from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    """
    Mean pooling to get sentence embeddings. See:
    https://huggingface.co/sentence-transformers/paraphrase-distilroberta-base-v1
    """
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) # Sum columns
    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
    return sum_embeddings / sum_mask
    
# Fetch the model & tokenizer from transformers library
model_name = 'sentence-transformers/stsb-roberta-large'
model = AutoModel.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Tokenize input
encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=512, return_tensors="pt")    

# Create word embeddings
model_output = model(**encoded_input)

# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']).detach().numpy()