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import streamlit as st
from transformers import AutoTokenizer, AutoModel
import transformers
import torch
from sentence_transformers import util
# explicit no operation hash functions defined, because raw sentences, embedding, model and tokenizer are not going to change
@st.cache(hash_funcs={list: lambda _: None})
def load_raw_sentences(filename):
with open(filename) as f:
return f.readlines()
@st.cache(hash_funcs={torch.Tensor: lambda _: None})
def load_embeddings(filename):
with open(filename) as f:
return torch.load(filename,map_location=torch.device('cpu') )
#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, all_sentences, 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):
if idx < len(all_sentences):
pairs.append({'score': '{:.4f}'.format(score), 'text': all_sentences[idx]})
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
# explicit no operation hash function, because model and tokenizer are not going to change
@st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None, transformers.models.bert.modeling_bert.BertModel: lambda _: None})
def load_model_and_tokenizer():
multilingual_checkpoint = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
tokenizer = AutoTokenizer.from_pretrained(multilingual_checkpoint)
model = AutoModel.from_pretrained(multilingual_checkpoint)
print(type(tokenizer))
print(type(model))
return model, tokenizer
model,tokenizer = load_model_and_tokenizer();
raw_text_file = 'data/data/joint_text_filtered.md'
all_sentences = load_raw_sentences(raw_text_file)
embeddings_file = 'data/multibert_embedded.pt'
all_embeddings = load_embeddings(embeddings_file)
st.header('RF szöveg kereső')
st.caption('[HU] Adjon meg egy tetszőleges kifejezést és a rendszer visszaadja az 5 hozzá legjobban hasonlító szöveget')
text_area_input_query = st.text_area('[HU] Beviteli mező - [EN] Query input',value='Mikor van a leadási hataridő?')
if text_area_input_query:
query_embedding = calculateEmbeddings([text_area_input_query],tokenizer,model)
top_pairs = findTopKMostSimilar(query_embedding, all_embeddings, all_sentences, 5)
st.json(top_pairs)
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