zemo3.py

import torch
from tokenizer import BertTokenizer
from torch import nn
from bert import BertModel

# Initialize the BERT tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Example sentence
sentences = [
    "She loves reading novels in her free time",
    "An apple a day keeps the doctor away",
    "If you can't explain it simply, you don't understand it well enough."
]

# Tokenize and encode the sentence
encoding = tokenizer.batch_encode_plus(
    sentences,
    max_length=512,
    padding='max_length',
    truncation=True,
    return_tensors='pt'
)

# Get the token IDs from the encoding
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']

model = BertModel.from_pretrained('bert-base-uncased')

assert isinstance(model, BertModel)
print(model.embed(input_ids).size())