Delete emotions.txt

emotions.txt

@@ -1,140 +0,0 @@
-<<<<<<< HEAD
-PS C:\Users\NAVYA\Documents\moodify> python emotions.py
-2025-02-26 20:38:46.440320: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-2025-02-26 20:38:47.658979: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-WARNING:tensorflow:From C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\tf_keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
-
-Dataset Columns Before Preprocessing: ['text', 'labels', 'id']
-Map: 100%|█████████████████████████████████████████████████████████████████████████████████████| 43410/43410 [00:22<00:00, 1958.97 examples/s] 
-Map: 100%|███████████████████████████████████████████████████████████████████████████████████████| 5426/5426 [00:03<00:00, 1796.32 examples/s] 
-Map: 100%|███████████████████████████████████████████████████████████████████████████████████████| 5427/5427 [00:02<00:00, 1936.32 examples/s] 
-Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
-You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
-{'eval_loss': 1.414624571800232, 'eval_accuracy': 0.5748249170659786, 'eval_f1': 0.55625264544128, 'eval_runtime': 37.1848, 'eval_samples_per_second': 145.92, 'eval_steps_per_second': 4.572, 'epoch': 1.0}
-{'eval_loss': 1.3568519353866577, 'eval_accuracy': 0.5895687430888316, 'eval_f1': 0.5727110766843768, 'eval_runtime': 38.7582, 'eval_samples_per_second': 139.996, 'eval_steps_per_second': 4.386, 'epoch': 2.0}                                                                              
-{'train_runtime': 6368.0108, 'train_samples_per_second': 13.634, 'train_steps_per_second': 0.213, 'train_loss': 1.50392983585684, 'epoch': 2.0}
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1356/1356 [1:46:08<00:00,  4.70s/it]
-Training completed!
-Model and tokenizer saved!
-
-Evaluating model on test set...
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 170/170 [00:38<00:00,  4.43it/s]
-
-Evaluation Results:
-Test Accuracy: 0.5779
-Test F1 Score: 0.5608
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-
-Classification Report:
-               precision    recall  f1-score   support
-
-           0       0.65      0.74      0.69       504
-           1       0.73      0.86      0.79       252
-           2       0.47      0.47      0.47       197
-           3       0.32      0.20      0.25       286
-           4       0.54      0.35      0.42       318
-           5       0.46      0.40      0.43       114
-           6       0.47      0.39      0.43       139
-           7       0.43      0.61      0.51       233
-           8       0.60      0.42      0.49        74
-           9       0.38      0.22      0.28       127
-          10       0.42      0.37      0.39       220
-          11       0.48      0.40      0.44        84
-          12       0.71      0.40      0.51        30
-          13       0.48      0.39      0.43        84
-          14       0.59      0.70      0.64        74
-          15       0.84      0.83      0.83       288
-          16       0.00      0.00      0.00         6
-          17       0.52      0.56      0.54       116
-          18       0.65      0.82      0.72       169
-          19       0.00      0.00      0.00        16
-          20       0.56      0.49      0.52       120
-          21       0.00      0.00      0.00         8
-          22       0.47      0.08      0.14       109
-          23       0.00      0.00      0.00         7
-          24       0.57      0.74      0.64        46
-          25       0.55      0.47      0.51       108
-          26       0.42      0.48      0.44        92
-          27       0.60      0.71      0.65      1606
-
-    accuracy                           0.58      5427
-   macro avg       0.46      0.43      0.44      5427
-weighted avg       0.56      0.58      0.56      5427
-
-Test results saved to 'test_results.csv'!
-=======
-PS C:\Users\NAVYA\Documents\moodify> python emotions.py
-2025-02-26 20:38:46.440320: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-2025-02-26 20:38:47.658979: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
-WARNING:tensorflow:From C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\tf_keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.
-
-Dataset Columns Before Preprocessing: ['text', 'labels', 'id']
-Map: 100%|█████████████████████████████████████████████████████████████████████████████████████| 43410/43410 [00:22<00:00, 1958.97 examples/s] 
-Map: 100%|███████████████████████████████████████████████████████████████████████████████████████| 5426/5426 [00:03<00:00, 1796.32 examples/s] 
-Map: 100%|███████████████████████████████████████████████████████████████████████████████████████| 5427/5427 [00:02<00:00, 1936.32 examples/s] 
-Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
-You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
-{'eval_loss': 1.414624571800232, 'eval_accuracy': 0.5748249170659786, 'eval_f1': 0.55625264544128, 'eval_runtime': 37.1848, 'eval_samples_per_second': 145.92, 'eval_steps_per_second': 4.572, 'epoch': 1.0}
-{'eval_loss': 1.3568519353866577, 'eval_accuracy': 0.5895687430888316, 'eval_f1': 0.5727110766843768, 'eval_runtime': 38.7582, 'eval_samples_per_second': 139.996, 'eval_steps_per_second': 4.386, 'epoch': 2.0}                                                                              
-{'train_runtime': 6368.0108, 'train_samples_per_second': 13.634, 'train_steps_per_second': 0.213, 'train_loss': 1.50392983585684, 'epoch': 2.0}
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1356/1356 [1:46:08<00:00,  4.70s/it]
-Training completed!
-Model and tokenizer saved!
-
-Evaluating model on test set...
-100%|██████████████████████████████████���████████████████████████████████████████████████████████████████████| 170/170 [00:38<00:00,  4.43it/s]
-
-Evaluation Results:
-Test Accuracy: 0.5779
-Test F1 Score: 0.5608
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-C:\Users\NAVYA\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\metrics\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
-  _warn_prf(average, modifier, f"{metric.capitalize()} is", len(result))
-
-Classification Report:
-               precision    recall  f1-score   support
-
-           0       0.65      0.74      0.69       504
-           1       0.73      0.86      0.79       252
-           2       0.47      0.47      0.47       197
-           3       0.32      0.20      0.25       286
-           4       0.54      0.35      0.42       318
-           5       0.46      0.40      0.43       114
-           6       0.47      0.39      0.43       139
-           7       0.43      0.61      0.51       233
-           8       0.60      0.42      0.49        74
-           9       0.38      0.22      0.28       127
-          10       0.42      0.37      0.39       220
-          11       0.48      0.40      0.44        84
-          12       0.71      0.40      0.51        30
-          13       0.48      0.39      0.43        84
-          14       0.59      0.70      0.64        74
-          15       0.84      0.83      0.83       288
-          16       0.00      0.00      0.00         6
-          17       0.52      0.56      0.54       116
-          18       0.65      0.82      0.72       169
-          19       0.00      0.00      0.00        16
-          20       0.56      0.49      0.52       120
-          21       0.00      0.00      0.00         8
-          22       0.47      0.08      0.14       109
-          23       0.00      0.00      0.00         7
-          24       0.57      0.74      0.64        46
-          25       0.55      0.47      0.51       108
-          26       0.42      0.48      0.44        92
-          27       0.60      0.71      0.65      1606
-
-    accuracy                           0.58      5427
-   macro avg       0.46      0.43      0.44      5427
-weighted avg       0.56      0.58      0.56      5427
-
-Test results saved to 'test_results.csv'!
->>>>>>> b1313c5d084e410cadf261f2fafd8929cb149a4f
-PS C:\Users\NAVYA\Doc