_ids)
    last_hidden_states = outputs[0]  # The last hidden-state is the first element of the output tuple
    # output[0] is token vector
    # output[1] is the mean pooling of all hidden states
    return last_hidden_states[0][1]



