generated from do867bc/DIPLOMOVA_PRACA
140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
## IMPORT NESSESARY EQUIPMENTS
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from transformers import T5ForConditionalGeneration, T5Tokenizer,AutoTokenizer
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import torch
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import evaluate # Bleu
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import json
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import random
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import statistics
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from sklearn.metrics import precision_score, recall_score, f1_score
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## TURN WARNINGS OFF
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import warnings
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warnings.filterwarnings("ignore")
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##13/03/23 added
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from rouge import Rouge
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# Názov modelu
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DEVICE ='cuda:0'
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#T5 MODEL
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#model_name = 'T5_SK_model'
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#model_dir = "/home/omasta/T5_JUPYTER/qa_model"
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#tokenizer_dir = "/home/omasta/T5_JUPYTER/qa_tokenizer"
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#mT5 SMALL MODEL
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model_name = 'mT5_SMALL'
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model_dir = '/home/omasta/T5_JUPYTER/qa_model_mT5_small'
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tokenizer_dir = '/home/omasta/T5_JUPYTER/qa_tokenizer_mT5_small'
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#Načítanie modelu z adresára
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MODEL = T5ForConditionalGeneration.from_pretrained(model_dir, from_tf=False, return_dict=True).to(DEVICE)
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print("Model succesfully loaded!")
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TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=True)
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print("Tokenizer succesfully loaded!")
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Q_LEN = 512
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TOKENIZER.add_tokens('<sep>')
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MODEL.resize_token_embeddings(len(TOKENIZER))
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def predict_answer(data, ref_answer=None,random=None):
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predictions=[]
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for i in data:
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inputs = TOKENIZER(i['input'], max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True)
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input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).to(DEVICE).unsqueeze(0)
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outputs = MODEL.generate(input_ids=input_ids, attention_mask=attention_mask)
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predicted_answer = TOKENIZER.decode(outputs.flatten(), skip_special_tokens=True)
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ref_answer = i['answer'].lower()
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#print(ref_answer)
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if ref_answer:
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# Load the Bleu metric
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bleu = evaluate.load("google_bleu")
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#print('debug')
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#precision = list(precision_score(ref_answer, predicted_answer))
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#recall = list(recall_score(ref_answer, predicted_answer))
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#f1 = list(f1_score(ref_answer, predicted_answer))
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score = bleu.compute(predictions=[predicted_answer],
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references=[ref_answer])
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predictions.append({'prediction':predicted_answer,'ref_answer':ref_answer,'score':score['google_bleu']})
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return predictions
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def prepare_data(data):
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articles = []
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for article in data["data"]:
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for paragraph in article["paragraphs"]:
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for qa in paragraph["qas"]:
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question = qa["question"]
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answer = qa["answers"][0]["text"]
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inputs = {"input": paragraph["context"]+ "<sep>" + question, "answer": answer}
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articles.append(inputs)
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return articles
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dev_data_path = '/home/omasta/T5_JUPYTER/skquad-221017/dev-v1.json'
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with open(dev_data_path,'r') as f:
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data=json.load(f)
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#print('data imported')
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dev_data = prepare_data(data)
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#print('data prepared')
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print(f'Number of dev samples {len(dev_data)}')
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print(dev_data[0])
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bleu_score = []
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precisions=[]
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f1_scores=[]
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recall_scores=[]
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rouge_1 = []
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rouge_2 = []
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#X = 150
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evaluate = predict_answer(dev_data)
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rouge = Rouge()
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for item in evaluate:
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bleu_score.append(item['score'])
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try:
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#scores = rouge.get_scores(item['prediction'], item['ref_answer'], avg=True)
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precision=precision_score(list(item['ref_answer']), list(item['prediction']),average='macro')
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recall=recall_score(list(item['ref_answer']), list(item['prediction']),average='macro')
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f1=f1_score(list(item['ref_answer']), list(item['prediction']),average='macro')
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except ValueError:
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precision=0
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recall=0
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f1=0
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precisions.append(precision)
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f1_scores.append(f1)
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recall_scores.append(recall)
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def rouge_eval(dict_x):
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rouge = Rouge()
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rouge_scores=[]
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for item in dict_x:
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if item['prediction'] and item['ref_answer']:
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rouge_score = rouge.get_scores(item['prediction'], item['ref_answer'])
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rouge_scores.append(rouge_score)
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else:
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continue
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return rouge_scores
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print(f'VYHODNOTENIE VYSLEDKOV : ------------------------')
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#print(evaluate)
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#bleu_score_total = statistics.mean(bleu_score)
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#recall_score_total= statistics.mean(recall_scores)
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#f1_score_total = statistics.mean(f1_scores)
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#precision_total = statistics.mean(precisions)
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#print(f'Bleu_score of model {model_name} : ',bleu_score_total)
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#print(f'Recall of model {model_name}: ',recall_score_total)
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#print(f'F1 of model {model_name} : ', f1_score_total)
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#print(f'Precision of model {model_name}: :',precision_total)
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#print(rouge_eval(evaluate))
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print(f'{model_name} results')
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rouge_scores = rouge_eval(evaluate)
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rouge_values = [score[0]['rouge-1']['f'] for score in rouge_scores]
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mean_rouge_score = statistics.mean(rouge_values)
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print(f'Rouge:{mean_rouge_score}')
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rouge2_values = [score[0]['rouge-2']['f'] for score in rouge_scores]
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mean_rouge_score =statistics.mean(rouge2_values)
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print(f'Rouge-2:{mean_rouge_score}')
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