--- license: mit base_model: gpt2 tags: - generated_from_keras_callback model-index: - name: deneme_spor results: [] --- # deneme_spor This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.9093 - Validation Loss: 5.9538 - Epoch: 149 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -963, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.1978 | 8.9070 | 0 | | 8.7400 | 8.5517 | 1 | | 8.4947 | 8.3909 | 2 | | 8.3502 | 8.2608 | 3 | | 8.2126 | 8.1241 | 4 | | 8.0688 | 7.9827 | 5 | | 7.9232 | 7.8449 | 6 | | 7.7844 | 7.7107 | 7 | | 7.6446 | 7.5719 | 8 | | 7.4919 | 7.4263 | 9 | | 7.3429 | 7.2975 | 10 | | 7.2042 | 7.1774 | 11 | | 7.0643 | 7.0685 | 12 | | 6.9229 | 6.9668 | 13 | | 6.7836 | 6.8770 | 14 | | 6.6425 | 6.7752 | 15 | | 6.4982 | 6.6895 | 16 | | 6.3539 | 6.5963 | 17 | | 6.2035 | 6.5170 | 18 | | 6.0612 | 6.4285 | 19 | | 5.9164 | 6.3429 | 20 | | 5.7708 | 6.2664 | 21 | | 5.6249 | 6.1997 | 22 | | 5.4822 | 6.1348 | 23 | | 5.3368 | 6.0659 | 24 | | 5.1959 | 6.0042 | 25 | | 5.0527 | 5.9525 | 26 | | 4.9070 | 5.9538 | 27 | | 4.9062 | 5.9538 | 28 | | 4.9095 | 5.9538 | 29 | | 4.9056 | 5.9538 | 30 | | 4.9111 | 5.9538 | 31 | | 4.9080 | 5.9538 | 32 | | 4.9072 | 5.9538 | 33 | | 4.9063 | 5.9538 | 34 | | 4.9086 | 5.9538 | 35 | | 4.9081 | 5.9538 | 36 | | 4.9115 | 5.9538 | 37 | | 4.9052 | 5.9538 | 38 | | 4.9073 | 5.9538 | 39 | | 4.9064 | 5.9538 | 40 | | 4.9096 | 5.9538 | 41 | | 4.9093 | 5.9538 | 42 | | 4.9077 | 5.9538 | 43 | | 4.9078 | 5.9538 | 44 | | 4.9073 | 5.9538 | 45 | | 4.9076 | 5.9538 | 46 | | 4.9096 | 5.9538 | 47 | | 4.9093 | 5.9538 | 48 | | 4.9093 | 5.9538 | 49 | | 4.9082 | 5.9538 | 50 | | 4.9106 | 5.9538 | 51 | | 4.9076 | 5.9538 | 52 | | 4.9079 | 5.9538 | 53 | | 4.9093 | 5.9538 | 54 | | 4.9096 | 5.9538 | 55 | | 4.9063 | 5.9538 | 56 | | 4.9071 | 5.9538 | 57 | | 4.9122 | 5.9538 | 58 | | 4.9108 | 5.9538 | 59 | | 4.9072 | 5.9538 | 60 | | 4.9073 | 5.9538 | 61 | | 4.9085 | 5.9538 | 62 | | 4.9080 | 5.9538 | 63 | | 4.9092 | 5.9538 | 64 | | 4.9077 | 5.9538 | 65 | | 4.9087 | 5.9538 | 66 | | 4.9073 | 5.9538 | 67 | | 4.9078 | 5.9538 | 68 | | 4.9102 | 5.9538 | 69 | | 4.9095 | 5.9538 | 70 | | 4.9099 | 5.9538 | 71 | | 4.9081 | 5.9538 | 72 | | 4.9089 | 5.9538 | 73 | | 4.9068 | 5.9538 | 74 | | 4.9091 | 5.9538 | 75 | | 4.9078 | 5.9538 | 76 | | 4.9083 | 5.9538 | 77 | | 4.9067 | 5.9538 | 78 | | 4.9077 | 5.9538 | 79 | | 4.9111 | 5.9538 | 80 | | 4.9088 | 5.9538 | 81 | | 4.9085 | 5.9538 | 82 | | 4.9093 | 5.9538 | 83 | | 4.9086 | 5.9538 | 84 | | 4.9088 | 5.9538 | 85 | | 4.9057 | 5.9538 | 86 | | 4.9104 | 5.9538 | 87 | | 4.9081 | 5.9538 | 88 | | 4.9070 | 5.9538 | 89 | | 4.9076 | 5.9538 | 90 | | 4.9078 | 5.9538 | 91 | | 4.9097 | 5.9538 | 92 | | 4.9082 | 5.9538 | 93 | | 4.9061 | 5.9538 | 94 | | 4.9111 | 5.9538 | 95 | | 4.9067 | 5.9538 | 96 | | 4.9070 | 5.9538 | 97 | | 4.9089 | 5.9538 | 98 | | 4.9051 | 5.9538 | 99 | | 4.9072 | 5.9538 | 100 | | 4.9110 | 5.9538 | 101 | | 4.9094 | 5.9538 | 102 | | 4.9089 | 5.9538 | 103 | | 4.9072 | 5.9538 | 104 | | 4.9072 | 5.9538 | 105 | | 4.9055 | 5.9538 | 106 | | 4.9079 | 5.9538 | 107 | | 4.9075 | 5.9538 | 108 | | 4.9100 | 5.9538 | 109 | | 4.9106 | 5.9538 | 110 | | 4.9081 | 5.9538 | 111 | | 4.9094 | 5.9538 | 112 | | 4.9108 | 5.9538 | 113 | | 4.9082 | 5.9538 | 114 | | 4.9089 | 5.9538 | 115 | | 4.9099 | 5.9538 | 116 | | 4.9063 | 5.9538 | 117 | | 4.9094 | 5.9538 | 118 | | 4.9059 | 5.9538 | 119 | | 4.9096 | 5.9538 | 120 | | 4.9065 | 5.9538 | 121 | | 4.9092 | 5.9538 | 122 | | 4.9092 | 5.9538 | 123 | | 4.9107 | 5.9538 | 124 | | 4.9061 | 5.9538 | 125 | | 4.9117 | 5.9538 | 126 | | 4.9087 | 5.9538 | 127 | | 4.9062 | 5.9538 | 128 | | 4.9105 | 5.9538 | 129 | | 4.9093 | 5.9538 | 130 | | 4.9078 | 5.9538 | 131 | | 4.9067 | 5.9538 | 132 | | 4.9104 | 5.9538 | 133 | | 4.9065 | 5.9538 | 134 | | 4.9077 | 5.9538 | 135 | | 4.9101 | 5.9538 | 136 | | 4.9063 | 5.9538 | 137 | | 4.9091 | 5.9538 | 138 | | 4.9100 | 5.9538 | 139 | | 4.9101 | 5.9538 | 140 | | 4.9057 | 5.9538 | 141 | | 4.9080 | 5.9538 | 142 | | 4.9076 | 5.9538 | 143 | | 4.9085 | 5.9538 | 144 | | 4.9071 | 5.9538 | 145 | | 4.9107 | 5.9538 | 146 | | 4.9102 | 5.9538 | 147 | | 4.9071 | 5.9538 | 148 | | 4.9093 | 5.9538 | 149 | ### Framework versions - Transformers 4.38.2 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2