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| 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 |