from transformers import pipeline, AutoTokenizer import pandas as pd import numpy as np import torch import streamlit as st USE_GPU = True if USE_GPU and torch.cuda.is_available(): device = torch.device("cuda:0") else: device = torch.device('cpu') MODEL_NAME_ENGLISH = "facebook/xlm-v-base" #SENTENCE_MODEL_NAME_ENGLISH = 'sentence-transformers/all-MiniLM-L6-v2' #WORD_MODEL_NAME_ENGLISH = 'vocab-transformers/distilbert-word2vec_256k-MLM_best' # chinese models MODEL_NAME_CHINESE = "IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece" WORD_PROBABILITY_THRESHOLD = 0.02 #WORD_PROBABILITY_THRESHOLD_ENGLISH = 0.02 #WORD_PROBABILITY_THRESHOLD_CHINESE = 0.02 TOP_K_WORDS = 10 ENGLISH_LANG = "English" CHINESE_LANG = "Chinese" CHINESE_WORDLIST = ['一定','一样','不得了','主观','从此','便于','俗话','倒霉','候选','充沛','分别','反倒','只好','同情','吹捧','咳嗽','围绕','如意','实行','将近','就职','应该','归还','当面','忘记','急忙','恢复','悲哀','感冒','成长','截至','打架','把握','报告','抱怨','担保','拒绝','拜访','拥护','拳头','拼搏','损坏','接待','握手','揭发','攀登','显示','普遍','未免','欣赏','正式','比如','流浪','涂抹','深刻','演绎','留念','瞻仰','确保','稍微','立刻','精心','结算','罕见','访问','请示','责怪','起初','转达','辅导','过瘾','运动','连忙','适合','遭受','重叠','镇静'] @st.cache_resource def get_model_chinese(): return pipeline("fill-mask", MODEL_NAME_CHINESE, device = device) @st.cache_resource def get_model_english(): return pipeline("fill-mask", MODEL_NAME_ENGLISH, device = device) @st.cache_data def get_wordlist_chinese(): return pd.read_csv('wordlist_chinese.csv') @st.cache_data def get_wordlist_english(): return pd.read_csv('wordlist_english.csv') def assess_chinese(word, sentence): print("Assessing English") if sentence.lower().find(word.lower()) == -1: print('Sentence does not contain the word!') return text = sentence.replace(word.lower(), "") top_k_prediction = mask_filler_chinese(text, top_k=TOP_K_WORDS) target_word_prediction = mask_filler_chinese(text, targets = word) score = target_word_prediction[0]['score'] # append the original word if its not found in the results top_k_prediction_filtered = [output for output in top_k_prediction if \ output['token_str'] == word] if len(top_k_prediction_filtered) == 0: top_k_prediction.extend(target_word_prediction) return top_k_prediction, score def assess_english(word, sentence): if sentence.lower().find(word.lower()) == -1: raise Exception("Sentence does not contain the target word") text = sentence.replace(word.lower(), "") top_k_prediction = mask_filler_english(text, top_k=TOP_K_WORDS) target_word_prediction = mask_filler_english(text, targets = chr(9601)+word) score = target_word_prediction[0]['score'] # append the original word if its not found in the results top_k_prediction_filtered = [output for output in top_k_prediction if \ output['token_str'] == word] if len(top_k_prediction_filtered) == 0: top_k_prediction.extend(target_word_prediction) return top_k_prediction, score def assess_sentence(language, word, sentence): if (language == ENGLISH_LANG): return assess_english(word, sentence) elif (language == CHINESE_LANG): return assess_chinese(word, sentence) def get_chinese_word(): include = (wordlist_chinese.assess == True) & (wordlist_chinese.Chinese.apply(len) == 2) possible_words = wordlist_chinese[include] word = possible_words.sample(1).iloc[0].Chinese test_words = CHINESE_WORDLIST word = np.random.choice(test_words) return word def get_english_word(): include = (wordlist_english.assess == True) possible_words = wordlist_english[include] word = possible_words.sample(1).iloc[0].word test_words = ["independent","satisfied","excited"] word = np.random.choice(test_words) return word def get_word(language): if (language == ENGLISH_LANG): return get_english_word() elif (language == CHINESE_LANG): return get_chinese_word() mask_filler_chinese = get_model_chinese() mask_filler_english = get_model_english() wordlist_chinese = get_wordlist_chinese() wordlist_english = get_wordlist_english() def highlight_given_word(row): color = '#ACE5EE' if row.Words == target_word else 'white' return [f'background-color:{color}'] * len(row) def get_top_5_results(top_k_prediction): predictions_df = pd.DataFrame(top_k_prediction) predictions_df = predictions_df.drop(columns=["token", "sequence"]) predictions_df = predictions_df.rename(columns={"score": "Probability", "token_str": "Words"}) if (predictions_df[:5].Words == target_word).sum() == 0: print("target word not in top 5") top_5_df = predictions_df[:5] target_word_df = predictions_df[(predictions_df.Words == target_word)] print(target_word_df) top_5_df = pd.concat([top_5_df, target_word_df]) else: top_5_df = predictions_df[:5] top_5_df['Probability'] = top_5_df['Probability'].apply(lambda x: f"{x:.2%}") return top_5_df #### Streamlit Page st.title("造句 Auto-marking Demo") language = st.radio("Select your language", (ENGLISH_LANG, CHINESE_LANG)) #st.info("You are practising on " + language) if 'target_word' not in st.session_state: st.session_state['target_word'] = get_word(language) target_word = st.session_state['target_word'] st.write("Target word: ", target_word) if st.button("Get new word"): st.session_state['target_word'] = get_word(language) st.experimental_rerun() st.subheader("Form your sentence and input below!") sentence = st.text_input('Enter your sentence here', placeholder="Enter your sentence here!") if st.button("Grade"): top_k_prediction, score = assess_sentence(language, target_word, sentence) with open('./result01.json', 'w') as outfile: outfile.write(str(top_k_prediction)) st.write(f"Probability: {score:.2%}") st.write(f"Target probability: {WORD_PROBABILITY_THRESHOLD:.2%}") predictions_df = get_top_5_results(top_k_prediction) df_style = predictions_df.style.apply(highlight_given_word, axis=1) if (score >= WORD_PROBABILITY_THRESHOLD): st.balloons() st.success("Yay good job! That's a great sentence 🕺 Practice again with other word", icon="✅") st.table(df_style) else: st.warning("Hmmm.. maybe try again?") st.table(df_style)