from evaluation_utils import * import unicodedata as ud # pip install konlpy from konlpy.tag import Okt # pip install hausastemmer import hausastemmer # git clone https://github.com/aznlp-disc/stemmer.git, cp word.txt & suffix.txt. from stemmer.stemmer import Stemmer as AZStemmer from string import punctuation # pip install nlp-id from nlp_id.lemmatizer import Lemmatizer as IDLemmatizer # pip install hazm from hazm import Lemmatizer as PRLemmatizer # pip install qalsadi from qalsadi.lemmatizer import Lemmatizer as ARLeammatizer # pip install cltk from cltk import NLP # !pip install spark-nlp==5.3.3 pyspark==3.3.1 from sparknlp.base import * from sparknlp.annotator import * from sparknlp.pretrained import PretrainedPipeline import sparknlp from SUSTEM.SUSTEM_S import * import spacy # pip install jieba import jieba # git clone https://github.com/anoopkunchukuttan/indic_nlp_library.git & https://github.com/anoopkunchukuttan/indic_nlp_resources.git # The path to the local git repo for Indic NLP library INDIC_NLP_LIB_HOME=os.path.abspath("./indic_nlp_library") # The path to the local git repo for Indic NLP Resources INDIC_NLP_RESOURCES=os.path.abspath("./indic_nlp_resources") sys.path.append(INDIC_NLP_LIB_HOME) from indicnlp import common from indicnlp import loader from indicnlp.tokenize import indic_tokenize def lemma_check(answer,llm_response,nlp_pipeline,language='Korean'): if answer in llm_response or answer.replace('-',' ') in llm_response or answer.replace(' ','-') in llm_response: return True if language == 'Korean': okt = Okt() answer_tokens = okt.morphs(' '.join([w for w,p in okt.pos(answer) if p!='Josa']),stem=True) llm_tokens = okt.morphs(' '.join([w for w,p in okt.pos(llm_response) if p!='Josa']),stem=True) elif language == 'Hausa': answer_tokens = [hausastemmer.stem(term.strip('-')) for term in answer.split()] llm_tokens = [hausastemmer.stem(term.strip('-')) for term in llm_response.split()] elif language == 'Amharic': answer_tokens = [token.result if lemma.result.startswith('_') else lemma.result for token,lemma in zip(nlp_pipeline.fullAnnotate(answer)[0]['lemma'],nlp_pipeline.fullAnnotate(answer)[0]['token'])] llm_tokens = [token.result if lemma.result.startswith('_') else lemma.result for token,lemma in zip(nlp_pipeline.fullAnnotate(llm_response)[0]['lemma'],nlp_pipeline.fullAnnotate(llm_response)[0]['token'])] elif language == 'Azerbaijani': # Instantiate Stemmer object my_stemmer = AZStemmer() def stem_words(my_text): my_text=my_text.replace("İ", "I") my_text=my_text.replace("“", "") my_text=my_text.replace("”", "") my_text=my_text.replace("'", "") my_text=my_text.replace('"', "") my_text=my_text.split() my_words=[] for word in my_text: my_words.append(''.join(c for c in word if (c not in punctuation) or (c == '-'))) # Apply stemming to the list of words my_words = my_stemmer.stem_words(my_words) # Print words after stemming return my_words answer_tokens = stem_words(answer) llm_tokens = stem_words(llm_response) elif language == 'Indonesian': lemmatizer = IDLemmatizer() answer_tokens = lemmatizer.lemmatize(answer).split() llm_tokens = lemmatizer.lemmatize(llm_response).split() elif language == 'Persian': lemmatizer = PRLemmatizer() answer_tokens = [lemmatizer.lemmatize(term) for term in answer.split()] llm_tokens = [lemmatizer.lemmatize(term) for term in llm_response.split()] elif language == 'Arabic': lemmatizer = ARLeammatizer() answer_tokens = lemmatizer.lemmatize(answer) llm_tokens = lemmatizer.lemmatize(llm_response) elif language == 'Greek': cltk_nlp = NLP(language="grc", suppress_banner=True) answer_tokens = cltk_nlp.analyze(text=answer).lemmata llm_tokens = cltk_nlp.analyze(text=llm_response).lemmata elif language == 'Spanish': answer_tokens = [lemma.result for lemma in nlp_pipeline.fullAnnotate(answer)[0]['lemma']] llm_tokens = [lemma.result for lemma in nlp_pipeline.fullAnnotate(llm_response)[0]['lemma']] elif language == 'Sundanese': stemmer = EcsStemmer() answer_tokens = [stemmer.stemmingProcess(word.replace('(','').replace(')','')) for word in answer.split()] llm_tokens = [stemmer.stemmingProcess(word.replace('(','').replace(')','')) for word in llm_response.split()] elif language == 'English': answer_tokens = [token.lemma_ for token in nlp_pipeline(answer)] llm_tokens = [token.lemma_ for token in nlp_pipeline(llm_response)] elif language == 'Chinese': answer_tokens = list(jieba.cut(answer)) llm_tokens = list(jieba.cut(llm_response)) elif language == 'Assamese': common.set_resources_path(INDIC_NLP_RESOURCES) loader.load() answer_tokens = indic_tokenize.trivial_tokenize(answer) llm_tokens = indic_tokenize.trivial_tokenize(llm_response) d = {ord('\N{COMBINING ACUTE ACCENT}'):None} answer_tokens = [ud.normalize('NFD',term).translate(d).lower() for term in answer_tokens if term not in punctuation and term != ''] llm_tokens = [ud.normalize('NFD',term).translate(d).lower() for term in llm_tokens if term not in punctuation and term != ''] for a in answer_tokens: if a not in llm_tokens: return False return True def hard_exact_match(annotation_dict,response_df,id_col,r_col,annotations_key='annotations'): binary_score = 0 weight_score = 0 for qid,data in annotation_dict.items(): llm_response = get_llm_response_by_id(response_df,qid,id_col,r_col) if llm_response and data[annotations_key]: max_vote = max(list(data[annotations_key].values())) for k,v in sorted(data[annotations_key].items(), key=lambda item: item[1],reverse=True): if k == llm_response: binary_score += 1 weight_score += v/max_vote break binary_score = binary_score / len(annotation_dict) * 100 weight_score = weight_score / len(annotation_dict) * 100 print(binary_score) print(weight_score) return binary_score, weight_score def soft_exact_match(country,language,annotation_dict,response_df,id_col,r_col,annotations_key='aggregated_answers'): binary_score = 0 weight_score = 0 valid_question_cnt = 0 if language == 'Spanish': spark = sparknlp.start() document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutputCol("token") lemmatizer = LemmatizerModel.pretrained("lemma", "es") \ .setInputCols(["token"]) \ .setOutputCol("lemma") nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, lemmatizer]) nlpPipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF('text'))) elif language == 'Amharic': spark = sparknlp.start() document_assembler = DocumentAssembler() \ .setInputCol("text") \ .setOutputCol("document") tokenizer = Tokenizer() \ .setInputCols(["document"]) \ .setOutputCol("token") lemmatizer = LemmatizerModel.pretrained("lemma", "am") \ .setInputCols(["token"]) \ .setOutputCol("lemma") nlp_pipeline = Pipeline(stages=[document_assembler,tokenizer,lemmatizer]) nlpPipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF('text'))) else: nlpPipeline = None en_lemmatizer = spacy.load("en_core_web_sm") response_df['binary_score'] = [None]*response_df.shape[0] response_df['weight_score'] = [None]*response_df.shape[0] pb = tqdm(annotation_dict.items(),total=len(annotation_dict)) for qid,data in pb: pb.set_description(qid) if data['idks']['no-answer']+data['idks']['not-applicable'] >= 3 or data['idks']['idk']>=5 or len(data[annotations_key])==0: continue valid_question_cnt += 1 llm_response = get_llm_response_by_id(response_df,qid,id_col,r_col) flag = False if llm_response and data[annotations_key]: max_vote = data[annotations_key][0]['count'] for agg_ans in data[annotations_key]: if language != 'English': for a in agg_ans['answers']: if lemma_check(a,llm_response,nlpPipeline,language): binary_score += 1 weight_score += agg_ans['count']/max_vote flag = True break if not flag: for a in agg_ans['en_answers']: if lemma_check(a,llm_response,en_lemmatizer,'English'): binary_score += 1 weight_score += agg_ans['count']/max_vote flag = True break if flag: break if flag: response_df.loc[response_df[id_col]==qid,'binary_score'] = 1 response_df.loc[response_df[id_col]==qid,'weight_score'] = agg_ans['count']/max_vote print(response_df.loc[response_df[id_col]==qid]) else: response_df.loc[response_df[id_col]==qid,'binary_score'] = 0 response_df.loc[response_df[id_col]==qid,'weight_score'] = 0 pb.set_postfix({'bs':binary_score/valid_question_cnt*100,'ws':weight_score/valid_question_cnt*100}) binary_score = binary_score / valid_question_cnt * 100 weight_score = weight_score / valid_question_cnt * 100 print(binary_score) print(weight_score) return binary_score, weight_score, response_df