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from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer
from sklearn.metrics.pairwise import cosine_similarity
import streamlit as st
import torch
model_checkpoint = "vives/distilbert-base-uncased-finetuned-imdb-accelerate"
model = AutoModelForMaskedLM.from_pretrained(model_checkpoint,output_hidden_states=True)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
text1 = st.text_area("Enter first sentence")
text2 = st.text_area("Enter second sentence")
def concat_tokens(t1,t2):
tokens = {'input_ids': [], 'attention_mask': []}
sentences = [t1, t2]
for sentence in sentences:
# encode each sentence and append to dictionary
new_tokens = tokenizer.encode_plus(sentence, max_length=128,
truncation=True, padding='max_length',
return_tensors='pt')
tokens['input_ids'].append(new_tokens['input_ids'][0])
tokens['attention_mask'].append(new_tokens['attention_mask'][0])
# reformat list of tensors into single tensor
tokens['input_ids'] = torch.stack(tokens['input_ids'])
tokens['attention_mask'] = torch.stack(tokens['attention_mask'])
return tokens
def pool_embeddings(out, tok):
embeddings = out["hidden_states"][-1]
attention_mask = tok['attention_mask']
mask = attention_mask.unsqueeze(-1).expand(embeddings.size()).float()
masked_embeddings = embeddings * mask
summed = torch.sum(masked_embeddings, 1)
summed_mask = torch.clamp(mask.sum(1), min=1e-9)
mean_pooled = summed / summed_mask
return mean_pooled
if text1 and text2:
tokens = concat_tokens(text1,text2)
outputs = model(**tokens)
mean_pooled = pool_embeddings(outputs,tokens).detach().numpy()
st.write(cosine_similarity(
[mean_pooled[0]],
mean_pooled[1:]
))
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