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): fitered_data = [] with open(filename) as f: raw_data = f.readlines() for line in raw_data: if len(line)>1: filtered_data.append(line) return filtered_data @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' #alternative: SZTAKI-HLT/hubert-base-cc tokenizer = AutoTokenizer.from_pretrained(multilingual_checkpoint) model = AutoModel.from_pretrained(multilingual_checkpoint) print(type(tokenizer)) print(type(model)) return model, tokenizer @st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None, transformers.models.bert.modeling_bert.BertModel: lambda _: None}) def load_hu_model_and_tokenizer(): multilingual_checkpoint = 'SZTAKI-HLT/hubert-base-cc' #alternative: SZTAKI-HLT/hubert-base-cc 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(); model_hu,tokenizer_hu = load_hu_model_and_tokenizer(); raw_text_file = 'joint_text_filtered.md' all_sentences = load_raw_sentences(raw_text_file) embeddings_file = 'multibert_embedded.pt' #alternative: hunbert_embedded.pt all_embeddings = load_embeddings(embeddings_file) embeddings_file_hu = 'hunbert_embedded.pt' all_embeddings_hu = load_embeddings(embeddings_file_hu) 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 határidő?') 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) query_embedding = calculateEmbeddings([text_area_input_query],tokenizer_hu,model_hu) top_pairs = findTopKMostSimilar(query_embedding, all_embeddings_hu, all_sentences, 5) st.json(top_pairs)