{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":51250,"offset":51240,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"As an AI developed by OpenAI, I don't have the capability to generate images. My primary function is to provide information, answer questions, and help with a variety of text-based tasks. However, OpenAI has developed another AI called DALL-E, which specializes in creating images from textual descriptions. If you have access to DALL-E or a similar AI image generator, you can use text prompts to generate images.\n\nFor image generation, I can provide guidance or suggest ideas for prompts that you might use with an AI image-generating service, but I cannot directly create or display images for you. If you need help coming up with a prompt or description for an image you'd like to generate, feel free to ask!","behavior":"degenerate_repetition","conversation_hash":"30d795187786700806930bad399cc749","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4-1106-preview","n_chars":737,"n_turns_in_conversation":1,"n_words":124,"source_row_offset":51240,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:43:15 GMT","turn_id":"bbb3f5b05c3b0fb2aa65e36d","turn_index":0,"user_text":"Can you generate images?"},{"assistant_initiates":false,"asst_text":"The learning rate in a machine learning model is a hyperparameter that determines the step size at each iteration while moving towards a minimum of a loss function. Choosing the right learning rate is crucial as it can greatly affect the convergence of the model training and the final performance.\n\nFrom the provided output of the training epochs, it seems that a learning rate (lr) of `1.0000e-04` (or 0.0001) is being used. Analyzing the output:\n\n- The model is making progress, albeit slowly, as observed by the decreasing loss and improving accuracy, precision, and recall over the epochs.\n- By epoch 135, the model has not yet shown signs of overfitting, as both training and validation accuracies are improving.\n\nBased on these observations, the learning rate does not necessarily seem too low, as the model is improving and learning from the data. However, whether it could be optimized is a different question. Here are a few considerations:\n\n1. If the model training is too slow: A learning rate scheduler or different optimization algorithms like Adam, which adapts the learning rate during training, could help improve training speed with a suitable initial learning rate.\n\n2. If the improvements plateau early: It could be beneficial to try a slightly higher learning rate or employ a learning rate scheduler that starts higher and gradually decreases.\n\n3. Learning rate too low: If the model improvements are minimal and training is very slow, a higher learning rate might be necessary.\n\n4. Learning rate too high: On the other hand, if the model begins to show erratic behavior with large fluctuations in loss and accuracy, this could be a sign that the learning rate is too high.\n\nIn practice, it's common to experiment with various learning rates or use learning rate finding techniques to determine the best learning rate. It often involves starting with a small learning rate that is incrementally increased and observing the loss; when the loss starts to increase rapidly, the optimal learning rate is a bit lower than that point.\n\nRemember that the learning rate is one of many hyperparameters, and the overall architecture, data preprocessing, activation functions, batch size, and other factors also contribute significantly to model performance. It's always a good idea to perform hyperparameter tuning systematically or use automated methods like grid search, random search, or Bayesian optimization methods.","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":35980,"n_turns_in_conversation":16,"n_words":4966,"source_row_offset":51241,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"33143d7559074e620ea935ed","turn_index":0,"user_text":"Epoch 1/1000\n7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04\nEpoch 2/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04\nEpoch 3/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04\nEpoch 4/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04\nEpoch 5/1000\n7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04\nEpoch 6/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04\nEpoch 7/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04\nEpoch 8/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04\nEpoch 9/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04\nEpoch 10/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04\nEpoch 11/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04\nEpoch 12/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04\nEpoch 13/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04\nEpoch 14/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04\nEpoch 15/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04\nEpoch 16/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04\nEpoch 17/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04\nEpoch 18/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04\nEpoch 19/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04\nEpoch 20/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04\nEpoch 21/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04\nEpoch 22/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04\nEpoch 23/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04\nEpoch 24/1000\n7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04\nEpoch 25/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04\nEpoch 26/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04\nEpoch 27/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04\nEpoch 28/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04\nEpoch 29/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04\nEpoch 30/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04\nEpoch 31/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04\nEpoch 32/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04\nEpoch 33/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04\nEpoch 34/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04\nEpoch 35/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04\nEpoch 36/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04\nEpoch 37/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04\nEpoch 38/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04\nEpoch 39/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04\nEpoch 40/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04\nEpoch 41/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04\nEpoch 42/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04\nEpoch 43/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04\nEpoch 44/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04\nEpoch 45/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04\nEpoch 46/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04\nEpoch 47/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04\nEpoch 48/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04\nEpoch 49/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04\nEpoch 50/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04\nEpoch 51/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04\nEpoch 52/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04\nEpoch 53/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04\nEpoch 54/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04\nEpoch 55/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04\nEpoch 56/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04\nEpoch 57/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04\nEpoch 58/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04\nEpoch 59/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04\nEpoch 60/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04\nEpoch 61/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04\nEpoch 62/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04\nEpoch 63/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04\nEpoch 64/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 65/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04\nEpoch 66/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04\nEpoch 67/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04\nEpoch 68/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04\nEpoch 69/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04\nEpoch 70/1000\n7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04\nEpoch 71/1000\n7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04\nEpoch 72/1000\n7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04\nEpoch 73/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04\nEpoch 74/1000\n7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04\nEpoch 75/1000\n7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 76/1000\n7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04\nEpoch 77/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04\nEpoch 78/1000\n7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04\nEpoch 79/1000\n7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04\nEpoch 80/1000\n7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04\nEpoch 81/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04\nEpoch 82/1000\n7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04\nEpoch 83/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 84/1000\n7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04\nEpoch 85/1000\n7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04\nEpoch 86/1000\n7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04\nEpoch 87/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04\nEpoch 88/1000\n7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04\nEpoch 89/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04\nEpoch 90/1000\n7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04\nEpoch 91/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04\nEpoch 92/1000\n7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04\nEpoch 93/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04\nEpoch 94/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04\nEpoch 95/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04\nEpoch 96/1000\n7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04\nEpoch 97/1000\n7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04\nEpoch 98/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04\nEpoch 99/1000\n7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04\nEpoch 100/1000\n7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04\nEpoch 101/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04\nEpoch 102/1000\n7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04\nEpoch 103/1000\n7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04\nEpoch 104/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04\nEpoch 105/1000\n7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04\nEpoch 106/1000\n7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04\nEpoch 107/1000\n7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04\nEpoch 108/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04\nEpoch 109/1000\n7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04\nEpoch 110/1000\n7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04\nEpoch 111/1000\n7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04\nEpoch 112/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04\nEpoch 113/1000\n7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04\nEpoch 114/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04\nEpoch 115/1000\n7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04\nEpoch 116/1000\n7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04\nEpoch 117/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04\nEpoch 118/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04\nEpoch 119/1000\n7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04\nEpoch 120/1000\n7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04\nEpoch 121/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04\nEpoch 122/1000\n7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04\nEpoch 123/1000\n7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04\nEpoch 124/1000\n7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04\nEpoch 125/1000\n7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04\nEpoch 126/1000\n7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04\nEpoch 127/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04\nEpoch 128/1000\n7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04\nEpoch 129/1000\n7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04\nEpoch 130/1000\n7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04\nEpoch 131/1000\n7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04\nEpoch 132/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04\nEpoch 133/1000\n7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04\nEpoch 134/1000\n7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04\nEpoch 135/1000\n3753/7680 [=============>................] - ETA: 1:03 - loss: 0.6167 - accuracy: 0.6560 - precision: 0.6585 - recall: 0.6464\n\n\u5b66\u4e60\u7387\u662f\u4e0d\u662f\u8bbe\u7f6e\u7684\u592a\u4f4e\u4e86"},{"assistant_initiates":false,"asst_text":"\u5728\u673a\u5668\u5b66\u4e60\u6a21\u578b\u4e2d\uff0c\u5b66\u4e60\u7387\uff08learning rate\uff09\u662f\u4e00\u4e2a\u8d85\u53c2\u6570\uff0c\u5b83\u51b3\u5b9a\u4e86\u5728\u8fed\u4ee3\u8fc7\u7a0b\u4e2d\u5411\u635f\u5931\u51fd\u6570\u6700\u5c0f\u503c\u79fb\u52a8\u7684\u6b65\u957f\u5927\u5c0f\u3002\u9009\u62e9\u5408\u9002\u7684\u5b66\u4e60\u7387\u81f3\u5173\u91cd\u8981\uff0c\u56e0\u4e3a\u5b83\u4f1a\u76f4\u63a5\u5f71\u54cd\u5230\u6a21\u578b\u8bad\u7ec3\u7684\u6536\u655b\u901f\u5ea6\u548c\u6700\u7ec8\u6027\u80fd\u3002\n\n\u4ece\u60a8\u63d0\u4f9b\u7684\u8bad\u7ec3\u5468\u671f\uff08epochs\uff09\u8f93\u51fa\u6765\u770b\uff0c\u6b63\u5728\u4f7f\u7528\u7684\u5b66\u4e60\u7387\uff08lr\uff09\u662f `1.0000e-04`\uff08\u53730.0001\uff09\u3002\u5206\u6790\u8f93\u51fa\uff1a\n\n- \u6a21\u578b\u6b63\u5728\u53d6\u5f97\u8fdb\u5c55\uff0c\u5c3d\u7ba1\u8fdb\u6b65\u7f13\u6162\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u635f\u5931\u9010\u6e10\u51cf\u5c0f\u4ee5\u53ca\u51c6\u786e\u5ea6\u3001\u7cbe\u786e\u5ea6\u548c\u53ec\u56de\u7387\u9010\u6e10\u63d0\u9ad8\u6765\u89c2\u5bdf\u5230\u3002\n- \u5728\u7b2c135\u5468\u671f\uff0c\u6a21\u578b\u5c1a\u672a\u8868\u73b0\u51fa\u8fc7\u62df\u5408\u7684\u8ff9\u8c61\uff0c\u56e0\u4e3a\u8bad\u7ec3\u548c\u9a8c\u8bc1\u51c6\u786e\u5ea6\u90fd\u5728\u63d0\u9ad8\u3002\n\n\u6839\u636e\u8fd9\u4e9b\u89c2\u5bdf\uff0c\u5f53\u524d\u7684\u5b66\u4e60\u7387\u5e76\u4e0d\u4e00\u5b9a\u8fc7\u4f4e\uff0c\u56e0\u4e3a\u6a21\u578b\u6b63\u5728\u6539\u8fdb\u5e76\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\u3002\u7136\u800c\uff0c\u662f\u5426\u53ef\u4ee5\u4f18\u5316\u5b66\u4e60\u7387\u5219\u662f\u53e6\u4e00\u4e2a\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u8003\u8651\u56e0\u7d20\uff1a\n\n1. \u5982\u679c\u6a21\u578b\u8bad\u7ec3\u901f\u5ea6\u8fc7\u6162\uff1a\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\uff0c\u6216\u5c1d\u8bd5\u4f7f\u7528\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\uff08\u5982Adam\uff09\uff0c\u540e\u8005\u4f1a\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u81ea\u9002\u5e94\u8c03\u6574\u5b66\u4e60\u7387\uff0c\u6709\u52a9\u4e8e\u6539\u8fdb\u8bad\u7ec3\u901f\u5ea6\uff0c\u5e76\u53ef\u4ee5\u8bbe\u7f6e\u5408\u7406\u7684\u521d\u59cb\u5b66\u4e60\u7387\u3002\n\n2. \u5982\u679c\u6027\u80fd\u63d0\u5347\u5f88\u5feb\u5c31\u8fbe\u5230\u4e86\u5e73\u53f0\u671f\uff1a\u8fd9\u65f6\u5019\u53ef\u4ee5\u5c1d\u8bd5\u4f7f\u7528\u7a0d\u5fae\u66f4\u9ad8\u7684\u5b66\u4e60\u7387\uff0c\u6216\u8005\u91c7\u7528\u4e00\u4e2a\u521d\u59cb\u503c\u8f83\u9ad8\u3001\u968f\u540e\u9010\u6e10\u964d\u4f4e\u7684\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\u3002\n\n3. \u5b66\u4e60\u7387\u8fc7\u4f4e\uff1a\u5982\u679c\u6a21\u578b\u6539\u8fdb\u5f88\u5c11\u5e76\u4e14\u8bad\u7ec3\u901f\u5ea6\u975e\u5e38\u6162\uff0c\u53ef\u80fd\u9700\u8981\u4e00\u4e2a\u66f4\u9ad8\u7684\u5b66\u4e60\u7387\u3002\n\n4. \u5b66\u4e60\u7387\u8fc7\u9ad8\uff1a\u53e6\u4e00\u65b9\u9762\uff0c\u5982\u679c\u6a21\u578b\u5f00\u59cb\u8868\u73b0\u51fa\u5ffd\u4e0a\u5ffd\u4e0b\u7684\u884c\u4e3a\uff0c\u635f\u5931\u548c\u51c6\u786e\u5ea6\u6ce2\u52a8\u5f88\u5927\uff0c\u8fd9\u53ef\u80fd\u662f\u5b66\u4e60\u7387\u8bbe\u7f6e\u8fc7\u9ad8\u7684\u4fe1\u53f7\u3002\n\n\u5728\u5b9e\u8df5\u4e2d\uff0c\u5e38\u5e38\u9700\u8981\u5c1d\u8bd5\u4e0d\u540c\u7684\u5b66\u4e60\u7387\uff0c\u6216\u8005\u4f7f\u7528\u5b66\u4e60\u7387\u67e5\u627e\u6280\u672f\u6765\u786e\u5b9a\u6700\u4f73\u5b66\u4e60\u7387\u3002\u8fd9\u901a\u5e38\u6d89\u53ca\u5230\u4ece\u4e00\u4e2a\u5c0f\u7684\u5b66\u4e60\u7387\u5f00\u59cb\uff0c\u9010\u6b65\u589e\u52a0\u5b66\u4e60\u7387\uff0c\u5e76\u89c2\u5bdf\u635f\u5931\uff1b\u5f53\u635f\u5931\u5f00\u59cb\u8fc5\u901f\u4e0a\u5347\u65f6\uff0c\u6700\u4f18\u5b66\u4e60\u7387\u5e94\u8be5\u7565\u4f4e\u4e8e\u90a3\u4e2a\u70b9\u3002\n\n\u8bb0\u4f4f\uff0c\u5b66\u4e60\u7387\u53ea\u662f\u4f17\u591a\u8d85\u53c2\u6570\u4e2d\u7684\u4e00\u4e2a\uff0c\u6574\u4f53\u67b6\u6784\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u6fc0\u6d3b\u51fd\u6570\u3001\u6279\u5904\u7406\u5927\u5c0f\u7b49\u5176\u4ed6\u56e0\u7d20\u4e5f\u5bf9\u6a21\u578b\u6027\u80fd\u6709\u7740\u91cd\u8981\u5f71\u54cd\u3002\u5f80\u5f80\u9700\u8981\u7cfb\u7edf\u5730\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\uff0c\u6216\u8005\u4f7f\u7528\u7f51\u683c\u641c\u7d22\uff08grid search\uff09\u3001\u968f\u673a\u641c\u7d22\uff08random search\uff09\u6216\u8d1d\u53f6\u65af\u4f18\u5316\u65b9\u6cd5\u7b49\u81ea\u52a8\u5316\u65b9\u6cd5\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":811,"n_turns_in_conversation":16,"n_words":22,"source_row_offset":51242,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"1eb3d6fe0bce348e019ffda9","turn_index":1,"user_text":"\u8bf7\u7528\u4e2d\u6587"},{"assistant_initiates":false,"asst_text":"\u6570\u636e\u91cf\u8fbe\u5230\u4ebf\u7ea7\u522b\u65f6\uff0c\u4f60\u7684\u6a21\u578b\u8bad\u7ec3\u901f\u5ea6\u786e\u5b9e\u4f1a\u88ab\u8ba4\u4e3a\u662f\u8f83\u6162\u7684\u3002\u6839\u636e\u4f60\u7684\u65e5\u5fd7\u663e\u793a\uff0c\u6bcf\u4e2aepoch\u9700\u8981\u51e0\u5206\u949f\u7684\u8bad\u7ec3\u65f6\u95f4\uff0c\u5bf9\u4e8e\u5927\u578b\u6570\u636e\u96c6\u800c\u8a00\uff0c\u5b8c\u6210\u6240\u6709epoch\u53ef\u80fd\u9700\u8981\u76f8\u5f53\u957f\u7684\u65f6\u95f4\u3002\n\n\u5bf9\u4e8e\u5982\u6b64\u5927\u89c4\u6a21\u7684\u6570\u636e\u96c6\uff0c\u4e00\u4e9b\u5e38\u89c1\u7684\u5904\u7406\u65b9\u5f0f\u53ef\u80fd\u5305\u62ec\uff1a\n\n1. **\u5206\u5e03\u5f0f\u8bad\u7ec3**\uff1a\u901a\u8fc7\u591a\u4e2aGPU\u5e76\u884c\u5904\u7406\u6570\u636e\u6765\u52a0\u901f\u8bad\u7ec3\u3002\u6846\u67b6\u5982TensorFlow\u548cPyTorch\u90fd\u63d0\u4f9b\u4e86\u5206\u5e03\u5f0f\u8bad\u7ec3\u7684\u652f\u6301\u3002\n\n2. **\u4f7f\u7528\u66f4\u9ad8\u6548\u7684\u6570\u636e\u52a0\u8f7d\u548c\u9884\u5904\u7406**\uff1a\u4f8b\u5982\uff0c\u786e\u4fdd\u6570\u636e\u9884\u5904\u7406\u4e0d\u6210\u4e3a\u74f6\u9888\uff0c\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u8005\u5f02\u6b65\u52a0\u8f7d\u6570\u636e\u3002\n\n3. **\u6a21\u578b\u7b80\u5316**\uff1a\u9009\u62e9\u4f7f\u7528\u66f4\u7b80\u5355\u7684\u6a21\u578b\u6216\u51cf\u5c11\u6a21\u578b\u590d\u6742\u6027\uff0c\u8fd9\u53ef\u4ee5\u4f7f\u8bad\u7ec3\u901f\u5ea6\u52a0\u5feb\u3002\n\n4. **\u5b66\u4e60\u7387\u8c03\u6574**\uff1a\u5b9e\u65bd\u5b66\u4e60\u7387\u8c03\u5ea6\u5668\uff0c\u5982warmup\u968f\u540e\u9010\u6b65\u51cf\u5c11\u5b66\u4e60\u7387\u6216\u4f7f\u7528\u81ea\u9002\u5e94\u5b66\u4e60\u7387\u7b97\u6cd5\u3002\n\n5. **\u6570\u636e\u964d\u7ef4**\uff1a\u5982\u679c\u8f93\u5165\u7279\u5f81\u975e\u5e38\u591a\uff0c\u4f7f\u7528PCA\u6216\u5176\u4ed6\u964d\u7ef4\u65b9\u6cd5\u53ef\u80fd\u52a0\u5feb\u8bad\u7ec3\u901f\u5ea6\u3002\n\n6. **\u589e\u5927\u6279\u5904\u7406\u5927\u5c0f\uff08batch size\uff09**\uff1a\u5982\u679c\u4f60\u7684\u786c\u4ef6\u8d44\u6e90\u5141\u8bb8\u7684\u8bdd\uff0c\u589e\u52a0\u6279\u5904\u7406\u5927\u5c0f\u53ef\u4ee5\u66f4\u5145\u5206\u5730\u5229\u7528GPU\u8d44\u6e90\uff0c\u52a0\u5feb\u8bad\u7ec3\u8fc7\u7a0b\u3002\n\n7. **\u6570\u636e\u5b50\u96c6**\uff1a\u5728\u8f83\u5c0f\u7684\u6570\u636e\u5b50\u96c6\u4e0a\u5148\u8fdb\u884c\u6a21\u578b\u539f\u578b\u8bbe\u8ba1\u548c\u8fed\u4ee3\uff0c\u627e\u5230\u6bd4\u8f83\u597d\u7684\u6a21\u578b\u548c\u8d85\u53c2\u6570\u4e4b\u540e\u518d\u6269\u5c55\u5230\u66f4\u5927\u7684\u6570\u636e\u96c6\u3002\n\n8. **\u8f6c\u79fb\u5b66\u4e60**\uff1a\u5982\u679c\u53ef\u80fd\uff0c\u4f7f\u7528\u5df2\u7ecf\u5728\u7c7b\u4f3c\u4efb\u52a1\u4e0a\u9884\u8bad\u7ec3\u8fc7\u7684\u6a21\u578b\uff0c\u8fd9\u6837\u901a\u5e38\u53ea\u9700\u8c03\u6574\u540e\u51e0\u5c42\u5373\u53ef\u3002\n\n\u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u7684\u8bad\u7ec3\uff0c\u901a\u5e38\u9700\u8981\u4e00\u4e2a\u5e73\u8861\u901f\u5ea6\u548c\u51c6\u786e\u6027\u7684\u7efc\u5408\u7b56\u7565\u3002\u53ef\u80fd\u8fd8\u9700\u8981\u4e00\u4e9b\u7279\u5b9a\u7684\u786c\u4ef6\u4f18\u5316\uff0c\u6bd4\u5982\u4f7f\u7528\u9ad8\u901f\u7684SSD\u786c\u76d8\u6765\u5b58\u50a8\u6570\u636e\u96c6\uff0c\u4ee5\u51cf\u5c11\u4ece\u78c1\u76d8\u52a0\u8f7d\u6570\u636e\u7684\u65f6\u95f4\u3002\n\n\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8fd9\u4e9b\u7b56\u7565\u53ef\u80fd\u9700\u8981\u6839\u636e\u5177\u4f53\u7684\u786c\u4ef6\u914d\u7f6e\u3001\u6570\u636e\u96c6\u7279\u70b9\u548c\u6a21\u578b\u8981\u6c42\u7b49\u56e0\u7d20\u6765\u8c03\u6574\u3002\u6700\u91cd\u8981\u7684\u8fd8\u662f\u8981\u7406\u89e3\u6570\u636e\u548c\u6a21\u578b\u7684\u7279\u70b9\uff0c\u4ece\u800c\u627e\u5230\u5408\u9002\u7684\u89e3\u51b3\u65b9\u6848\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":734,"n_turns_in_conversation":16,"n_words":22,"source_row_offset":51243,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"b14f058883c61a97bd2a65b2","turn_index":2,"user_text":"\u6211\u7684\u8fd9\u79cd\u901f\u5ea6\u5728\u4ebf\u7ea7\u7684\u6570\u636e\u91cf\u4e2d\uff0c\u7b97\u6162\u7684\u5417"},{"assistant_initiates":false,"asst_text":"\u4ece\u60a8\u7684\u8bad\u7ec3\u65e5\u5fd7\u770b\uff0c\u5230\u4e86\u7b2c135\u4e2aepoch\u65f6\uff0c\u6a21\u578b\u7684\u9a8c\u8bc1\u51c6\u786e\u7387\u63a5\u8fd168%\u3002\u53ef\u4ee5\u9884\u8ba1\uff0c\u968f\u7740\u8bad\u7ec3\u7684\u7ee7\u7eed\uff0c\u5728\u4e0d\u51fa\u73b0\u4e25\u91cd\u8fc7\u62df\u5408\u7684\u60c5\u51b5\u4e0b\uff0c\u6a21\u578b\u51c6\u786e\u7387\u53ef\u80fd\u4f1a\u7ee7\u7eed\u5c0f\u5e45\u63d0\u5347\u3002\n\n\u4f46\u8981\u786e\u5b9a\u4e00\u4e2a\u201c\u7406\u60f3\u7684\u51c6\u786e\u7387\u201d\uff0c\u5fc5\u987b\u8003\u8651\u4ee5\u4e0b\u56e0\u7d20\uff1a\n\n1. **\u57fa\u51c6\u6027\u80fd\uff08Baseline Performance\uff09**\uff1a\u5982\u679c\u5b58\u5728\u9884\u5148\u5b9a\u4e49\u7684\u57fa\u7ebf\u6a21\u578b\u6216\u4e1a\u754c\u6807\u51c6\uff0c\u8fd9\u5c06\u6709\u52a9\u4e8e\u8bbe\u5b9a\u4e00\u4e2a\u76ee\u6807\u51c6\u786e\u7387\u3002\u4f8b\u5982\uff0c\u5982\u679c\u5f53\u524d\u4efb\u52a1\u662f\u4e00\u4e2a\u6807\u51c6\u7684\u56fe\u50cf\u8bc6\u522b\u95ee\u9898\uff0c\u5e76\u4e14\u5df2\u77e5\u6700\u4f73\u6a21\u578b\u80fd\u591f\u8fbe\u523090%\u7684\u51c6\u786e\u7387\uff0c\u90a3\u4e48\u60a8\u7684\u6a21\u578b\u51c6\u786e\u7387\u5e94\u5c3d\u91cf\u63a5\u8fd1\u8fd9\u4e2a\u6c34\u5e73\u3002\n\n2. **\u95ee\u9898\u96be\u5ea6**\uff1a\u6709\u4e9b\u95ee\u9898\u672c\u8d28\u4e0a\u5c31\u6bd4\u5176\u4ed6\u95ee\u9898\u66f4\u96be\u89e3\u51b3\uff0c\u56e0\u6b64\u5373\u4f7f\u9ad8\u7ea7\u6a21\u578b\u4e5f\u53ef\u80fd\u65e0\u6cd5\u8fbe\u5230\u975e\u5e38\u9ad8\u7684\u51c6\u786e\u7387\u3002\n\n3. **\u6570\u636e\u8d28\u91cf**\uff1a\u6570\u636e\u7684\u566a\u58f0\u6c34\u5e73\u3001\u6807\u6ce8\u7684\u51c6\u786e\u6027\u7b49\u56e0\u7d20\uff0c\u90fd\u4f1a\u5f71\u54cd\u5230\u6a21\u578b\u80fd\u8fbe\u5230\u7684\u6700\u9ad8\u51c6\u786e\u7387\u3002\n\n4. **\u7c7b\u522b\u5e73\u8861**\uff1a\u5982\u679c\u60a8\u5728\u5904\u7406\u4e00\u4e2a\u5206\u7c7b\u95ee\u9898\uff0c\u5176\u4e2d\u67d0\u4e9b\u7c7b\u522b\u7684\u6837\u672c\u6570\u91cf\u8fdc\u591a\u4e8e\u5176\u4ed6\u7c7b\u522b\uff0c\u90a3\u4e48\u8fd9\u53ef\u80fd\u4f1a\u504f\u5411\u4e8e\u5728\u90a3\u4e9b\u5177\u6709\u66f4\u591a\u6837\u672c\u7684\u7c7b\u522b\u4e0a\u83b7\u5f97\u66f4\u597d\u7684\u8868\u73b0\u3002\n\n\u7406\u60f3\u7684\u51c6\u786e\u7387\u5e94\u5f53\u662f\u57fa\u4e8e\u4efb\u52a1\u7684\u5177\u4f53\u80cc\u666f\u548c\u4e0a\u8ff0\u56e0\u7d20\u6765\u786e\u5b9a\u7684\u3002\u5982\u679c\u60a8\u76ee\u524d\u7684\u6a21\u578b\u5df2\u7ecf\u8d85\u51fa\u4e86\u4e1a\u754c\u6807\u51c6\uff0c\u6216\u8005\u8fbe\u5230\u4e86\u7c7b\u4f3c\u95ee\u9898\u4e0a\u7684\u5148\u8fdb\u6c34\u5e73\uff0c\u90a3\u4e48\u60a8\u7684\u6a21\u578b\u51c6\u786e\u7387\u5c31\u53ef\u4ee5\u8ba4\u4e3a\u662f\u7406\u60f3\u7684\u3002\u53cd\u4e4b\uff0c\u5982\u679c\u5b83\u4f4e\u4e8e\u671f\u671b\uff0c\u90a3\u4e48\u53ef\u80fd\u8fd8\u9700\u8981\u8fdb\u4e00\u6b65\u8c03\u6574\u6a21\u578b\u3001\u589e\u5f3a\u6570\u636e\u5904\u7406\u6d41\u7a0b\u6216\u8003\u8651\u4f7f\u7528\u4e0d\u540c\u7684\u6a21\u578b\u67b6\u6784\u3002\n\n\u6700\u540e\uff0c\u51c6\u786e\u5ea6\u5e76\u4e0d\u662f\u8861\u91cf\u6240\u6709\u7c7b\u578b\u6a21\u578b\u597d\u574f\u7684\u552f\u4e00\u6307\u6807\uff0c\u5176\u4ed6\u6307\u6807\u5982\u7cbe\u786e\u5ea6\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u3001AUC-ROC\u66f2\u7ebf\u7b49\u53ef\u80fd\u4f1a\u63d0\u4f9b\u989d\u5916\u7684\u6027\u80fd\u4fe1\u606f\uff0c\u7279\u522b\u662f\u5728\u7c7b\u522b\u4e0d\u5e73\u8861\u7684\u60c5\u51b5\u4e0b\u3002\u56e0\u6b64\uff0c\u5728\u8bbe\u7f6e\u76ee\u6807\u51c6\u786e\u7387\u65f6\uff0c\u8bf7\u7efc\u5408\u8003\u8651\u4e0d\u540c\u7684\u6027\u80fd\u6307\u6807\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":678,"n_turns_in_conversation":16,"n_words":14,"source_row_offset":51244,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"02262a973fa7edfd522c5449","turn_index":3,"user_text":"\u6309\u7167\u8fd9\u4e2a\u901f\u5ea6\uff0c\u4f60\u89c9\u5f97\u8dd1\u5b8c\u4e4b\u540e\u7684\u7406\u60f3\u51c6\u786e\u7387\u662f\u591a\u5c11"},{"assistant_initiates":false,"asst_text":"\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f1a\u5173\u6ce8\u9a8c\u8bc1\u96c6\uff08validation set\uff09\u4e0a\u7684\u6027\u80fd\u800c\u4e0d\u4ec5\u4ec5\u662f\u8bad\u7ec3\u96c6\uff08training set\uff09\u7684\u6027\u80fd\uff0c\u539f\u56e0\u5982\u4e0b\uff1a\n\n1. **\u6cdb\u5316\u80fd\u529b**: \u8bad\u7ec3\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u4e3b\u8981\u53cd\u6620\u4e86\u6a21\u578b\u5bf9\u8bad\u7ec3\u6570\u636e\u7684\u62df\u5408\u7a0b\u5ea6\uff0c\u800c\u4e0d\u4e00\u5b9a\u4ee3\u8868\u6a21\u578b\u5bf9\u672a\u77e5\u6570\u636e\u7684\u6cdb\u5316\u80fd\u529b\u3002\u4f7f\u7528\u9a8c\u8bc1\u96c6\uff08\u901a\u5e38\u662f\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u770b\u4e0d\u5230\u7684\u6570\u636e\uff09\u80fd\u66f4\u597d\u5730\u8bc4\u4f30\u6a21\u578b\u5728\u5904\u7406\u65b0\u6570\u636e\u65f6\u7684\u6027\u80fd\u3002\n\n2. **\u8fc7\u62df\u5408**: \u5982\u679c\u4e00\u4e2a\u6a21\u578b\u5b8c\u7f8e\u5730\u8bb0\u4f4f\u4e86\u8bad\u7ec3\u96c6\u6570\u636e\u800c\u6ca1\u6709\u5b66\u5230\u6cdb\u5316\u7684\u80fd\u529b\uff0c\u5b83\u5728\u8bad\u7ec3\u96c6\u4e0a\u7684\u51c6\u786e\u7387\u53ef\u80fd\u4f1a\u975e\u5e38\u9ad8\u3002\u4f46\u8fd9\u6837\u7684\u6a21\u578b\u5728\u65b0\u6570\u636e\u4e0a\u7684\u8868\u73b0\u53ef\u80fd\u5f88\u5dee\uff0c\u56e0\u4e3a\u5b83\u8fc7\u5ea6\u62df\u5408\u4e86\u8bad\u7ec3\u6570\u636e\u4e2d\u7684\u566a\u58f0\u548c\u7279\u5b9a\u7684\u6a21\u5f0f\u3002\n\n3. **\u6a21\u578b\u9009\u62e9\u548c\u8c03\u4f18**: \u5728\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u6211\u4eec\u7ecf\u5e38\u9700\u8981\u8fdb\u884c\u6a21\u578b\u9009\u62e9\uff08model selection\uff09\u548c\u8d85\u53c2\u6570\u8c03\u4f18\uff08hyperparameter tuning\uff09\u3002\u4f7f\u7528\u9a8c\u8bc1\u96c6\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u9009\u62e9\u6027\u80fd\u6700\u4f73\u7684\u6a21\u578b\u548c\u8bbe\u7f6e\uff0c\u800c\u4e0d\u662f\u53ea\u9009\u62e9\u5728\u8bad\u7ec3\u96c6\u4e0a\u8868\u73b0\u826f\u597d\u7684\u6a21\u578b\u548c\u8bbe\u7f6e\u3002\n\n4. **\u8bc4\u4f30\u5ea6\u91cf\u7684\u53ef\u9760\u6027**: \u57fa\u4e8e\u72ec\u7acb\u4e8e\u8bad\u7ec3\u6570\u636e\u7684\u9a8c\u8bc1\u96c6\u6240\u5f97\u5230\u7684\u8bc4\u4f30\u6307\u6807\uff08\u5982\u51c6\u786e\u7387\u3001\u635f\u5931\u51fd\u6570\u7b49\uff09\uff0c\u901a\u5e38\u88ab\u8ba4\u4e3a\u66f4\u53ef\u9760\uff0c\u56e0\u4e3a\u5b83\u4eec\u53cd\u6620\u4e86\u6a21\u578b\u5bf9\u672a\u89c1\u8fc7\u7684\u6570\u636e\u7684\u5b9e\u9645\u8868\u73b0\u3002\n\n\u6700\u7ec8\uff0c\u6211\u4eec\u7684\u76ee\u6807\u662f\u5f00\u53d1\u4e00\u4e2a\u5728\u771f\u5b9e\u4e16\u754c\u5e94\u7528\u4e2d\u8868\u73b0\u826f\u597d\u7684\u6a21\u578b\uff0c\u800c\u9a8c\u8bc1\u96c6\u63d0\u4f9b\u4e86\u4e00\u79cd\u5728\u6a21\u578b\u8fd8\u6ca1\u63a5\u89e6\u6d4b\u8bd5\u96c6\u6216\u771f\u5b9e\u6570\u636e\u524d\uff0c\u8bc4\u4f30\u5176\u53ef\u80fd\u6027\u80fd\u7684\u65b9\u6cd5\u3002\u7136\u800c\uff0c\u9a8c\u8bc1\u96c6\u4e5f\u4e0d\u662f\u7ec8\u6781\u7684\u6027\u80fd\u8861\u91cf\u6807\u51c6\u3002\u5728\u5b8c\u6210\u6a21\u578b\u7684\u9009\u62e9\u548c\u8c03\u4f18\u540e\uff0c\u6211\u4eec\u8fd8\u4f1a\u5728\u4e00\u4e2a\u4ece\u672a\u7528\u4e8e\u8bad\u7ec3\u548c\u9a8c\u8bc1\u7684\u6d4b\u8bd5\u96c6\uff08test set\uff09\u4e0a\u8bc4\u4f30\u6700\u7ec8\u6a21\u578b\u7684\u6027\u80fd\uff0c\u4ee5\u83b7\u5f97\u5bf9\u6a21\u578b\u771f\u5b9e\u6cdb\u5316\u80fd\u529b\u7684\u6700\u7ec8\u8bc4\u4f30\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":667,"n_turns_in_conversation":16,"n_words":20,"source_row_offset":51245,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"cdc48978264933e1aa2c4957","turn_index":4,"user_text":"\u60a8\u4e3a\u4ec0\u4e48\u4e0d\u4f7f\u7528\u8bad\u7ec3\u96c6\u7684\u51c6\u786e\u7387\uff0c\u800c\u662f\u5173\u6ce8\u9a8c\u8bc1\u96c6"},{"assistant_initiates":false,"asst_text":"\u5728\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u65f6\uff0c\u53ef\u80fd\u4f1a\u89c2\u5bdf\u5230\u635f\u5931\u6216\u8005\u51c6\u786e\u7387\u5728\u67d0\u4e2a\u503c\u9644\u8fd1\u6ce2\u52a8\uff0c\u7136\u540e\u518d\u6b21\u5f00\u59cb\u6539\u5584\u6216\u8005\u4e0b\u964d\u7684\u73b0\u8c61\u3002\u8fd9\u4e2a\u73b0\u8c61\u53ef\u80fd\u7531\u591a\u79cd\u56e0\u7d20\u5f15\u8d77\uff1a\n\n1. **\u5c40\u90e8\u6700\u5c0f/\u978d\u70b9**\uff1a\u4f18\u5316\u8fc7\u7a0b\u53ef\u80fd\u6682\u65f6\u5361\u5728\u5c40\u90e8\u6700\u5c0f\u503c\u6216\u978d\u70b9\u9644\u8fd1\u3002\u978d\u70b9\u662f\u6307\u5728\u67d0\u4e00\u65b9\u5411\u662f\u6700\u5927\u503c\uff0c\u5728\u53e6\u4e00\u65b9\u5411\u662f\u6700\u5c0f\u503c\u7684\u70b9\u3002\u5728\u8fd9\u4e9b\u70b9\u9644\u8fd1\uff0c\u68af\u5ea6\u53ef\u80fd\u975e\u5e38\u5fae\u5c0f\uff0c\u5bfc\u81f4\u4f18\u5316\u5668\u9700\u8981\u4e00\u4e9bepoch\u624d\u80fd\u201c\u9003\u8131\u201d\u51fa\u6765\u3002\n\n2. **\u5b66\u4e60\u7387**\uff1a\u5982\u679c\u4f7f\u7528\u7684\u662f\u56fa\u5b9a\u5b66\u4e60\u7387\uff0c\u6a21\u578b\u53ef\u80fd\u5728\u63a5\u8fd1\u6700\u5c0f\u503c\u7684\u65f6\u5019\u6b65\u957f\u592a\u5927\uff0c\u4ece\u800c\u6765\u56de\u201c\u8df3\u8fc7\u201d\u6700\u4f18\u70b9\u3002\u5982\u679c\u4f7f\u7528\u81ea\u9002\u5e94\u5b66\u4e60\u7387\uff0c\u4f18\u5316\u5668\u53ef\u80fd\u5728\u5b66\u4e60\u7387\u51cf\u5c0f\u65f6\u627e\u5230\u4e86\u66f4\u597d\u7684\u4e0b\u964d\u8def\u5f84\uff0c\u4ece\u800c\u7ee7\u7eed\u6539\u5584\u6027\u80fd\u3002\n\n3. **\u6279\u91cf\u5927\u5c0f**\uff1a\u8f83\u5927\u7684\u6279\u91cf\u4f1a\u63d0\u4f9b\u66f4\u7a33\u5b9a\u7684\u68af\u5ea6\u4f30\u8ba1\uff0c\u4f46\u53ef\u80fd\u9700\u8981\u66f4\u957f\u7684\u65f6\u95f4\u624d\u80fd\u9002\u5e94loss surface\u7684\u53d8\u5316\u3002\u8f83\u5c0f\u7684\u6279\u91cf\u4f1a\u5bfc\u81f4\u68af\u5ea6\u4f30\u8ba1\u7684\u65b9\u5dee\u589e\u5927\uff0c\u53ef\u80fd\u6709\u52a9\u4e8e\u8df3\u51fa\u5c40\u90e8\u6700\u5c0f\u503c\uff0c\u4f46\u540c\u65f6\u4e5f\u53ef\u80fd\u5bfc\u81f4\u8bad\u7ec3\u4e0d\u7a33\u5b9a\u3002\n\n4. **\u6570\u636e\u7279\u6027**\uff1a\u8bad\u7ec3\u96c6\u4e2d\u53ef\u80fd\u5305\u542b\u4e00\u4e9b\u5f02\u5e38\u7684\u6216\u566a\u58f0\u8f83\u5927\u7684\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u53ef\u80fd\u4f1a\u5728\u6a21\u578b\u5b66\u4e60\u65f6\u5f15\u8d77\u6ce2\u52a8\u3002\n\n5. **\u6743\u91cd\u521d\u59cb\u5316\u548c\u6b63\u5219\u5316**\uff1a\u4e0d\u540c\u7684\u521d\u59cb\u5316\u548c\u6b63\u5219\u5316\u7b56\u7565\u53ef\u80fd\u4f1a\u5f71\u54cd\u6a21\u578b\u7684\u5b66\u4e60\u8f68\u8ff9\uff0c\u6709\u65f6\u53ef\u80fd\u5bfc\u81f4\u5728\u4f18\u5316\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u5e73\u53f0\u671f\u3002\n\n6. **\u52a8\u91cf\u6216\u5176\u4ed6\u52a0\u901f\u5668**\uff1a\u5982\u679c\u4f7f\u7528\u52a8\u91cf\u6216Adam\u7b49\u4f18\u5316\u7b97\u6cd5\uff0c\u66f4\u65b0\u7684\u5386\u53f2\u53ef\u80fd\u4f1a\u5f71\u54cd\u5f53\u524d\u7684\u68af\u5ea6\u65b9\u5411\u548c\u5927\u5c0f\uff0c\u5bfc\u81f4\u6ce2\u52a8\u3002\n\n7. **\u5f02\u8d28\u6027\u6570\u636e**\uff1a\u8bad\u7ec3\u6570\u636e\u53ef\u80fd\u4e0d\u662f\u5747\u5300\u5206\u5e03\u7684\uff0c\u67d0\u4e9b\u6570\u636e\u6279\u6b21\u53ef\u80fd\u6bd4\u5176\u4ed6\u7684\u96be\u5b66\u4e60\uff0c\u5bfc\u81f4\u6027\u80fd\u6682\u65f6\u505c\u6ede\u6216\u6ce2\u52a8\u3002\n\n\u901a\u5e38\uff0c\u7ecf\u5386\u4e00\u6bb5\u65f6\u95f4\u7684\u6ce2\u52a8\u540e\u7ee7\u7eed\u4e0b\u964d\u6216\u6539\u5584\u662f\u4e00\u4e2a\u6b63\u5e38\u73b0\u8c61\uff0c\u8868\u793a\u6a21\u578b\u4ecd\u5728\u5b66\u4e60\u548c\u9002\u5e94\u6570\u636e\u3002\u5982\u679c\u6ce2\u52a8\u6301\u7eed\u5b58\u5728\uff0c\u53ef\u80fd\u9700\u8981\u8c03\u6574\u8bad\u7ec3\u7b56\u7565\uff0c\u6bd4\u5982\u66f4\u6539\u5b66\u4e60\u7387\u3001\u4f18\u5316\u5668\u8bbe\u7f6e\u6216\u5c1d\u8bd5\u5176\u4ed6\u6b63\u5219\u5316\u6280\u672f\u3002\u540c\u65f6\uff0c\u8fd9\u4e5f\u662f\u4e3a\u4ec0\u4e48\u8bad\u7ec3\u671f\u95f4\u76d1\u89c6\u9a8c\u8bc1\u96c6\u6027\u80fd\u5f88\u91cd\u8981\u7684\u539f\u56e0\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u662f\u5426\u6b63\u5728\u8fc7\u62df\u5408\uff0c\u6216\u8005\u662f\u5426\u9700\u8981\u8c03\u6574\u8bad\u7ec3\u7b56\u7565\u4ee5\u6539\u5584\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":786,"n_turns_in_conversation":16,"n_words":18,"source_row_offset":51246,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"f728933b9ff75db57340340d","turn_index":5,"user_text":"\u4e3a\u4ec0\u4e48\u6a21\u578b\u4f1a\u5728\u4e00\u4e2a\u5e73\u53f0\u6ce2\u52a8\u56db\u4e94\u8f6e\u4e4b\u540e\u5f00\u59cb\u7ee7\u7eed\u4e0b\u964d"},{"assistant_initiates":false,"asst_text":"\u8981\u4f30\u7b97\u5728\u5355\u4e2aNVIDIA RTX 2060\u663e\u5361\u4e0a\u8bad\u7ec3\u5341\u4ebf\u6570\u636e\u6240\u9700\u7684\u65f6\u95f4\uff0c\u9700\u8981\u8003\u8651\u591a\u4e2a\u56e0\u7d20\uff0c\u5982\u6a21\u578b\u590d\u6742\u5ea6\u3001\u6570\u636e\u7684\u7279\u5f81\u7ef4\u5ea6\u3001\u8bad\u7ec3\u7b97\u6cd5\u7684\u6548\u7387\u3001\u6570\u636e\u9884\u5904\u7406\u548c\u52a0\u8f7d\u7684\u65f6\u95f4\u3001\u6279\u6b21\u5927\u5c0f\uff08batch size\uff09\u3001\u6bcf\u4e2aepoch\u7684\u8fed\u4ee3\u6b21\u6570\uff0c\u4ee5\u53ca\u662f\u5426\u5e94\u7528\u4e86\u9ad8\u6548\u7684\u4f18\u5316\u6280\u672f\u7b49\u7b49\u3002\n\n\u4ee5\u4e0b\u662f\u4e00\u4e9b\u53ef\u80fd\u5f71\u54cd\u8bad\u7ec3\u65f6\u95f4\u7684\u4e3b\u8981\u56e0\u7d20\uff1a\n\n1. **\u6a21\u578b\u590d\u6742\u5ea6**\uff1a\u6a21\u578b\u7684\u5c42\u6570\u548c\u53c2\u6570\u6570\u91cf\u76f4\u63a5\u5f71\u54cd\u524d\u5411\u4f20\u64ad\u548c\u53cd\u5411\u4f20\u64ad\u7684\u8ba1\u7b97\u8d1f\u8f7d\u3002\n\n2. **\u6570\u636e\u7ef4\u5ea6**\uff1a\u8f93\u5165\u6570\u636e\u7684\u7ef4\u5ea6\u8d8a\u9ad8\uff0c\u9700\u8981\u7684\u8ba1\u7b97\u91cf\u4e5f\u8d8a\u5927\u3002\n\n3. **\u6279\u91cf\u5927\u5c0f**\uff1a\u6279\u91cf\u5927\u5c0f\u4f1a\u53d7\u5230GPU\u5185\u5b58\u7684\u9650\u5236\u3002\u6279\u91cf\u5927\u5c0f\u8d8a\u5927\uff0cGPU\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u8d8a\u80fd\u5f97\u5230\u5229\u7528\uff0c\u4f46\u8fc7\u5927\u7684\u6279\u91cf\u53ef\u80fd\u53d7\u9650\u4e8e\u663e\u5b58\u5927\u5c0f\u3002\n\n4. **\u6570\u636e\u4f20\u8f93**\uff1a\u6570\u636e\u9700\u8981\u4ece\u78c1\u76d8\u52a0\u8f7d\u5230\u5185\u5b58\uff0c\u518d\u4ece\u5185\u5b58\u4f20\u8f93\u5230GPU\u3002\u5982\u679c\u8fd9\u4e2a\u8fc7\u7a0b\u6ca1\u6709\u5f97\u5230\u5f88\u597d\u7684\u4f18\u5316\uff0c\u4f1a\u6210\u4e3a\u8bad\u7ec3\u7684\u74f6\u9888\u3002\n\n5. **\u4f18\u5316\u7b97\u6cd5\u6548\u7387**\uff1a\u4e0d\u540c\u7684\u4f18\u5316\u7b97\u6cd5\uff08\u5982SGD\u3001Adam\u7b49\uff09\u5c06\u5f71\u54cd\u6536\u655b\u901f\u5ea6\u3002\n\n6. **\u5e76\u884c\u548c\u5206\u5e03\u5f0f\u8bad\u7ec3**\uff1a\u5728\u4e00\u4e2aGPU\u4e0a\u8bad\u7ec3\u5341\u4ebf\u6761\u6570\u636e\u901a\u5e38\u4e0d\u5207\u5b9e\u9645\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5de5\u4e1a\u754c\u4f1a\u901a\u8fc7\u591aGPU\u6216\u5206\u5e03\u5f0f\u8bad\u7ec3\u6765\u52a0\u901f\u8fd9\u4e2a\u8fc7\u7a0b\u3002\n\n7. **epoch\u6570\u91cf**\uff1a\u9700\u8981\u8fd0\u884c\u591a\u5c11\u4e2a\u8bad\u7ec3\u5468\u671f\uff0c\u4ee5\u53ca\u6570\u636e\u662f\u5426\u9700\u8981\u591a\u6b21\u904d\u5386\uff08\u5982\u4f7f\u7528\u8fed\u4ee3\u6b21\u6570\u5c0f\u4e8e\u6570\u636e\u91cf\u7684\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u65b9\u6cd5\uff09\u3002\n\n\u4f30\u7b97\u5355\u4e2aRTX 2060\u663e\u5361\u8bad\u7ec310\u4ebf\u6570\u636e\u7684\u65f6\u95f4\u975e\u5e38\u56f0\u96be\uff0c\u56e0\u4e3a\u8fd9\u53d6\u51b3\u4e8e\u4e0a\u8ff0\u63d0\u5230\u7684\u4f17\u591a\u56e0\u7d20\uff0c\u800c\u4e14\u4e5f\u56e0\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u7684\u4e0d\u540c\uff08\u5206\u7c7b\u3001\u56de\u5f52\u3001\u5f3a\u5316\u5b66\u4e60\u7b49\uff09\u800c\u5f02\u3002\n\n\u5982\u679c\u60a8\u7684\u4efb\u52a1\u786e\u5b9e\u6d89\u53ca\u5230\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u5e76\u6253\u7b97\u5728\u5355\u4e2aGPU\u4e0a\u8fdb\u884c\u8bad\u7ec3\uff0c\u5efa\u8bae\u91c7\u7528\u4ee5\u4e0b\u63aa\u65bd\u6765\u5c3d\u53ef\u80fd\u9ad8\u6548\u5730\u5229\u7528\u786c\u4ef6\u8d44\u6e90\uff1a\n\n- **\u6a21\u578b\u7b80\u5316**\uff1a\u9009\u62e9\u66f4\u5c0f\u6216\u8005\u66f4\u4f18\u5316\u7684\u7f51\u7edc\u3002\n- **\u6570\u636e\u964d\u7ef4**\uff1a\u901a\u8fc7\u7279\u5f81\u9009\u62e9\u6216\u964d\u7ef4\u6280\u672f\u51cf\u5c0f\u8f93\u5165\u7684\u5927\u5c0f\u3002\n- **\u6709\u6548\u7684\u6570\u636e\u52a0\u8f7d**\uff1a\u4f7f\u7528\u8db3\u591f\u5feb\u7684\u6570\u636e\u9884\u5904\u7406\u548c\u52a0\u8f7d\u6d41\u7a0b\uff0c\u4f8b\u5982\u4f7f\u7528\u591a\u7ebf\u7a0b\u6570\u636e\u52a0\u8f7d\u7b49\u3002\n- **\u5b66\u4e60\u7387\u8c03\u5ea6**\uff1a\u4f7f\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\u6765\u52a0\u901f\u8bad\u7ec3\u8fc7\u7a0b\u53ca\u6536\u655b\u3002\n- **\u6a21\u578b\u526a\u679d**\uff1a\u53bb\u9664\u4e0d\u90a3\u4e48\u91cd\u8981\u7684\u6743\u91cd\u6765\u51cf\u5c11\u6a21\u578b\u5927\u5c0f\uff0c\u4ee5\u51cf\u5c11\u8ba1\u7b97\u8d1f\u62c5\u3002\n\n\u5373\u4fbf\u5982\u6b64\uff0c\u82e5\u8981\u5728\u5355\u4e2aRTX 2060\u4e0a\u8bad\u7ec3\u5341\u4ebf\u6570\u636e\u70b9\u7684\u6a21\u578b\uff0c\u6211\u4eec\u4ecd\u7136\u53ef\u4ee5\u9884\u6d4b\u8fd9\u5c06\u662f\u4e00\u4e2a\u6301\u7eed\u591a\u5929\u751a\u81f3\u6570\u5468\u7684\u957f\u671f\u8fc7\u7a0b\u3002\u5982\u679c\u53ef\u80fd\uff0c\u5bfb\u627e\u63d0\u9ad8\u786c\u4ef6\u8d44\u6e90\uff08\u4f8b\u5982\u5347\u7ea7\u5230\u66f4\u5f3a\u5927\u7684GPU\u6216\u4f7f\u7528\u591aGPU\u7cfb\u7edf\uff09\u6216\u51cf\u5c11\u6570\u636e\u89c4\u6a21\u7684\u65b9\u6cd5\u4f1a\u662f\u66f4\u597d\u7684\u9009\u62e9\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":983,"n_turns_in_conversation":16,"n_words":35,"source_row_offset":51247,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"433b1e02547928fbf8f4acd1","turn_index":6,"user_text":"\u5de5\u4e1a\u754c\u8bad\u7ec3\u5341\u4ebf\u6570\u636e\uff0c\u5728\u5355\u4e2a2060\u663e\u5361\u4e0a\uff0c\u4e00\u822c\u8981\u51e0\u5929"},{"assistant_initiates":false,"asst_text":"\u4ece\u4f60\u7684\u63cf\u8ff0\u4e2d\uff0c\u51c6\u786e\u7387\u5728\u7ecf\u8fc7\u4e00\u6bb5\u65f6\u95f4\u7684\u6ce2\u52a8\u540e\u4e0a\u5347\u5230\u4e8666%\uff0c\u8fd9\u610f\u5473\u7740\u6a21\u578b\u4ecd\u7136\u5728\u5b66\u4e60\uff0c\u5e76\u4e14\u6700\u7ec8\u627e\u5230\u4e86\u4e00\u4e2a\u6bd4\u4e4b\u524d\u66f4\u597d\u7684\u70b9\u6765\u51cf\u5c11\u635f\u5931\u3002\u8fd9\u79cd\u884c\u4e3a\u53ef\u80fd\u8868\u660e\uff0c\u5728\u76ee\u524d\u7684\u8d85\u53c2\u6570\u8bbe\u5b9a\uff08\u5305\u62ec\u5b66\u4e60\u7387\uff09\u4e0b\uff0c\u6a21\u578b\u786e\u5b9e\u8fd8\u6709\u6539\u8fdb\u7684\u7a7a\u95f4\u3002\n\n\u662f\u5426\u9700\u8981\u8c03\u6574\u5b66\u4e60\u7387\u53d6\u51b3\u4e8e\u591a\u4e2a\u56e0\u7d20\uff1a\n\n1. **\u8bad\u7ec3\u901f\u5ea6**\uff1a\u5982\u679c\u4f60\u8ba4\u4e3a\u8bad\u7ec3\u7684\u901f\u5ea6\u53ef\u4ee5\u63a5\u53d7\uff0c\u5e76\u4e14\u51c6\u786e\u7387\u4ecd\u5728\u7a33\u6b65\u4e0a\u5347\uff0c\u90a3\u4e48\u5f53\u524d\u7684\u5b66\u4e60\u7387\u53ef\u80fd\u662f\u9002\u5f53\u7684\u3002\n\n2. **\u6536\u655b\u72b6\u6001**\uff1a\u5982\u679c\u51c6\u786e\u7387\u63d0\u5347\u5f88\u7f13\u6162\uff0c\u6216\u8005\u51fa\u73b0\u4e86\u660e\u663e\u7684\u505c\u6ede\uff08\u5982\u591a\u4e2aepochs\u4e2d\u90fd\u6ca1\u6709\u4efb\u4f55\u6539\u5584\uff09\uff0c\u90a3\u4e48\u589e\u52a0\u5b66\u4e60\u7387\u53ef\u80fd\u6709\u52a9\u4e8e\u52a0\u5feb\u5b66\u4e60\u8fc7\u7a0b\u3002\n\n3. **\u8fc7\u62df\u5408\u60c5\u51b5**\uff1a\u5982\u679c\u4f60\u89c2\u5bdf\u5230\u8bad\u7ec3\u51c6\u786e\u7387\u660e\u663e\u9ad8\u4e8e\u9a8c\u8bc1\u51c6\u786e\u7387\uff0c\u5e76\u4e14\u5dee\u5f02\u968f\u7740\u65f6\u95f4\u8d8a\u6765\u8d8a\u5927\uff0c\u90a3\u4e48\u8fd9\u53ef\u80fd\u662f\u8fc7\u62df\u5408\u7684\u4fe1\u53f7\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u589e\u52a0\u5b66\u4e60\u7387\u672a\u5fc5\u662f\u6700\u4f73\u9009\u62e9\uff0c\u53cd\u800c\u53ef\u80fd\u9700\u8981\u589e\u52a0\u6b63\u5219\u5316\u6216\u91c7\u53d6\u65e9\u505c\uff08early stopping\uff09\u7b49\u63aa\u65bd\u3002\n\n4. **\u6ce2\u52a8\u60c5\u51b5**\uff1a\u4e00\u4e9b\u6ce2\u52a8\u662f\u6b63\u5e38\u7684\uff0c\u4f46\u5982\u679c\u6ce2\u52a8\u975e\u5e38\u5267\u70c8\uff0c\u8fd9\u53ef\u80fd\u8868\u660e\u5b66\u4e60\u7387\u8fc7\u9ad8\u3002\u5982\u679c\u6ce2\u52a8\u76f8\u5bf9\u8f83\u5c0f\u5e76\u4e14\u6a21\u578b\u6700\u7ec8\u80fd\u591f\u4ece\u6ce2\u52a8\u4e2d\u6062\u590d\u5e76\u7ee7\u7eed\u6539\u8fdb\uff0c\u90a3\u4e48\u5f53\u524d\u7684\u5b66\u4e60\u7387\u53ef\u80fd\u662f\u53ef\u884c\u7684\u3002\n\n\u800c\u4e14\uff0c\u8bb0\u4f4f\u5b66\u4e60\u7387\u4e0d\u662f\u4e00\u4e2a\u5b64\u7acb\u7684\u53c2\u6570\uff0c\u5b83\u548c\u5176\u4ed6\u8d85\u53c2\u6570\uff08\u5982\u6279\u91cf\u5927\u5c0f\u3001\u6b63\u5219\u5316\u7cfb\u6570\u7b49\uff09\u4e00\u8d77\u51b3\u5b9a\u4e86\u6a21\u578b\u8bad\u7ec3\u7684\u884c\u4e3a\u3002\u6709\u65f6\u5019\uff0c\u6539\u53d8\u5176\u4e2d\u4e00\u4e2a\u8d85\u53c2\u6570\uff0c\u53ef\u80fd\u9700\u8981\u91cd\u65b0\u8c03\u6574\u5176\u4ed6\u8d85\u53c2\u6570\u624d\u80fd\u5f97\u5230\u6700\u4f73\u7ed3\u679c\u3002\n\n\u5982\u679c\u4f60\u6709\u5145\u5206\u7684\u65f6\u95f4\u548c\u8d44\u6e90\uff0c\u5c1d\u8bd5\u4f7f\u7528\u4e00\u4e2a\u5b66\u4e60\u7387\u8303\u56f4\u8fdb\u884c\u8bad\u7ec3\u5e76\u76d1\u89c6\u6a21\u578b\u7684\u8868\u73b0\uff0c\u6216\u8005\u91c7\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\uff0c\u5982\u9010\u6b65\u51cf\u5c0f\u5b66\u4e60\u7387\u6216\u8005\u4f7f\u7528\u9884\u70ed\uff08warm-up\uff09\u7b56\u7565\uff0c\u90fd\u662f\u4e0d\u9519\u7684\u65b9\u6cd5\u6765\u627e\u5230\u6700\u4f73\u7684\u5b66\u4e60\u7387\u8bbe\u7f6e\u3002\u8fd9\u4e9b\u7b56\u7565\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u7cfb\u7edf\u5730\u8c03\u6574\u5b66\u4e60\u7387\u5e76\u8bc4\u4f30\u5f71\u54cd\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":711,"n_turns_in_conversation":16,"n_words":14,"source_row_offset":51248,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"fb8f78cafb60d8cc188becd4","turn_index":7,"user_text":"\u7ecf\u5386\u4e867\u8f6e\u6ce2\u52a8\uff0c\u51c6\u786e\u7387\u7ec8\u4e8e\u5347\u5230\u4e8666%\uff0c\u8fd9\u662f\u5426\u8bf4\u660e\u6211\u7684\u5b66\u4e60\u7387\u8bbe\u7f6e\u7684\u662f\u6070\u5f53\u7684\uff0c\u4e0d\u9700\u8981\u8c03\u9ad8\uff1a"},{"assistant_initiates":false,"asst_text":"\u6839\u636e\u60a8\u63d0\u4f9b\u7684\u8bad\u7ec3\u65e5\u5fd7\uff0c\u6a21\u578b\u7684\u9a8c\u8bc1\u51c6\u786e\u7387\u786e\u5b9e\u5728\u9010\u6e10\u63d0\u9ad8\uff0c\u5373\u4f7f\u5728\u67d0\u4e9b\u65f6\u671f\u7ecf\u5386\u6ce2\u52a8\u3002\u5b66\u4e60\u7387\u662f\u5426\u5408\u9002\uff0c\u4e0d\u80fd\u53ea\u6839\u636e\u51c6\u786e\u7387\u7684\u63d0\u5347\u5224\u65ad\uff0c\u9700\u8981\u8003\u8651\u591a\u4e2a\u56e0\u7d20\u3002\u60a8\u76ee\u524d\u7684\u5b66\u4e60\u7387\u53ef\u80fd\u5141\u8bb8\u4e86\u6a21\u578b\u5728\u53c2\u6570\u7a7a\u95f4\u4e2d\u7a33\u5b9a\u5730\u63a2\u7d22\uff0c\u5e76\u6700\u7ec8\u627e\u5230\u4e86\u635f\u5931\u51fd\u6570\u66f4\u4f4e\u7684\u533a\u57df\u3002\n\n\u8fd9\u91cc\u6709\u51e0\u4e2a\u5173\u952e\u70b9\u53ef\u4ee5\u8003\u8651\uff1a\n\n- \u5982\u679c\u6a21\u578b\u51c6\u786e\u7387\u4ecd\u7136\u5728\u7a33\u5065\u5730\u63d0\u5347\uff0c\u5e76\u6ca1\u6709\u51fa\u73b0\u660e\u663e\u8ff9\u8c61\u8868\u660e\u8fdb\u6b65\u505c\u6ede\u4e0d\u524d\uff0c\u90a3\u4e48\u5f53\u524d\u7684\u5b66\u4e60\u7387\u53ef\u80fd\u662f\u9002\u5f53\u7684\u3002\n\n- \u8fdb\u884c\u4e86140\u4e2aepoch\u540e\uff0c\u51c6\u786e\u7387\u4ece52%\u63d0\u5347\u5230\u4e86\u5927\u7ea668%\uff0c\u8fd9\u610f\u5473\u7740\u6a21\u578b\u6b63\u5728\u5b66\u4e60\u5e76\u4e14\u6b63\u5728\u8d70\u5411\u66f4\u597d\u7684\u6027\u80fd\u3002\u5982\u6b64\u7a33\u5b9a\u7684\u63d0\u5347\u901a\u5e38\u8bf4\u660e\u5b66\u4e60\u7387\u8bbe\u5b9a\u662f\u5408\u9002\u7684\u3002\n\n- \u867d\u7136\u7ecf\u5386\u4e86\u51e0\u8f6e\u6ce2\u52a8\uff0c\u4f46\u6700\u7ec8\u51c6\u786e\u7387\u4ecd\u7136\u80fd\u591f\u63d0\u5347\uff0c\u8fd9\u4e5f\u53ef\u80fd\u662f\u56e0\u4e3a\u6a21\u578b\u8d8a\u8fc7\u4e86\u67d0\u4e9b\u5c40\u90e8\u6700\u5c0f\u70b9\uff0c\u6216\u8005\u662f\u56e0\u4e3a\u68af\u5ea6\u4e0b\u964d\u4e2d\u7684\u968f\u673a\u6027\u800c\u5076\u7136\u53d1\u73b0\u4e86\u66f4\u597d\u7684\u5b66\u4e60\u8def\u5f84\u3002\n\n- \u4ece\u6ce2\u52a8\u4e4b\u540e\u91cd\u56de\u51c6\u786e\u7387\u63d0\u5347\u7684\u8f68\u9053\uff0c\u8bf4\u660e\u6a21\u578b\u6709\u80fd\u529b\u4ece\u6ce2\u52a8\u4e2d\u6062\u590d\uff0c\u5e76\u627e\u5230\u68af\u5ea6\u4f18\u5316\u7684\u65b9\u5411\uff0c\u8fd9\u4e5f\u6307\u793a\u5f53\u524d\u7684\u5b66\u4e60\u7387\u8fd8\u53ef\u4ee5\u7ee7\u7eed\u4f7f\u7528\u3002\n\n\u603b\u7684\u6765\u8bf4\uff0c\u53ea\u8981\u8bad\u7ec3\u6307\u6807\uff08\u5982\u635f\u5931\u548c\u51c6\u786e\u7387\uff09\u7ee7\u7eed\u5411\u597d\u7684\u65b9\u5411\u53d1\u5c55\uff0c\u5e76\u4e14\u6ca1\u6709\u51fa\u73b0\u8fc7\u62df\u5408\u7684\u8ff9\u8c61\uff0c\u5c31\u6682\u65f6\u6ca1\u6709\u5fc5\u8981\u8c03\u6574\u5b66\u4e60\u7387\u3002\u4e0d\u8fc7\uff0c\u5982\u679c\u6539\u8fdb\u53d8\u5f97\u7f13\u6162\u6216\u8005\u505c\u6ede\uff0c\u53ef\u80fd\u9700\u8981\u8003\u8651\u8c03\u6574\u5b66\u4e60\u7387\u6216\u8005\u4f7f\u7528\u5b66\u4e60\u7387\u8c03\u5ea6\u7b56\u7565\uff0c\u5982\u5b66\u4e60\u7387\u8870\u51cf\u6216\u5468\u671f\u6027\u8c03\u6574\u7b49\u3002\u6b64\u5916\uff0c\u5728\u5b9e\u9645\u7684\u6a21\u578b\u8bad\u7ec3\u4e2d\uff0c\u901a\u5e38\u4f1a\u4f7f\u7528\u8bf8\u5982\u9a8c\u8bc1\u96c6\u4e0a\u51c6\u786e\u7387\u63d0\u524d\u505c\u6b62\u589e\u957f\u65f6\u964d\u4f4e\u5b66\u4e60\u7387\u7684\u7b56\u7565\uff0c\u6765\u52a8\u6001\u8c03\u6574\u5b66\u4e60\u7387\u3002","behavior":"degenerate_repetition","conversation_hash":"012a556fef19c9e49a06fd3bbf8a6bb8","conversation_redacted":false,"conversation_toxic":false,"country":"Japan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-4-1106-preview","n_chars":36841,"n_turns_in_conversation":16,"n_words":4960,"source_row_offset":51249,"source_shard":"turns-00016.parquet","timestamp":"Sun, 28 Jan 2024 18:44:10 GMT","turn_id":"c135fd3d6ab302b6afaa5a02","turn_index":8,"user_text":"Epoch 1/1000\n7680/7680 [==============================] - 182s 23ms/step - loss: 0.7901 - accuracy: 0.5237 - precision: 0.5238 - recall: 0.5217 - val_loss: 0.7325 - val_accuracy: 0.5495 - val_precision: 0.5408 - val_recall: 0.6552 - lr: 1.0000e-04\nEpoch 2/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.7174 - accuracy: 0.5440 - precision: 0.5445 - recall: 0.5387 - val_loss: 0.7015 - val_accuracy: 0.5575 - val_precision: 0.5527 - val_recall: 0.6037 - lr: 1.0000e-04\nEpoch 3/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6977 - accuracy: 0.5515 - precision: 0.5520 - recall: 0.5467 - val_loss: 0.6902 - val_accuracy: 0.5622 - val_precision: 0.5592 - val_recall: 0.5875 - lr: 1.0000e-04\nEpoch 4/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6904 - accuracy: 0.5560 - precision: 0.5564 - recall: 0.5517 - val_loss: 0.6856 - val_accuracy: 0.5659 - val_precision: 0.5636 - val_recall: 0.5842 - lr: 1.0000e-04\nEpoch 5/1000\n7680/7680 [==============================] - 174s 23ms/step - loss: 0.6873 - accuracy: 0.5591 - precision: 0.5595 - recall: 0.5556 - val_loss: 0.6831 - val_accuracy: 0.5684 - val_precision: 0.5650 - val_recall: 0.5947 - lr: 1.0000e-04\nEpoch 6/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6853 - accuracy: 0.5614 - precision: 0.5617 - recall: 0.5588 - val_loss: 0.6814 - val_accuracy: 0.5712 - val_precision: 0.5678 - val_recall: 0.5969 - lr: 1.0000e-04\nEpoch 7/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6841 - accuracy: 0.5634 - precision: 0.5639 - recall: 0.5592 - val_loss: 0.6798 - val_accuracy: 0.5734 - val_precision: 0.5699 - val_recall: 0.5982 - lr: 1.0000e-04\nEpoch 8/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6828 - accuracy: 0.5658 - precision: 0.5662 - recall: 0.5626 - val_loss: 0.6786 - val_accuracy: 0.5755 - val_precision: 0.5718 - val_recall: 0.6011 - lr: 1.0000e-04\nEpoch 9/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6822 - accuracy: 0.5675 - precision: 0.5676 - recall: 0.5663 - val_loss: 0.6776 - val_accuracy: 0.5774 - val_precision: 0.5718 - val_recall: 0.6166 - lr: 1.0000e-04\nEpoch 10/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6811 - accuracy: 0.5694 - precision: 0.5695 - recall: 0.5687 - val_loss: 0.6766 - val_accuracy: 0.5795 - val_precision: 0.5734 - val_recall: 0.6213 - lr: 1.0000e-04\nEpoch 11/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6803 - accuracy: 0.5713 - precision: 0.5715 - recall: 0.5699 - val_loss: 0.6757 - val_accuracy: 0.5807 - val_precision: 0.5768 - val_recall: 0.6060 - lr: 1.0000e-04\nEpoch 12/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6794 - accuracy: 0.5733 - precision: 0.5738 - recall: 0.5701 - val_loss: 0.6745 - val_accuracy: 0.5834 - val_precision: 0.5812 - val_recall: 0.5968 - lr: 1.0000e-04\nEpoch 13/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6789 - accuracy: 0.5734 - precision: 0.5736 - recall: 0.5719 - val_loss: 0.6744 - val_accuracy: 0.5827 - val_precision: 0.5775 - val_recall: 0.6164 - lr: 1.0000e-04\nEpoch 14/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6778 - accuracy: 0.5757 - precision: 0.5760 - recall: 0.5739 - val_loss: 0.6729 - val_accuracy: 0.5852 - val_precision: 0.5826 - val_recall: 0.6010 - lr: 1.0000e-04\nEpoch 15/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6771 - accuracy: 0.5777 - precision: 0.5785 - recall: 0.5727 - val_loss: 0.6721 - val_accuracy: 0.5868 - val_precision: 0.5815 - val_recall: 0.6195 - lr: 1.0000e-04\nEpoch 16/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6765 - accuracy: 0.5783 - precision: 0.5784 - recall: 0.5774 - val_loss: 0.6713 - val_accuracy: 0.5882 - val_precision: 0.5818 - val_recall: 0.6272 - lr: 1.0000e-04\nEpoch 17/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6758 - accuracy: 0.5793 - precision: 0.5796 - recall: 0.5775 - val_loss: 0.6694 - val_accuracy: 0.5924 - val_precision: 0.5906 - val_recall: 0.6023 - lr: 1.0000e-04\nEpoch 18/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6752 - accuracy: 0.5803 - precision: 0.5806 - recall: 0.5782 - val_loss: 0.6696 - val_accuracy: 0.5912 - val_precision: 0.5859 - val_recall: 0.6219 - lr: 1.0000e-04\nEpoch 19/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6743 - accuracy: 0.5827 - precision: 0.5827 - recall: 0.5829 - val_loss: 0.6678 - val_accuracy: 0.5946 - val_precision: 0.5912 - val_recall: 0.6134 - lr: 1.0000e-04\nEpoch 20/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6737 - accuracy: 0.5845 - precision: 0.5850 - recall: 0.5815 - val_loss: 0.6669 - val_accuracy: 0.5946 - val_precision: 0.5911 - val_recall: 0.6138 - lr: 1.0000e-04\nEpoch 21/1000\n7680/7680 [==============================] - 173s 22ms/step - loss: 0.6727 - accuracy: 0.5850 - precision: 0.5851 - recall: 0.5846 - val_loss: 0.6661 - val_accuracy: 0.5961 - val_precision: 0.5924 - val_recall: 0.6161 - lr: 1.0000e-04\nEpoch 22/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6722 - accuracy: 0.5861 - precision: 0.5864 - recall: 0.5842 - val_loss: 0.6659 - val_accuracy: 0.5967 - val_precision: 0.5931 - val_recall: 0.6158 - lr: 1.0000e-04\nEpoch 23/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6712 - accuracy: 0.5881 - precision: 0.5885 - recall: 0.5853 - val_loss: 0.6646 - val_accuracy: 0.5983 - val_precision: 0.5942 - val_recall: 0.6199 - lr: 1.0000e-04\nEpoch 24/1000\n7680/7680 [==============================] - 165s 21ms/step - loss: 0.6711 - accuracy: 0.5880 - precision: 0.5883 - recall: 0.5859 - val_loss: 0.6638 - val_accuracy: 0.5999 - val_precision: 0.5944 - val_recall: 0.6290 - lr: 1.0000e-04\nEpoch 25/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6701 - accuracy: 0.5896 - precision: 0.5898 - recall: 0.5889 - val_loss: 0.6625 - val_accuracy: 0.6017 - val_precision: 0.5991 - val_recall: 0.6144 - lr: 1.0000e-04\nEpoch 26/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6695 - accuracy: 0.5914 - precision: 0.5915 - recall: 0.5911 - val_loss: 0.6617 - val_accuracy: 0.6036 - val_precision: 0.6004 - val_recall: 0.6197 - lr: 1.0000e-04\nEpoch 27/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6689 - accuracy: 0.5914 - precision: 0.5918 - recall: 0.5894 - val_loss: 0.6611 - val_accuracy: 0.6032 - val_precision: 0.6010 - val_recall: 0.6141 - lr: 1.0000e-04\nEpoch 28/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6678 - accuracy: 0.5929 - precision: 0.5930 - recall: 0.5919 - val_loss: 0.6605 - val_accuracy: 0.6047 - val_precision: 0.5987 - val_recall: 0.6347 - lr: 1.0000e-04\nEpoch 29/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6674 - accuracy: 0.5925 - precision: 0.5924 - recall: 0.5927 - val_loss: 0.6606 - val_accuracy: 0.6046 - val_precision: 0.5998 - val_recall: 0.6286 - lr: 1.0000e-04\nEpoch 30/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6668 - accuracy: 0.5949 - precision: 0.5952 - recall: 0.5935 - val_loss: 0.6591 - val_accuracy: 0.6060 - val_precision: 0.6036 - val_recall: 0.6178 - lr: 1.0000e-04\nEpoch 31/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6658 - accuracy: 0.5957 - precision: 0.5959 - recall: 0.5943 - val_loss: 0.6574 - val_accuracy: 0.6079 - val_precision: 0.6040 - val_recall: 0.6267 - lr: 1.0000e-04\nEpoch 32/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6652 - accuracy: 0.5966 - precision: 0.5967 - recall: 0.5958 - val_loss: 0.6562 - val_accuracy: 0.6092 - val_precision: 0.6076 - val_recall: 0.6163 - lr: 1.0000e-04\nEpoch 33/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6646 - accuracy: 0.5980 - precision: 0.5982 - recall: 0.5971 - val_loss: 0.6554 - val_accuracy: 0.6109 - val_precision: 0.6085 - val_recall: 0.6220 - lr: 1.0000e-04\nEpoch 34/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6640 - accuracy: 0.5989 - precision: 0.5992 - recall: 0.5974 - val_loss: 0.6556 - val_accuracy: 0.6102 - val_precision: 0.6069 - val_recall: 0.6257 - lr: 1.0000e-04\nEpoch 35/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6633 - accuracy: 0.6002 - precision: 0.6006 - recall: 0.5979 - val_loss: 0.6541 - val_accuracy: 0.6120 - val_precision: 0.6115 - val_recall: 0.6143 - lr: 1.0000e-04\nEpoch 36/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6629 - accuracy: 0.6002 - precision: 0.6007 - recall: 0.5978 - val_loss: 0.6537 - val_accuracy: 0.6131 - val_precision: 0.6117 - val_recall: 0.6192 - lr: 1.0000e-04\nEpoch 37/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6621 - accuracy: 0.6010 - precision: 0.6011 - recall: 0.6003 - val_loss: 0.6523 - val_accuracy: 0.6151 - val_precision: 0.6150 - val_recall: 0.6156 - lr: 1.0000e-04\nEpoch 38/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6613 - accuracy: 0.6030 - precision: 0.6038 - recall: 0.5989 - val_loss: 0.6520 - val_accuracy: 0.6155 - val_precision: 0.6131 - val_recall: 0.6258 - lr: 1.0000e-04\nEpoch 39/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6608 - accuracy: 0.6030 - precision: 0.6036 - recall: 0.5999 - val_loss: 0.6508 - val_accuracy: 0.6161 - val_precision: 0.6149 - val_recall: 0.6209 - lr: 1.0000e-04\nEpoch 40/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6600 - accuracy: 0.6041 - precision: 0.6052 - recall: 0.5992 - val_loss: 0.6503 - val_accuracy: 0.6165 - val_precision: 0.6140 - val_recall: 0.6272 - lr: 1.0000e-04\nEpoch 41/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6594 - accuracy: 0.6053 - precision: 0.6068 - recall: 0.5987 - val_loss: 0.6489 - val_accuracy: 0.6188 - val_precision: 0.6212 - val_recall: 0.6088 - lr: 1.0000e-04\nEpoch 42/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6589 - accuracy: 0.6061 - precision: 0.6080 - recall: 0.5974 - val_loss: 0.6487 - val_accuracy: 0.6194 - val_precision: 0.6213 - val_recall: 0.6115 - lr: 1.0000e-04\nEpoch 43/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6581 - accuracy: 0.6079 - precision: 0.6088 - recall: 0.6037 - val_loss: 0.6470 - val_accuracy: 0.6210 - val_precision: 0.6221 - val_recall: 0.6167 - lr: 1.0000e-04\nEpoch 44/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6576 - accuracy: 0.6075 - precision: 0.6091 - recall: 0.6003 - val_loss: 0.6468 - val_accuracy: 0.6214 - val_precision: 0.6181 - val_recall: 0.6352 - lr: 1.0000e-04\nEpoch 45/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6572 - accuracy: 0.6083 - precision: 0.6092 - recall: 0.6042 - val_loss: 0.6458 - val_accuracy: 0.6230 - val_precision: 0.6212 - val_recall: 0.6304 - lr: 1.0000e-04\nEpoch 46/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6565 - accuracy: 0.6092 - precision: 0.6102 - recall: 0.6046 - val_loss: 0.6451 - val_accuracy: 0.6237 - val_precision: 0.6258 - val_recall: 0.6155 - lr: 1.0000e-04\nEpoch 47/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6556 - accuracy: 0.6106 - precision: 0.6113 - recall: 0.6071 - val_loss: 0.6445 - val_accuracy: 0.6247 - val_precision: 0.6257 - val_recall: 0.6206 - lr: 1.0000e-04\nEpoch 48/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6557 - accuracy: 0.6101 - precision: 0.6115 - recall: 0.6038 - val_loss: 0.6442 - val_accuracy: 0.6244 - val_precision: 0.6250 - val_recall: 0.6218 - lr: 1.0000e-04\nEpoch 49/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6549 - accuracy: 0.6112 - precision: 0.6128 - recall: 0.6042 - val_loss: 0.6425 - val_accuracy: 0.6267 - val_precision: 0.6247 - val_recall: 0.6346 - lr: 1.0000e-04\nEpoch 50/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6540 - accuracy: 0.6130 - precision: 0.6145 - recall: 0.6064 - val_loss: 0.6418 - val_accuracy: 0.6279 - val_precision: 0.6255 - val_recall: 0.6374 - lr: 1.0000e-04\nEpoch 51/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6535 - accuracy: 0.6143 - precision: 0.6152 - recall: 0.6101 - val_loss: 0.6409 - val_accuracy: 0.6285 - val_precision: 0.6314 - val_recall: 0.6176 - lr: 1.0000e-04\nEpoch 52/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6528 - accuracy: 0.6141 - precision: 0.6155 - recall: 0.6081 - val_loss: 0.6402 - val_accuracy: 0.6297 - val_precision: 0.6272 - val_recall: 0.6394 - lr: 1.0000e-04\nEpoch 53/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6521 - accuracy: 0.6149 - precision: 0.6164 - recall: 0.6084 - val_loss: 0.6394 - val_accuracy: 0.6302 - val_precision: 0.6338 - val_recall: 0.6169 - lr: 1.0000e-04\nEpoch 54/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6518 - accuracy: 0.6161 - precision: 0.6173 - recall: 0.6108 - val_loss: 0.6391 - val_accuracy: 0.6309 - val_precision: 0.6270 - val_recall: 0.6461 - lr: 1.0000e-04\nEpoch 55/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6510 - accuracy: 0.6164 - precision: 0.6177 - recall: 0.6106 - val_loss: 0.6379 - val_accuracy: 0.6324 - val_precision: 0.6289 - val_recall: 0.6458 - lr: 1.0000e-04\nEpoch 56/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6508 - accuracy: 0.6166 - precision: 0.6182 - recall: 0.6096 - val_loss: 0.6367 - val_accuracy: 0.6341 - val_precision: 0.6336 - val_recall: 0.6363 - lr: 1.0000e-04\nEpoch 57/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6499 - accuracy: 0.6185 - precision: 0.6201 - recall: 0.6119 - val_loss: 0.6368 - val_accuracy: 0.6330 - val_precision: 0.6307 - val_recall: 0.6418 - lr: 1.0000e-04\nEpoch 58/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6495 - accuracy: 0.6182 - precision: 0.6200 - recall: 0.6105 - val_loss: 0.6361 - val_accuracy: 0.6351 - val_precision: 0.6353 - val_recall: 0.6343 - lr: 1.0000e-04\nEpoch 59/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6494 - accuracy: 0.6188 - precision: 0.6206 - recall: 0.6112 - val_loss: 0.6355 - val_accuracy: 0.6356 - val_precision: 0.6355 - val_recall: 0.6358 - lr: 1.0000e-04\nEpoch 60/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6486 - accuracy: 0.6192 - precision: 0.6203 - recall: 0.6143 - val_loss: 0.6351 - val_accuracy: 0.6366 - val_precision: 0.6376 - val_recall: 0.6332 - lr: 1.0000e-04\nEpoch 61/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6478 - accuracy: 0.6205 - precision: 0.6222 - recall: 0.6136 - val_loss: 0.6329 - val_accuracy: 0.6380 - val_precision: 0.6357 - val_recall: 0.6463 - lr: 1.0000e-04\nEpoch 62/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6470 - accuracy: 0.6221 - precision: 0.6236 - recall: 0.6158 - val_loss: 0.6322 - val_accuracy: 0.6385 - val_precision: 0.6351 - val_recall: 0.6514 - lr: 1.0000e-04\nEpoch 63/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6466 - accuracy: 0.6227 - precision: 0.6244 - recall: 0.6158 - val_loss: 0.6320 - val_accuracy: 0.6390 - val_precision: 0.6389 - val_recall: 0.6391 - lr: 1.0000e-04\nEpoch 64/1000\n7680/7680 [==============================] - 179s 23ms/step - loss: 0.6465 - accuracy: 0.6226 - precision: 0.6240 - recall: 0.6168 - val_loss: 0.6311 - val_accuracy: 0.6398 - val_precision: 0.6366 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 65/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6457 - accuracy: 0.6226 - precision: 0.6243 - recall: 0.6158 - val_loss: 0.6303 - val_accuracy: 0.6408 - val_precision: 0.6368 - val_recall: 0.6551 - lr: 1.0000e-04\nEpoch 66/1000\n7680/7680 [==============================] - 176s 23ms/step - loss: 0.6449 - accuracy: 0.6239 - precision: 0.6257 - recall: 0.6169 - val_loss: 0.6293 - val_accuracy: 0.6425 - val_precision: 0.6408 - val_recall: 0.6488 - lr: 1.0000e-04\nEpoch 67/1000\n7680/7680 [==============================] - 175s 23ms/step - loss: 0.6448 - accuracy: 0.6247 - precision: 0.6266 - recall: 0.6173 - val_loss: 0.6294 - val_accuracy: 0.6430 - val_precision: 0.6389 - val_recall: 0.6579 - lr: 1.0000e-04\nEpoch 68/1000\n7680/7680 [==============================] - 178s 23ms/step - loss: 0.6444 - accuracy: 0.6250 - precision: 0.6269 - recall: 0.6176 - val_loss: 0.6288 - val_accuracy: 0.6429 - val_precision: 0.6392 - val_recall: 0.6560 - lr: 1.0000e-04\nEpoch 69/1000\n7680/7680 [==============================] - 177s 23ms/step - loss: 0.6438 - accuracy: 0.6261 - precision: 0.6273 - recall: 0.6215 - val_loss: 0.6282 - val_accuracy: 0.6440 - val_precision: 0.6426 - val_recall: 0.6486 - lr: 1.0000e-04\nEpoch 70/1000\n7680/7680 [==============================] - 184s 24ms/step - loss: 0.6429 - accuracy: 0.6262 - precision: 0.6281 - recall: 0.6185 - val_loss: 0.6274 - val_accuracy: 0.6448 - val_precision: 0.6402 - val_recall: 0.6611 - lr: 1.0000e-04\nEpoch 71/1000\n7680/7680 [==============================] - 183s 24ms/step - loss: 0.6432 - accuracy: 0.6265 - precision: 0.6285 - recall: 0.6187 - val_loss: 0.6258 - val_accuracy: 0.6466 - val_precision: 0.6467 - val_recall: 0.6463 - lr: 1.0000e-04\nEpoch 72/1000\n7680/7680 [==============================] - 182s 24ms/step - loss: 0.6424 - accuracy: 0.6268 - precision: 0.6291 - recall: 0.6177 - val_loss: 0.6256 - val_accuracy: 0.6466 - val_precision: 0.6490 - val_recall: 0.6383 - lr: 1.0000e-04\nEpoch 73/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6415 - accuracy: 0.6286 - precision: 0.6302 - recall: 0.6223 - val_loss: 0.6252 - val_accuracy: 0.6474 - val_precision: 0.6448 - val_recall: 0.6564 - lr: 1.0000e-04\nEpoch 74/1000\n7680/7680 [==============================] - 204s 27ms/step - loss: 0.6414 - accuracy: 0.6291 - precision: 0.6306 - recall: 0.6236 - val_loss: 0.6248 - val_accuracy: 0.6473 - val_precision: 0.6432 - val_recall: 0.6616 - lr: 1.0000e-04\nEpoch 75/1000\n7680/7680 [==============================] - 207s 27ms/step - loss: 0.6404 - accuracy: 0.6289 - precision: 0.6307 - recall: 0.6218 - val_loss: 0.6244 - val_accuracy: 0.6481 - val_precision: 0.6471 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 76/1000\n7680/7680 [==============================] - 205s 27ms/step - loss: 0.6404 - accuracy: 0.6298 - precision: 0.6320 - recall: 0.6215 - val_loss: 0.6225 - val_accuracy: 0.6499 - val_precision: 0.6483 - val_recall: 0.6553 - lr: 1.0000e-04\nEpoch 77/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6399 - accuracy: 0.6303 - precision: 0.6325 - recall: 0.6221 - val_loss: 0.6221 - val_accuracy: 0.6502 - val_precision: 0.6480 - val_recall: 0.6575 - lr: 1.0000e-04\nEpoch 78/1000\n7680/7680 [==============================] - 200s 26ms/step - loss: 0.6393 - accuracy: 0.6312 - precision: 0.6335 - recall: 0.6227 - val_loss: 0.6218 - val_accuracy: 0.6509 - val_precision: 0.6491 - val_recall: 0.6569 - lr: 1.0000e-04\nEpoch 79/1000\n7680/7680 [==============================] - 209s 27ms/step - loss: 0.6392 - accuracy: 0.6311 - precision: 0.6329 - recall: 0.6242 - val_loss: 0.6214 - val_accuracy: 0.6512 - val_precision: 0.6488 - val_recall: 0.6592 - lr: 1.0000e-04\nEpoch 80/1000\n7680/7680 [==============================] - 209s 27ms/step - loss: 0.6384 - accuracy: 0.6324 - precision: 0.6344 - recall: 0.6246 - val_loss: 0.6208 - val_accuracy: 0.6512 - val_precision: 0.6487 - val_recall: 0.6599 - lr: 1.0000e-04\nEpoch 81/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6377 - accuracy: 0.6326 - precision: 0.6346 - recall: 0.6252 - val_loss: 0.6200 - val_accuracy: 0.6521 - val_precision: 0.6486 - val_recall: 0.6639 - lr: 1.0000e-04\nEpoch 82/1000\n7680/7680 [==============================] - 214s 28ms/step - loss: 0.6374 - accuracy: 0.6339 - precision: 0.6359 - recall: 0.6265 - val_loss: 0.6193 - val_accuracy: 0.6535 - val_precision: 0.6532 - val_recall: 0.6545 - lr: 1.0000e-04\nEpoch 83/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6367 - accuracy: 0.6344 - precision: 0.6366 - recall: 0.6262 - val_loss: 0.6179 - val_accuracy: 0.6541 - val_precision: 0.6549 - val_recall: 0.6516 - lr: 1.0000e-04\nEpoch 84/1000\n7680/7680 [==============================] - 213s 28ms/step - loss: 0.6362 - accuracy: 0.6347 - precision: 0.6375 - recall: 0.6245 - val_loss: 0.6176 - val_accuracy: 0.6549 - val_precision: 0.6539 - val_recall: 0.6582 - lr: 1.0000e-04\nEpoch 85/1000\n7680/7680 [==============================] - 208s 27ms/step - loss: 0.6359 - accuracy: 0.6349 - precision: 0.6375 - recall: 0.6258 - val_loss: 0.6191 - val_accuracy: 0.6538 - val_precision: 0.6483 - val_recall: 0.6723 - lr: 1.0000e-04\nEpoch 86/1000\n7680/7680 [==============================] - 211s 27ms/step - loss: 0.6359 - accuracy: 0.6346 - precision: 0.6370 - recall: 0.6255 - val_loss: 0.6163 - val_accuracy: 0.6565 - val_precision: 0.6562 - val_recall: 0.6574 - lr: 1.0000e-04\nEpoch 87/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6355 - accuracy: 0.6353 - precision: 0.6372 - recall: 0.6282 - val_loss: 0.6170 - val_accuracy: 0.6557 - val_precision: 0.6524 - val_recall: 0.6663 - lr: 1.0000e-04\nEpoch 88/1000\n7680/7680 [==============================] - 194s 25ms/step - loss: 0.6347 - accuracy: 0.6357 - precision: 0.6377 - recall: 0.6283 - val_loss: 0.6159 - val_accuracy: 0.6571 - val_precision: 0.6584 - val_recall: 0.6530 - lr: 1.0000e-04\nEpoch 89/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6344 - accuracy: 0.6369 - precision: 0.6392 - recall: 0.6286 - val_loss: 0.6150 - val_accuracy: 0.6585 - val_precision: 0.6568 - val_recall: 0.6637 - lr: 1.0000e-04\nEpoch 90/1000\n7680/7680 [==============================] - 196s 25ms/step - loss: 0.6340 - accuracy: 0.6373 - precision: 0.6397 - recall: 0.6289 - val_loss: 0.6142 - val_accuracy: 0.6585 - val_precision: 0.6591 - val_recall: 0.6567 - lr: 1.0000e-04\nEpoch 91/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6338 - accuracy: 0.6373 - precision: 0.6399 - recall: 0.6283 - val_loss: 0.6140 - val_accuracy: 0.6592 - val_precision: 0.6559 - val_recall: 0.6698 - lr: 1.0000e-04\nEpoch 92/1000\n7680/7680 [==============================] - 191s 25ms/step - loss: 0.6329 - accuracy: 0.6387 - precision: 0.6409 - recall: 0.6306 - val_loss: 0.6125 - val_accuracy: 0.6607 - val_precision: 0.6644 - val_recall: 0.6494 - lr: 1.0000e-04\nEpoch 93/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6325 - accuracy: 0.6387 - precision: 0.6408 - recall: 0.6310 - val_loss: 0.6122 - val_accuracy: 0.6612 - val_precision: 0.6577 - val_recall: 0.6722 - lr: 1.0000e-04\nEpoch 94/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6318 - accuracy: 0.6392 - precision: 0.6414 - recall: 0.6318 - val_loss: 0.6112 - val_accuracy: 0.6623 - val_precision: 0.6606 - val_recall: 0.6674 - lr: 1.0000e-04\nEpoch 95/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6317 - accuracy: 0.6397 - precision: 0.6423 - recall: 0.6306 - val_loss: 0.6116 - val_accuracy: 0.6616 - val_precision: 0.6637 - val_recall: 0.6550 - lr: 1.0000e-04\nEpoch 96/1000\n7680/7680 [==============================] - 196s 26ms/step - loss: 0.6310 - accuracy: 0.6406 - precision: 0.6438 - recall: 0.6293 - val_loss: 0.6112 - val_accuracy: 0.6620 - val_precision: 0.6629 - val_recall: 0.6594 - lr: 1.0000e-04\nEpoch 97/1000\n7680/7680 [==============================] - 195s 25ms/step - loss: 0.6311 - accuracy: 0.6406 - precision: 0.6428 - recall: 0.6328 - val_loss: 0.6106 - val_accuracy: 0.6623 - val_precision: 0.6623 - val_recall: 0.6623 - lr: 1.0000e-04\nEpoch 98/1000\n7680/7680 [==============================] - 193s 25ms/step - loss: 0.6305 - accuracy: 0.6409 - precision: 0.6435 - recall: 0.6318 - val_loss: 0.6103 - val_accuracy: 0.6634 - val_precision: 0.6663 - val_recall: 0.6545 - lr: 1.0000e-04\nEpoch 99/1000\n7680/7680 [==============================] - 190s 25ms/step - loss: 0.6300 - accuracy: 0.6415 - precision: 0.6440 - recall: 0.6329 - val_loss: 0.6102 - val_accuracy: 0.6629 - val_precision: 0.6598 - val_recall: 0.6725 - lr: 1.0000e-04\nEpoch 100/1000\n7680/7680 [==============================] - 185s 24ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6449 - recall: 0.6331 - val_loss: 0.6097 - val_accuracy: 0.6629 - val_precision: 0.6617 - val_recall: 0.6667 - lr: 1.0000e-04\nEpoch 101/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6297 - accuracy: 0.6422 - precision: 0.6448 - recall: 0.6334 - val_loss: 0.6085 - val_accuracy: 0.6651 - val_precision: 0.6627 - val_recall: 0.6722 - lr: 1.0000e-04\nEpoch 102/1000\n7680/7680 [==============================] - 203s 26ms/step - loss: 0.6294 - accuracy: 0.6416 - precision: 0.6444 - recall: 0.6321 - val_loss: 0.6079 - val_accuracy: 0.6655 - val_precision: 0.6668 - val_recall: 0.6617 - lr: 1.0000e-04\nEpoch 103/1000\n7680/7680 [==============================] - 214s 28ms/step - loss: 0.6287 - accuracy: 0.6426 - precision: 0.6454 - recall: 0.6332 - val_loss: 0.6060 - val_accuracy: 0.6669 - val_precision: 0.6669 - val_recall: 0.6670 - lr: 1.0000e-04\nEpoch 104/1000\n7680/7680 [==============================] - 212s 28ms/step - loss: 0.6282 - accuracy: 0.6438 - precision: 0.6463 - recall: 0.6355 - val_loss: 0.6068 - val_accuracy: 0.6662 - val_precision: 0.6632 - val_recall: 0.6756 - lr: 1.0000e-04\nEpoch 105/1000\n7680/7680 [==============================] - 203s 26ms/step - loss: 0.6279 - accuracy: 0.6437 - precision: 0.6462 - recall: 0.6354 - val_loss: 0.6059 - val_accuracy: 0.6678 - val_precision: 0.6693 - val_recall: 0.6636 - lr: 1.0000e-04\nEpoch 106/1000\n7680/7680 [==============================] - 201s 26ms/step - loss: 0.6274 - accuracy: 0.6445 - precision: 0.6470 - recall: 0.6360 - val_loss: 0.6043 - val_accuracy: 0.6688 - val_precision: 0.6693 - val_recall: 0.6673 - lr: 1.0000e-04\nEpoch 107/1000\n7680/7680 [==============================] - 200s 26ms/step - loss: 0.6269 - accuracy: 0.6456 - precision: 0.6481 - recall: 0.6371 - val_loss: 0.6045 - val_accuracy: 0.6690 - val_precision: 0.6677 - val_recall: 0.6727 - lr: 1.0000e-04\nEpoch 108/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6267 - accuracy: 0.6447 - precision: 0.6476 - recall: 0.6352 - val_loss: 0.6042 - val_accuracy: 0.6692 - val_precision: 0.6637 - val_recall: 0.6858 - lr: 1.0000e-04\nEpoch 109/1000\n7680/7680 [==============================] - 198s 26ms/step - loss: 0.6263 - accuracy: 0.6461 - precision: 0.6486 - recall: 0.6376 - val_loss: 0.6033 - val_accuracy: 0.6706 - val_precision: 0.6703 - val_recall: 0.6714 - lr: 1.0000e-04\nEpoch 110/1000\n7680/7680 [==============================] - 197s 26ms/step - loss: 0.6256 - accuracy: 0.6473 - precision: 0.6502 - recall: 0.6376 - val_loss: 0.6042 - val_accuracy: 0.6695 - val_precision: 0.6740 - val_recall: 0.6564 - lr: 1.0000e-04\nEpoch 111/1000\n7680/7680 [==============================] - 198s 26ms/step - loss: 0.6254 - accuracy: 0.6468 - precision: 0.6500 - recall: 0.6359 - val_loss: 0.6013 - val_accuracy: 0.6719 - val_precision: 0.6745 - val_recall: 0.6644 - lr: 1.0000e-04\nEpoch 112/1000\n7680/7680 [==============================] - 199s 26ms/step - loss: 0.6247 - accuracy: 0.6476 - precision: 0.6500 - recall: 0.6398 - val_loss: 0.6018 - val_accuracy: 0.6715 - val_precision: 0.6716 - val_recall: 0.6710 - lr: 1.0000e-04\nEpoch 113/1000\n7680/7680 [==============================] - 197s 26ms/step - loss: 0.6246 - accuracy: 0.6479 - precision: 0.6506 - recall: 0.6387 - val_loss: 0.6010 - val_accuracy: 0.6720 - val_precision: 0.6724 - val_recall: 0.6707 - lr: 1.0000e-04\nEpoch 114/1000\n7680/7680 [==============================] - 180s 23ms/step - loss: 0.6242 - accuracy: 0.6486 - precision: 0.6512 - recall: 0.6401 - val_loss: 0.6023 - val_accuracy: 0.6716 - val_precision: 0.6731 - val_recall: 0.6672 - lr: 1.0000e-04\nEpoch 115/1000\n7680/7680 [==============================] - 219s 28ms/step - loss: 0.6239 - accuracy: 0.6484 - precision: 0.6507 - recall: 0.6405 - val_loss: 0.5999 - val_accuracy: 0.6736 - val_precision: 0.6742 - val_recall: 0.6718 - lr: 1.0000e-04\nEpoch 116/1000\n7680/7680 [==============================] - 221s 29ms/step - loss: 0.6234 - accuracy: 0.6488 - precision: 0.6512 - recall: 0.6408 - val_loss: 0.6024 - val_accuracy: 0.6723 - val_precision: 0.6694 - val_recall: 0.6808 - lr: 1.0000e-04\nEpoch 117/1000\n7680/7680 [==============================] - 192s 25ms/step - loss: 0.6231 - accuracy: 0.6499 - precision: 0.6524 - recall: 0.6414 - val_loss: 0.5994 - val_accuracy: 0.6744 - val_precision: 0.6734 - val_recall: 0.6775 - lr: 1.0000e-04\nEpoch 118/1000\n7680/7680 [==============================] - 170s 22ms/step - loss: 0.6229 - accuracy: 0.6494 - precision: 0.6515 - recall: 0.6426 - val_loss: 0.5990 - val_accuracy: 0.6750 - val_precision: 0.6792 - val_recall: 0.6632 - lr: 1.0000e-04\nEpoch 119/1000\n7680/7680 [==============================] - 186s 24ms/step - loss: 0.6222 - accuracy: 0.6505 - precision: 0.6530 - recall: 0.6425 - val_loss: 0.5972 - val_accuracy: 0.6762 - val_precision: 0.6784 - val_recall: 0.6703 - lr: 1.0000e-04\nEpoch 120/1000\n7680/7680 [==============================] - 194s 25ms/step - loss: 0.6221 - accuracy: 0.6507 - precision: 0.6528 - recall: 0.6439 - val_loss: 0.5986 - val_accuracy: 0.6749 - val_precision: 0.6706 - val_recall: 0.6876 - lr: 1.0000e-04\nEpoch 121/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6213 - accuracy: 0.6523 - precision: 0.6551 - recall: 0.6432 - val_loss: 0.5971 - val_accuracy: 0.6771 - val_precision: 0.6772 - val_recall: 0.6765 - lr: 1.0000e-04\nEpoch 122/1000\n7680/7680 [==============================] - 187s 24ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6553 - recall: 0.6413 - val_loss: 0.5974 - val_accuracy: 0.6766 - val_precision: 0.6752 - val_recall: 0.6806 - lr: 1.0000e-04\nEpoch 123/1000\n7680/7680 [==============================] - 196s 25ms/step - loss: 0.6202 - accuracy: 0.6523 - precision: 0.6550 - recall: 0.6438 - val_loss: 0.5956 - val_accuracy: 0.6781 - val_precision: 0.6790 - val_recall: 0.6758 - lr: 1.0000e-04\nEpoch 124/1000\n7680/7680 [==============================] - 202s 26ms/step - loss: 0.6212 - accuracy: 0.6520 - precision: 0.6544 - recall: 0.6442 - val_loss: 0.5952 - val_accuracy: 0.6782 - val_precision: 0.6775 - val_recall: 0.6803 - lr: 1.0000e-04\nEpoch 125/1000\n7680/7680 [==============================] - 196s 26ms/step - loss: 0.6200 - accuracy: 0.6534 - precision: 0.6558 - recall: 0.6456 - val_loss: 0.5947 - val_accuracy: 0.6784 - val_precision: 0.6789 - val_recall: 0.6772 - lr: 1.0000e-04\nEpoch 126/1000\n7680/7680 [==============================] - 201s 26ms/step - loss: 0.6195 - accuracy: 0.6525 - precision: 0.6553 - recall: 0.6435 - val_loss: 0.5951 - val_accuracy: 0.6794 - val_precision: 0.6790 - val_recall: 0.6806 - lr: 1.0000e-04\nEpoch 127/1000\n7680/7680 [==============================] - 189s 25ms/step - loss: 0.6194 - accuracy: 0.6537 - precision: 0.6559 - recall: 0.6465 - val_loss: 0.5931 - val_accuracy: 0.6800 - val_precision: 0.6811 - val_recall: 0.6771 - lr: 1.0000e-04\nEpoch 128/1000\n7680/7680 [==============================] - 188s 24ms/step - loss: 0.6193 - accuracy: 0.6538 - precision: 0.6562 - recall: 0.6462 - val_loss: 0.5942 - val_accuracy: 0.6802 - val_precision: 0.6792 - val_recall: 0.6830 - lr: 1.0000e-04\nEpoch 129/1000\n7680/7680 [==============================] - 187s 24ms/step - loss: 0.6189 - accuracy: 0.6543 - precision: 0.6569 - recall: 0.6460 - val_loss: 0.5926 - val_accuracy: 0.6812 - val_precision: 0.6829 - val_recall: 0.6766 - lr: 1.0000e-04\nEpoch 130/1000\n7680/7680 [==============================] - 185s 24ms/step - loss: 0.6184 - accuracy: 0.6554 - precision: 0.6583 - recall: 0.6463 - val_loss: 0.5935 - val_accuracy: 0.6806 - val_precision: 0.6807 - val_recall: 0.6804 - lr: 1.0000e-04\nEpoch 131/1000\n7680/7680 [==============================] - 168s 22ms/step - loss: 0.6186 - accuracy: 0.6551 - precision: 0.6573 - recall: 0.6481 - val_loss: 0.5934 - val_accuracy: 0.6806 - val_precision: 0.6814 - val_recall: 0.6785 - lr: 1.0000e-04\nEpoch 132/1000\n7680/7680 [==============================] - 166s 22ms/step - loss: 0.6176 - accuracy: 0.6550 - precision: 0.6570 - recall: 0.6486 - val_loss: 0.5920 - val_accuracy: 0.6815 - val_precision: 0.6836 - val_recall: 0.6755 - lr: 1.0000e-04\nEpoch 133/1000\n7680/7680 [==============================] - 168s 22ms/step - loss: 0.6173 - accuracy: 0.6553 - precision: 0.6578 - recall: 0.6476 - val_loss: 0.5918 - val_accuracy: 0.6822 - val_precision: 0.6812 - val_recall: 0.6849 - lr: 1.0000e-04\nEpoch 134/1000\n7680/7680 [==============================] - 164s 21ms/step - loss: 0.6165 - accuracy: 0.6566 - precision: 0.6592 - recall: 0.6485 - val_loss: 0.5903 - val_accuracy: 0.6835 - val_precision: 0.6818 - val_recall: 0.6880 - lr: 1.0000e-04\nEpoch 135/1000\n7680/7680 [==============================] - 173s 23ms/step - loss: 0.6172 - accuracy: 0.6562 - precision: 0.6593 - recall: 0.6463 - val_loss: 0.5896 - val_accuracy: 0.6841 - val_precision: 0.6874 - val_recall: 0.6752 - lr: 1.0000e-04\nEpoch 136/1000\n7680/7680 [==============================] - 172s 22ms/step - loss: 0.6167 - accuracy: 0.6561 - precision: 0.6587 - recall: 0.6479 - val_loss: 0.5908 - val_accuracy: 0.6823 - val_precision: 0.6815 - val_recall: 0.6846 - lr: 1.0000e-04\nEpoch 137/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6158 - accuracy: 0.6578 - precision: 0.6600 - recall: 0.6511 - val_loss: 0.5901 - val_accuracy: 0.6834 - val_precision: 0.6805 - val_recall: 0.6913 - lr: 1.0000e-04\nEpoch 138/1000\n7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6585 - precision: 0.6601 - recall: 0.6537 - val_loss: 0.5887 - val_accuracy: 0.6850 - val_precision: 0.6860 - val_recall: 0.6824 - lr: 1.0000e-04\nEpoch 139/1000\n7680/7680 [==============================] - 165s 21ms/step - loss: 0.6150 - accuracy: 0.6592 - precision: 0.6616 - recall: 0.6517 - val_loss: 0.5871 - val_accuracy: 0.6859 - val_precision: 0.6851 - val_recall: 0.6881 - lr: 1.0000e-04\nEpoch 140/1000\n7680/7680 [==============================] - 171s 22ms/step - loss: 0.6151 - accuracy: 0.6579 - precision: 0.6602 - recall: 0.6507 - val_loss: 0.5879 - val_accuracy: 0.6862 - val_precision: 0.6877 - val_recall: 0.6823 - lr: 1.0000e-04\nEpoch 141/1000\n7680/7680 [==============================] - 165s 22ms/step - loss: 0.6151 - accuracy: 0.6575 - precision: 0.6602 - recall: 0.6489 - val_loss: 0.5878 - val_accuracy: 0.6861 - val_precision: 0.6831 - val_recall: 0.6944 - lr: 1.0000e-04\nEpoch 142/1000\n7680/7680 [==============================] - 168s 22ms/step - loss: 0.6136 - accuracy: 0.6597 - precision: 0.6621 - recall: 0.6522 - val_loss: 0.5880 - val_accuracy: 0.6865 - val_precision: 0.6851 - val_recall: 0.6905 - lr: 1.0000e-04\nEpoch 143/1000\n7680/7680 [==============================] - 167s 22ms/step - loss: 0.6139 - accuracy: 0.6590 - precision: 0.6612 - recall: 0.6523 - val_loss: 0.5855 - val_accuracy: 0.6880 - val_precision: 0.6903 - val_recall: 0.6819 - lr: 1.0000e-04\nEpoch 144/1000\n7680/7680 [==============================] - 169s 22ms/step - loss: 0.6136 - accuracy: 0.6598 - precision: 0.6621 - recall: 0.6525 - val_loss: 0.5848 - val_accuracy: 0.6885 - val_precision: 0.6861 - val_recall: 0.6949 - lr: 1.0000e-04\nEpoch 145/1000\n7680/7680 [==============================] - 165s 21ms/step - loss: 0.6131 - accuracy: 0.6607 - precision: 0.6630 - recall: 0.6535 - val_loss: 0.5849 - val_accuracy: 0.6887 - val_precision: 0.6861 - val_recall: 0.6957 - lr: 1.0000e-04\nEpoch 146/1000\n5844/7680 [=====================>........] - ETA: 30s - loss: 0.6127 - accuracy: 0.6611 - precision: 0.6628 - recall: 0.6564"}],"shard":{"index":16,"name":"turns-00016.parquet","rows":85868}}