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ber_try.py

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+from transformers import BertTokenizer, BertForMaskedLM
+import torch
+from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score
+import numpy as np
+import matplotlib.pyplot as plt
+
+# # Step 1: Prepare the dataset
+# # Load your training and validation datasets
+# def read_data(file_path):
+#     with open(file_path, 'r', encoding='utf-8') as file:
+#         data = file.readlines()
+#     return data
+
+# src_train = read_data('src_train.txt')  # File containing original sentences for training
+# tgt_train = read_data('tgt_train.txt')  # File containing corresponding simplified sentences for training
+# src_valid = read_data('src_valid.txt')  # File containing original sentences for validation
+# tgt_valid = read_data('tgt_valid.txt')  # File containing corresponding simplified sentences for validation
+
+# # Step 2: Fine-tune the BERT model
+# tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-french-wwm-cased')
+# model = BertForMaskedLM.from_pretrained('dbmdz/bert-base-french-wwm-cased')
+
+# # Fine-tune the model on your training dataset
+# # You need to define the training loop here
+
+# # Step 3: Evaluate the model
+# def evaluate_model(model, tokenizer, src_valid, tgt_valid):
+#     predicted_sentences = []
+#     true_labels = []
+
+#     for src_sentence, tgt_sentence in zip(src_valid, tgt_valid):
+#         # Tokenize and get predictions
+#         tokenized_sentence = tokenizer.encode(src_sentence, return_tensors='pt')
+#         with torch.no_grad():
+#             outputs = model(tokenized_sentence)
+#             predictions = outputs.logits[0].argmax(dim=-1).cpu().numpy()
+
+#         # Decode predicted sentence
+#         predicted_sentence = tokenizer.decode(predictions, skip_special_tokens=True)
+
+#         # Append to lists
+#         predicted_sentences.append(predicted_sentence)
+#         true_labels.append(tgt_sentence)
+
+#     # Calculate evaluation metrics
+#     precision = precision_score(true_labels, predicted_sentences, average='weighted')
+#     recall = recall_score(true_labels, predicted_sentences, average='weighted')
+#     f1 = f1_score(true_labels, predicted_sentences, average='weighted')
+
+#     # Create confusion matrix
+#     labels = np.unique(true_labels)
+#     cm = confusion_matrix(true_labels, predicted_sentences, labels=labels)
+
+#     return precision, recall, f1, cm
+
+# precision, recall, f1, confusion_matrix = evaluate_model(model, tokenizer, src_valid, tgt_valid)
+# print("Precision:", precision)
+# print("Recall:", recall)
+# print("F1 Score:", f1)
+# print("Confusion Matrix:")
+# print(confusion_matrix)
+
+# # Step 4: Analyze the results
+# # Count the number of sentences with perfect matches (>70% match, >50% match, <20% match)
+
+# def match_percentage(sentence1, sentence2):
+#     n = len(sentence1)
+#     if n == 0:
+#         return 0.0
+#     common = sum([1 for x, y in zip(sentence1, sentence2) if x == y])
+#     return common / n
+
+# matches_70 = 0
+# matches_50 = 0
+# matches_20 = 0
+# for pred, true in zip(predicted_sentences, tgt_valid):
+#     percentage = match_percentage(pred, true)
+#     if percentage > 0.7:
+#         matches_70 += 1
+#     if percentage > 0.5:
+#         matches_50 += 1
+#     if percentage < 0.2:
+#         matches_20 += 1
+
+# print("Number of sentences with >70% match:", matches_70)
+# print("Number of sentences with >50% match:", matches_50)
+# print("Number of sentences with <20% match:", matches_20)
+
+# # Save confusion matrix as image
+# plt.figure(figsize=(8, 6))
+# plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.cm.Blues)
+# plt.title('Confusion Matrix')
+# plt.colorbar()
+# tick_marks = np.arange(len(labels))
+# plt.xticks(tick_marks, labels, rotation=45)
+# plt.yticks(tick_marks, labels)
+# plt.xlabel('Predicted Label')
+# plt.ylabel('True Label')
+# plt.tight_layout()
+# plt.savefig('confusion_matrix.png')
+
+
+# Step 1: Prepare the dataset
+# Load your training and validation datasets
+def read_data(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = file.readlines()
+    return data
+
+def read_picto_ids(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        data = file.readlines()
+        picto_ids = [list(map(int, line.split())) for line in data]
+    return picto_ids
+
+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
+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
+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
+
+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
+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
+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
+
+# Now src_train, tgt_train, and picto_train are lists containing the sentences and picto IDs from the files.
+
+# Step 2: Fine-tune the BERT model
+# Same as before
+
+# Step 3: Evaluate the model
+def evaluate_model(model, tokenizer, src_valid, tgt_valid, picto_valid):
+    predicted_sentences = []
+    true_labels = []
+
+    for src_sentence, tgt_sentence, picto_ids in zip(src_valid, tgt_valid, picto_valid):
+        # Tokenize and get predictions
+        tokenized_sentence = tokenizer.encode(src_sentence, return_tensors='pt')
+        with torch.no_grad():
+            outputs = model(tokenized_sentence)
+            predictions = outputs.logits[0].argmax(dim=-1).cpu().numpy()
+
+        # Decode predicted sentence
+        predicted_sentence = tokenizer.decode(predictions, skip_special_tokens=True)
+
+        # Append to lists
+        predicted_sentences.append(predicted_sentence)
+        true_labels.append(tgt_sentence)
+
+    # Calculate evaluation metrics based on picto IDs
+    accuracies = {"100%": 0, "70%": 0, "50%": 0, "20%": 0}
+    for pred, true, picto_pred, picto_true in zip(predicted_sentences, true_labels, picto_valid, tgt_valid):
+        if pred == true:
+            accuracies["100%"] += 1
+        elif len(pred.split()) == len(picto_pred) == len(picto_true):
+            match_count = sum(1 for x, y in zip(picto_pred, picto_true) if x == y)
+            match_percentage = match_count / len(picto_pred)
+            if match_percentage >= 0.7:
+                accuracies["70%"] += 1
+            elif match_percentage >= 0.5:
+                accuracies["50%"] += 1
+            elif match_percentage >= 0.2:
+                accuracies["20%"] += 1
+
+    return accuracies
+from transformers import CamembertModel, CamembertTokenizer
+
+# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
+tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
+camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
+
+accuracies = evaluate_model(model, tokenizer, src_valid, tgt_valid, picto_valid)
+print("Accuracies based on picto IDs:")
+print(accuracies)

id_vaild.txt

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