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import streamlit as st | |
import pandas as pd | |
#from streamlit import cli as stcli | |
from streamlit.web import cli as stcli | |
from streamlit import runtime | |
from transformers import pipeline | |
from sentence_transformers import SentenceTransformer, util | |
import sys | |
HISTORY_WEIGHT = 80 # set history weight (if found any keyword from history, it will priorities based on its weight) | |
def get_model(model): | |
return pipeline("fill-mask", model=model, top_k=5)#s5t the maximum of tokens to be retrieved after each inference to model | |
def hash_func(inp): | |
return True | |
def loading_models(model='roberta-base'): | |
return get_model(model), SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')#'all-mpnet-base-v2')#'all-MiniLM-L6-v2') | |
def infer(text): | |
# global nlp | |
return nlp(text+' '+nlp.tokenizer.mask_token) | |
def sim(predicted_seq, sem_list): | |
return semantic_model.encode(predicted_seq, convert_to_tensor=True), \ | |
semantic_model.encode(sem_list, convert_to_tensor=True) | |
def main(text,semantic_text,history_keyword_text): | |
global semantic_model, data_load_state | |
data_load_state.text('Inference from model...') | |
result = infer(text) | |
sem_list=[semantic_text.strip()] | |
data_load_state.text('Checking similarity...') | |
if len(semantic_text): | |
predicted_seq=[rec['sequence'] for rec in result] | |
predicted_embeddings, semantic_history_embeddings = sim(predicted_seq, sem_list) | |
cosine_scores = util.cos_sim(predicted_embeddings, semantic_history_embeddings) | |
data_load_state.text('similarity check completed...') | |
for index, r in enumerate(result): | |
if len(semantic_text): | |
if len(r['token_str'])>2: #skip spcial chars such as "?" | |
result[index]['score']+=float(sum(cosine_scores[index]))*HISTORY_WEIGHT | |
if r['token_str'].lower().strip() in history_keyword_text.lower().strip() and len(r['token_str'].lower().strip())>1: | |
#found from history, then increase the score of tokens | |
result[index]['score']*=HISTORY_WEIGHT | |
data_load_state.text('Score updated...') | |
#sort the results | |
df=pd.DataFrame(result).sort_values(by='score', ascending=False) | |
return df | |
if __name__ == '__main__': | |
#if st._is_running_with_streamlit: | |
if runtime.exists(): | |
st.markdown(""" | |
# Auto-Complete | |
This is an example of an auto-complete approach where the next token suggested based on users's history | |
Keyword match & Semantic similarity of users's history (log). | |
The next token is predicted per probability and a weight if it is appeared in keyword user's history or | |
there is a similarity to semantic user's history. | |
## Source | |
Forked from **[mbahrami/Auto-Complete_Semantic](https://huggingface.co/spaces/mbahrami/Auto-Complete_Semantic)** with *[osanseviero/fork_a_repo](https://huggingface.co/spaces/osanseviero/fork_a_repo)*. | |
## Disclaimer | |
Additionally, we include facebook/xlm-v-base model (it includes Guarani during pre-training), | |
for comparison reasons. | |
""") | |
history_keyword_text = st.text_input("Enter users's history <Keywords Match> (optional, i.e., 'Premio Cervantes')", value="") | |
semantic_text = st.text_input("Enter users's history <Semantic> (optional, i.e., 'hai')", value="hai") | |
text = st.text_input("Enter a text for auto completion...", value="Augusto Roa Bastos ha'e kuimba'e arandu") | |
model = st.selectbox("Choose a model", | |
["mmaguero/gn-bert-tiny-cased", "mmaguero/gn-bert-small-cased", | |
"mmaguero/gn-bert-base-cased", "mmaguero/gn-bert-large-cased", | |
"mmaguero/multilingual-bert-gn-base-cased", "mmaguero/beto-gn-base-cased", | |
"facebook/xlm-v-base"]) | |
data_load_state = st.text('1.Loading model ...') | |
nlp, semantic_model = loading_models(model) | |
df=main(text,semantic_text,history_keyword_text) | |
#show the results as a table | |
st.table(df) | |
data_load_state.text('') | |
else: | |
sys.argv = ['streamlit', 'run', sys.argv[0]] | |
sys.exit(stcli.main()) |