BERT get sentence embedding

I am replicating code from this page. I have downloaded the BERT model to my local system and getting sentence embedding.

I have around 500,000 sentences for which I need sentence embedding and it is taking a lot of time.

1. Is there a way to expedite the process?
2. Would sending batches of sentences rather than one sentence at a time help?

.

#!pip install transformers
import torch
import transformers
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased',
                                  output_hidden_states = True, # Whether the model returns all hidden-states.
                                  )

# Put the model in "evaluation" mode, meaning feed-forward operation.
model.eval()

corpa=["i am a boy","i live in a city"]



storage=[]#list to store all embeddings

for text in corpa:
    # Add the special tokens.
    marked_text = "[CLS] " + text + " [SEP]"

    # Split the sentence into tokens.
    tokenized_text = tokenizer.tokenize(marked_text)

    # Map the token strings to their vocabulary indeces.
    indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)

    segments_ids = [1] * len(tokenized_text)

    tokens_tensor = torch.tensor([indexed_tokens])
    segments_tensors = torch.tensor([segments_ids])

    # Run the text through BERT, and collect all of the hidden states produced
    # from all 12 layers. 
    with torch.no_grad():

        outputs = model(tokens_tensor, segments_tensors)

        # Evaluating the model will return a different number of objects based on 
        # how it's  configured in the `from_pretrained` call earlier. In this case, 
        # becase we set `output_hidden_states = True`, the third item will be the 
        # hidden states from all layers. See the documentation for more details:
        # https://huggingface.co/transformers/model_doc/bert.html#bertmodel
        hidden_states = outputs[2]


    # `hidden_states` has shape [13 x 1 x 22 x 768]

    # `token_vecs` is a tensor with shape [22 x 768]
    token_vecs = hidden_states[-2][0]

    # Calculate the average of all 22 token vectors.
    sentence_embedding = torch.mean(token_vecs, dim=0)

    storage.append((text,sentence_embedding))

######update 1

I modified my code based upon the answer provided. It is not doing full batch processing

#!pip install transformers
import torch
import transformers
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased',
                                  output_hidden_states = True, # Whether the model returns all hidden-states.
                                  )

# Put the model in "evaluation" mode, meaning feed-forward operation.
model.eval()

batch_sentences = ["Hello I'm a single sentence",
                    "And another sentence",
                    "And the very very last one"]
encoded_inputs = tokenizer(batch_sentences)


storage=[]#list to store all embeddings
for i,text in enumerate(encoded_inputs['input_ids']):
    
    tokens_tensor = torch.tensor([encoded_inputs['input_ids'][i]])
    segments_tensors = torch.tensor([encoded_inputs['attention_mask'][i]])
    print (tokens_tensor)
    print (segments_tensors)

    # Run the text through BERT, and collect all of the hidden states produced
    # from all 12 layers. 
    with torch.no_grad():

        outputs = model(tokens_tensor, segments_tensors)

        # Evaluating the model will return a different number of objects based on 
        # how it's  configured in the `from_pretrained` call earlier. In this case, 
        # becase we set `output_hidden_states = True`, the third item will be the 
        # hidden states from all layers. See the documentation for more details:
        # https://huggingface.co/transformers/model_doc/bert.html#bertmodel
        hidden_states = outputs[2]


    # `hidden_states` has shape [13 x 1 x 22 x 768]

    # `token_vecs` is a tensor with shape [22 x 768]
    token_vecs = hidden_states[-2][0]

    # Calculate the average of all 22 token vectors.
    sentence_embedding = torch.mean(token_vecs, dim=0)
    print (sentence_embedding[:10])
    storage.append((text,sentence_embedding))

I could update first 2 lines from the for loop to below. But they work only if all sentences have same length after tokenization

tokens_tensor = torch.tensor([encoded_inputs['input_ids']])
segments_tensors = torch.tensor([encoded_inputs['attention_mask']])

moreover in that case outputs = model(tokens_tensor, segments_tensors) fails.

How could I fully perform batch processing in such case?

from BERT get sentence embedding