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