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  1. ber_try.py +172 -0
  2. id_train.txt +0 -0
  3. id_vaild.txt +1348 -0
  4. picto_id_train.txt +0 -0
  5. picto_id_valid.txt +1348 -0
  6. src_train.txt +0 -0
  7. src_valid.txt +1348 -0
  8. tgt_train.txt +0 -0
  9. tgt_valid.txt +1348 -0
ber_try.py ADDED
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+ from transformers import BertTokenizer, BertForMaskedLM
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+ import torch
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+ from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score
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+ import numpy as np
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+ import matplotlib.pyplot as plt
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+
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+ # # Step 1: Prepare the dataset
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+ # # Load your training and validation datasets
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+ # def read_data(file_path):
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+ # with open(file_path, 'r', encoding='utf-8') as file:
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+ # data = file.readlines()
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+ # return data
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+
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+ # src_train = read_data('src_train.txt') # File containing original sentences for training
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+ # tgt_train = read_data('tgt_train.txt') # File containing corresponding simplified sentences for training
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+ # src_valid = read_data('src_valid.txt') # File containing original sentences for validation
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+ # tgt_valid = read_data('tgt_valid.txt') # File containing corresponding simplified sentences for validation
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+
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+ # # Step 2: Fine-tune the BERT model
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+ # tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-french-wwm-cased')
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+ # model = BertForMaskedLM.from_pretrained('dbmdz/bert-base-french-wwm-cased')
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+
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+ # # Fine-tune the model on your training dataset
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+ # # You need to define the training loop here
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+
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+ # # Step 3: Evaluate the model
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+ # def evaluate_model(model, tokenizer, src_valid, tgt_valid):
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+ # predicted_sentences = []
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+ # true_labels = []
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+
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+ # for src_sentence, tgt_sentence in zip(src_valid, tgt_valid):
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+ # # Tokenize and get predictions
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+ # tokenized_sentence = tokenizer.encode(src_sentence, return_tensors='pt')
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+ # with torch.no_grad():
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+ # outputs = model(tokenized_sentence)
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+ # predictions = outputs.logits[0].argmax(dim=-1).cpu().numpy()
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+
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+ # # Decode predicted sentence
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+ # predicted_sentence = tokenizer.decode(predictions, skip_special_tokens=True)
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+
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+ # # Append to lists
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+ # predicted_sentences.append(predicted_sentence)
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+ # true_labels.append(tgt_sentence)
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+
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+ # # Calculate evaluation metrics
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+ # precision = precision_score(true_labels, predicted_sentences, average='weighted')
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+ # recall = recall_score(true_labels, predicted_sentences, average='weighted')
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+ # f1 = f1_score(true_labels, predicted_sentences, average='weighted')
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+
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+ # # Create confusion matrix
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+ # labels = np.unique(true_labels)
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+ # cm = confusion_matrix(true_labels, predicted_sentences, labels=labels)
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+
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+ # return precision, recall, f1, cm
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+
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+ # precision, recall, f1, confusion_matrix = evaluate_model(model, tokenizer, src_valid, tgt_valid)
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+ # print("Precision:", precision)
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+ # print("Recall:", recall)
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+ # print("F1 Score:", f1)
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+ # print("Confusion Matrix:")
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+ # print(confusion_matrix)
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+
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+ # # Step 4: Analyze the results
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+ # # Count the number of sentences with perfect matches (>70% match, >50% match, <20% match)
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+
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+ # def match_percentage(sentence1, sentence2):
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+ # n = len(sentence1)
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+ # if n == 0:
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+ # return 0.0
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+ # common = sum([1 for x, y in zip(sentence1, sentence2) if x == y])
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+ # return common / n
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+
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+ # matches_70 = 0
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+ # matches_50 = 0
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+ # matches_20 = 0
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+ # for pred, true in zip(predicted_sentences, tgt_valid):
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+ # percentage = match_percentage(pred, true)
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+ # if percentage > 0.7:
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+ # matches_70 += 1
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+ # if percentage > 0.5:
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+ # matches_50 += 1
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+ # if percentage < 0.2:
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+ # matches_20 += 1
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+
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+ # print("Number of sentences with >70% match:", matches_70)
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+ # print("Number of sentences with >50% match:", matches_50)
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+ # print("Number of sentences with <20% match:", matches_20)
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+
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+ # # Save confusion matrix as image
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+ # plt.figure(figsize=(8, 6))
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+ # plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)
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+ # plt.title('Confusion Matrix')
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+ # plt.colorbar()
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+ # tick_marks = np.arange(len(labels))
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+ # plt.xticks(tick_marks, labels, rotation=45)
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+ # plt.yticks(tick_marks, labels)
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+ # plt.xlabel('Predicted Label')
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+ # plt.ylabel('True Label')
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+ # plt.tight_layout()
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+ # plt.savefig('confusion_matrix.png')
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+
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+
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+ # Step 1: Prepare the dataset
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+ # Load your training and validation datasets
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+ def read_data(file_path):
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+ with open(file_path, 'r', encoding='utf-8') as file:
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+ data = file.readlines()
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+ return data
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+
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+ def read_picto_ids(file_path):
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+ with open(file_path, 'r', encoding='utf-8') as file:
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+ data = file.readlines()
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+ picto_ids = [list(map(int, line.split())) for line in data]
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+ return picto_ids
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+
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+ src_train = read_data(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\train_files\src_train.txt') # File containing original sentences for training
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+ tgt_train = read_data(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\train_files\tgt_train.txt') # File containing corresponding simplified sentences for training
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+ picto_train = read_picto_ids(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\train_files\picto_id_train.txt') # File containing picto IDs for training
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+
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+ src_valid = read_data(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\valid_files\src_valid.txt') # File containing original sentences for validation
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+ tgt_valid = read_data(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\valid_files\tgt_valid.txt') # File containing corresponding simplified sentences for validation
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+ picto_valid = read_picto_ids(r'C:\Users\LENOVO\Downloads\ToPicto\data\Text-to-Picto\valid_files\picto_id_valid.txt') # File containing picto IDs for validation
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+
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+ # Now src_train, tgt_train, and picto_train are lists containing the sentences and picto IDs from the files.
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+
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+ # Step 2: Fine-tune the BERT model
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+ # Same as before
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+
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+ # Step 3: Evaluate the model
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+ def evaluate_model(model, tokenizer, src_valid, tgt_valid, picto_valid):
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+ predicted_sentences = []
132
+ true_labels = []
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+
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+ for src_sentence, tgt_sentence, picto_ids in zip(src_valid, tgt_valid, picto_valid):
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+ # Tokenize and get predictions
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+ tokenized_sentence = tokenizer.encode(src_sentence, return_tensors='pt')
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+ with torch.no_grad():
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+ outputs = model(tokenized_sentence)
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+ predictions = outputs.logits[0].argmax(dim=-1).cpu().numpy()
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+
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+ # Decode predicted sentence
142
+ predicted_sentence = tokenizer.decode(predictions, skip_special_tokens=True)
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+
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+ # Append to lists
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+ predicted_sentences.append(predicted_sentence)
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+ true_labels.append(tgt_sentence)
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+
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+ # Calculate evaluation metrics based on picto IDs
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+ accuracies = {"100%": 0, "70%": 0, "50%": 0, "20%": 0}
150
+ for pred, true, picto_pred, picto_true in zip(predicted_sentences, true_labels, picto_valid, tgt_valid):
151
+ if pred == true:
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+ accuracies["100%"] += 1
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+ elif len(pred.split()) == len(picto_pred) == len(picto_true):
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+ match_count = sum(1 for x, y in zip(picto_pred, picto_true) if x == y)
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+ match_percentage = match_count / len(picto_pred)
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+ if match_percentage >= 0.7:
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+ accuracies["70%"] += 1
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+ elif match_percentage >= 0.5:
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+ accuracies["50%"] += 1
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+ elif match_percentage >= 0.2:
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+ accuracies["20%"] += 1
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+
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+ return accuracies
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+ from transformers import CamembertModel, CamembertTokenizer
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+
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+ # You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
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+ tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
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+ camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
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+
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+ accuracies = evaluate_model(model, tokenizer, src_valid, tgt_valid, picto_valid)
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+ print("Accuracies based on picto IDs:")
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+ print(accuracies)
id_train.txt ADDED
The diff for this file is too large to render. See raw diff
 
id_vaild.txt ADDED
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924
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925
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926
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927
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928
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929
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930
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931
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932
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933
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934
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935
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936
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937
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938
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939
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940
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941
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942
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943
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944
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945
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946
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947
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948
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949
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950
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951
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952
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953
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954
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955
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956
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957
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958
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959
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960
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961
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962
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963
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964
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965
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966
+ cefc-tcof-Reunion_luc_08-169
967
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968
+ cefc-tcof-Reunion_luc_08-312
969
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970
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971
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972
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973
+ cefc-tcof-Reunionpp_aar_08-2
974
+ cefc-tcof-Reunionpp_aar_08-210
975
+ cefc-tcof-Reunionpp_aar_08-217
976
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977
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978
+ cefc-tcof-Reunionpp_aar_08-275
979
+ cefc-tcof-Reunionpp_aar_08-295
980
+ cefc-tcof-Reunionpp_aar_08-300
981
+ cefc-tcof-Reunionpp_aar_08-34
982
+ cefc-tcof-Reunionpp_aar_08-349
983
+ cefc-tcof-Reunionpp_aar_08-353
984
+ cefc-tcof-Reunionpp_aar_08-360
985
+ cefc-tcof-Reunionpp_aar_08-400
986
+ cefc-tcof-Reunionpp_aar_08-429
987
+ cefc-tcof-Reunionpp_aar_08-441
988
+ cefc-tcof-See_reu_mat_08-102
989
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990
+ cefc-tcof-See_reu_mat_08-132
991
+ cefc-tcof-See_reu_mat_08-161
992
+ cefc-tcof-See_reu_mat_08-327
993
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994
+ cefc-tcof-See_reu_mat_08-375_2
995
+ cefc-tcof-See_reu_mat_08-385
996
+ cefc-tcof-See_reu_mat_08-398
997
+ cefc-tcof-See_reu_mat_08-496
998
+ cefc-tcof-See_reu_mat_08-497
999
+ cefc-tcof-See_reu_mat_08-50
1000
+ cefc-tcof-See_reu_mat_08-596
1001
+ cefc-tcof-See_reu_mat_08-632
1002
+ cefc-tcof-See_reu_mat_08-645_2
1003
+ cefc-tcof-See_reu_mat_08-649
1004
+ cefc-tcof-See_reu_mat_08-669
1005
+ cefc-tcof-Sousse_bur-1
1006
+ cefc-tcof-Sousse_bur-10
1007
+ cefc-tcof-Sousse_bur-119
1008
+ cefc-tcof-Sousse_bur-132
1009
+ cefc-tcof-Sousse_bur-140
1010
+ cefc-tcof-Sousse_bur-156
1011
+ cefc-tcof-Sousse_bur-178
1012
+ cefc-tcof-Sousse_bur-229
1013
+ cefc-tcof-Sousse_bur-294
1014
+ cefc-tcof-Sousse_bur-300
1015
+ cefc-tcof-Sousse_bur-323
1016
+ cefc-tcof-Sousse_bur-339
1017
+ cefc-tcof-Sousse_bur-342
1018
+ cefc-tcof-Sousse_bur-368
1019
+ cefc-tcof-Sousse_bur-407
1020
+ cefc-tcof-Sousse_bur-452
1021
+ cefc-tcof-Sousse_bur-500
1022
+ cefc-tcof-Sousse_bur-547
1023
+ cefc-tcof-Sousse_bur-56
1024
+ cefc-tcof-Sousse_bur-57
1025
+ cefc-tcof-Sousse_bur-64
1026
+ cefc-tcof-Sousse_bur-677
1027
+ cefc-tcof-Stag_bad_08-103
1028
+ cefc-tcof-Stag_bad_08-128
1029
+ cefc-tcof-Stag_bad_08-13
1030
+ cefc-tcof-Stag_bad_08-27
1031
+ cefc-tcof-Stag_bad_08-46
1032
+ cefc-tcof-Stag_bad_08-59
1033
+ cefc-tcof-Stag_bad_08-80
1034
+ cefc-tcof-Tel_maz_07-15
1035
+ cefc-tcof-Tel_maz_07-68
1036
+ cefc-tcof-Tel_maz_07-91
1037
+ cefc-tcof-Tromboniste-130
1038
+ cefc-tcof-Tromboniste-132
1039
+ cefc-tcof-Tromboniste-173
1040
+ cefc-tcof-Tromboniste-264
1041
+ cefc-tcof-Tromboniste-284
1042
+ cefc-tcof-Tromboniste-287
1043
+ cefc-tcof-Tromboniste-35
1044
+ cefc-tcof-Tromboniste-55
1045
+ cefc-tcof-Tromboniste-70
1046
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1047
+ cefc-tcof-Tscha_cha_reu_ass_08-11
1048
+ cefc-tcof-Tscha_cha_reu_ass_08-134
1049
+ cefc-tcof-Tscha_cha_reu_ass_08-138
1050
+ cefc-tcof-Tscha_cha_reu_ass_08-149
1051
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1052
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1053
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1054
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1055
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1056
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1057
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1058
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1059
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1060
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1061
+ cefc-tcof-Tscha_cha_reu_ass_08-401
1062
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1063
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1064
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1065
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1066
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1067
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1068
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1069
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1070
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1071
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1072
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1073
+ cefc-tcof-Tunisie_mun_08-15
1074
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1075
+ cefc-tcof-Tunisie_mun_08-338
1076
+ cefc-tcof-Tunisie_mun_08-358
1077
+ cefc-tcof-Tunisie_mun_08-454
1078
+ cefc-tcof-Tunisie_mun_08-516
1079
+ cefc-tcof-Tunisie_mun_08-619_1
1080
+ cefc-tcof-Tunisie_mun_08-62
1081
+ cefc-tcof-Tunisie_mun_08-70
1082
+ cefc-tcof-Vin_car_07-3
1083
+ cefc-tcof-Vin_car_07-40
1084
+ cefc-tcof-Vin_car_07-59
1085
+ cefc-tcof-Vin_car_07-76
1086
+ cefc-tcof-Vin_car_07-87
1087
+ cefc-tcof-Vin_car_07-90
1088
+ cefc-tcof-Vin_car_07-93
1089
+ cefc-tcof-Voyages_ric_06-117
1090
+ cefc-tcof-Voyages_ric_06-232
1091
+ cefc-tcof-Voyages_ric_06-242
1092
+ cefc-tcof-Voyages_ric_06-266
1093
+ cefc-tcof-Voyages_ric_06-277
1094
+ cefc-tcof-Voyages_ric_06-28
1095
+ cefc-tcof-Voyages_ric_06-29
1096
+ cefc-tcof-Voyages_ric_06-72
1097
+ cefc-tcof-Voyages_ric_06-97
1098
+ cefc-tcof-anniversaire-103
1099
+ cefc-tcof-anniversaire-114
1100
+ cefc-tcof-anniversaire-4
1101
+ cefc-tcof-anniversaire-49
1102
+ cefc-tcof-appartement_F3-45
1103
+ cefc-tcof-appartement_F3-80
1104
+ cefc-tcof-apres_chasse-108
1105
+ cefc-tcof-apres_chasse-126
1106
+ cefc-tcof-apres_chasse-212
1107
+ cefc-tcof-apres_chasse-26
1108
+ cefc-tcof-art_therapie-119
1109
+ cefc-tcof-art_therapie-176
1110
+ cefc-tcof-art_therapie-207
1111
+ cefc-tcof-art_therapie-230
1112
+ cefc-tcof-art_therapie-26
1113
+ cefc-tcof-art_therapie-31
1114
+ cefc-tcof-art_therapie-43
1115
+ cefc-tcof-art_therapie-79
1116
+ cefc-tcof-art_therapie-82
1117
+ cefc-tcof-art_therapie-84
1118
+ cefc-tcof-basket-112
1119
+ cefc-tcof-basket-115_2
1120
+ cefc-tcof-basket-124
1121
+ cefc-tcof-basket-46
1122
+ cefc-tcof-basket-58
1123
+ cefc-tcof-basket-90
1124
+ cefc-tcof-basket-98
1125
+ cefc-tcof-basket-99
1126
+ cefc-tcof-chocolats-18
1127
+ cefc-tcof-chocolats-37
1128
+ cefc-tcof-chocolats-69
1129
+ cefc-tcof-chocolats-72
1130
+ cefc-tcof-chocolats-79
1131
+ cefc-tcof-colocataires-13
1132
+ cefc-tcof-colocataires-167
1133
+ cefc-tcof-colocataires-177
1134
+ cefc-tcof-colocataires-25
1135
+ cefc-tcof-colocataires-26
1136
+ cefc-tcof-colocataires-44
1137
+ cefc-tcof-colocataires-63
1138
+ cefc-tcof-concert_Indochine-13
1139
+ cefc-tcof-concert_Indochine-158
1140
+ cefc-tcof-concert_Indochine-214
1141
+ cefc-tcof-concert_Indochine-235
1142
+ cefc-tcof-concert_Indochine-258
1143
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1144
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1145
+ cefc-tcof-concert_Indochine-50
1146
+ cefc-tcof-concert_Indochine-82
1147
+ cefc-tcof-concert_Indochine-93
1148