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from transformers import AutoTokenizer, AutoModel
import torch
from sentence_transformers import util
def load_raw_sentences(filename):
with open(filename) as f:
return f.readlines()
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def findTopKMostSimilar(query_embedding, embeddings, k):
cosine_scores = util.pytorch_cos_sim(query_embedding, embeddings)
cosine_scores_list = cosine_scores.squeeze().tolist()
pairs = []
for idx,score in enumerate(cosine_scores_list):
pairs.append({'index': idx, 'score': score})
pairs = sorted(pairs, key=lambda x: x['score'], reverse=True)
return pairs[0:k]
def calculateEmbeddings(sentences,tokenizer,model):
tokenized_sentences = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
with torch.no_grad():
model_output = model(**tokenized_sentences)
sentence_embeddings = mean_pooling(model_output, tokenized_sentences['attention_mask'])
return sentence_embeddings
multilingual_checkpoint = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
tokenizer = AutoTokenizer.from_pretrained(multilingual_checkpoint)
model = AutoModel.from_pretrained(multilingual_checkpoint)
raw_text_file = 'data/preprocessed/shortened_abstracts_hu_2021_09_01.txt'
embeddings_file = 'data/preprocessed/shortened_abstracts_hu_2021_09_01_embedded.pt'
all_sentences = load_raw_sentences(raw_text_file)
all_embeddings = torch.load(embeddings_file,map_location=torch.device('cpu') )
query = ''
while query != 'exit':
query = input("Enter your query: ")
query_embedding = calculateEmbeddings([query],tokenizer,model)
top_pairs = findTopKMostSimilar(query_embedding, all_embeddings, 5)
for pair in top_pairs:
i = pair['index']
score = pair['score']
print("{} \t\t Score: {:.4f}".format(all_sentences[i], score))
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