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--- |
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license: mit |
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base_model: xlnet-large-cased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: vedantjumle/xlnet-1 |
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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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# vedantjumle/xlnet-1 |
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This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co./xlnet-large-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0053 |
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- Validation Loss: 0.4856 |
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- Train Accuracy: 0.9033 |
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- Epoch: 93 |
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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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 5.1007 | 4.9565 | 0.0133 | 0 | |
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| 5.0503 | 4.8870 | 0.0367 | 1 | |
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| 4.9095 | 4.6674 | 0.07 | 2 | |
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| 4.5990 | 4.1706 | 0.2033 | 3 | |
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| 4.0403 | 3.4616 | 0.4267 | 4 | |
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| 3.2648 | 2.6274 | 0.6033 | 5 | |
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| 2.5315 | 1.8851 | 0.71 | 6 | |
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| 1.8938 | 1.4084 | 0.8033 | 7 | |
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| 1.3599 | 1.0397 | 0.84 | 8 | |
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| 0.9752 | 0.7675 | 0.8667 | 9 | |
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| 0.6995 | 0.6496 | 0.8667 | 10 | |
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| 0.5132 | 0.5293 | 0.89 | 11 | |
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| 0.3848 | 0.4618 | 0.9 | 12 | |
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| 0.2920 | 0.4516 | 0.8733 | 13 | |
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| 0.2286 | 0.4097 | 0.8967 | 14 | |
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| 0.1789 | 0.3951 | 0.9 | 15 | |
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| 0.1512 | 0.3845 | 0.8933 | 16 | |
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| 0.1320 | 0.3741 | 0.9067 | 17 | |
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| 0.1116 | 0.3553 | 0.9067 | 18 | |
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| 0.0935 | 0.3710 | 0.9 | 19 | |
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| 0.0886 | 0.3831 | 0.9067 | 20 | |
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| 0.0723 | 0.3490 | 0.91 | 21 | |
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| 0.0641 | 0.3448 | 0.91 | 22 | |
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| 0.0601 | 0.3682 | 0.9 | 23 | |
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| 0.0590 | 0.3716 | 0.9033 | 24 | |
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| 0.0491 | 0.3619 | 0.91 | 25 | |
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| 0.0404 | 0.3728 | 0.9033 | 26 | |
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| 0.0394 | 0.3624 | 0.91 | 27 | |
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| 0.0394 | 0.3249 | 0.9167 | 28 | |
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| 0.0387 | 0.3465 | 0.91 | 29 | |
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| 0.0456 | 0.3580 | 0.91 | 30 | |
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| 0.0323 | 0.3645 | 0.9133 | 31 | |
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| 0.0308 | 0.3633 | 0.9133 | 32 | |
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| 0.0312 | 0.3658 | 0.9033 | 33 | |
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| 0.0244 | 0.3621 | 0.9067 | 34 | |
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| 0.0255 | 0.3705 | 0.9067 | 35 | |
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| 0.0238 | 0.3618 | 0.9067 | 36 | |
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| 0.0222 | 0.3603 | 0.9067 | 37 | |
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| 0.0230 | 0.3678 | 0.9067 | 38 | |
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| 0.0272 | 0.4125 | 0.9033 | 39 | |
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| 0.0318 | 0.3973 | 0.91 | 40 | |
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| 0.0262 | 0.3871 | 0.9067 | 41 | |
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| 0.0299 | 0.3935 | 0.9033 | 42 | |
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| 0.0285 | 0.4192 | 0.9067 | 43 | |
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| 0.0206 | 0.4100 | 0.9133 | 44 | |
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| 0.0188 | 0.4106 | 0.9067 | 45 | |
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| 0.0179 | 0.4355 | 0.91 | 46 | |
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| 0.0151 | 0.4091 | 0.9133 | 47 | |
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| 0.0138 | 0.4046 | 0.9167 | 48 | |
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| 0.0128 | 0.4063 | 0.91 | 49 | |
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| 0.0174 | 0.4197 | 0.91 | 50 | |
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| 0.0247 | 0.4015 | 0.9133 | 51 | |
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| 0.0159 | 0.4290 | 0.91 | 52 | |
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| 0.0161 | 0.4353 | 0.9033 | 53 | |
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| 0.0163 | 0.4568 | 0.9033 | 54 | |
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| 0.0153 | 0.4428 | 0.8933 | 55 | |
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| 0.0145 | 0.4273 | 0.9033 | 56 | |
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| 0.0129 | 0.4315 | 0.8967 | 57 | |
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| 0.0107 | 0.4265 | 0.8933 | 58 | |
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| 0.0173 | 0.4303 | 0.8967 | 59 | |
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| 0.0150 | 0.4386 | 0.8933 | 60 | |
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| 0.0166 | 0.4308 | 0.91 | 61 | |
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| 0.0135 | 0.4533 | 0.8933 | 62 | |
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| 0.0096 | 0.4507 | 0.9 | 63 | |
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| 0.0091 | 0.4371 | 0.9033 | 64 | |
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| 0.0089 | 0.4383 | 0.9033 | 65 | |
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| 0.0083 | 0.4450 | 0.9033 | 66 | |
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| 0.0080 | 0.4487 | 0.9033 | 67 | |
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| 0.0082 | 0.4500 | 0.9 | 68 | |
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| 0.0077 | 0.4528 | 0.9033 | 69 | |
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| 0.0075 | 0.4516 | 0.9 | 70 | |
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| 0.0073 | 0.4474 | 0.9 | 71 | |
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| 0.0222 | 0.4517 | 0.9 | 72 | |
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| 0.0082 | 0.4778 | 0.9033 | 73 | |
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| 0.0072 | 0.4674 | 0.9 | 74 | |
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| 0.0072 | 0.4641 | 0.8967 | 75 | |
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| 0.0068 | 0.4537 | 0.9 | 76 | |
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| 0.0066 | 0.4565 | 0.8967 | 77 | |
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| 0.0063 | 0.4551 | 0.9033 | 78 | |
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| 0.0078 | 0.4614 | 0.8967 | 79 | |
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| 0.0107 | 0.4598 | 0.8967 | 80 | |
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| 0.0069 | 0.4536 | 0.9 | 81 | |
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| 0.0107 | 0.4594 | 0.9033 | 82 | |
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| 0.0072 | 0.4353 | 0.9033 | 83 | |
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| 0.0112 | 0.4995 | 0.9 | 84 | |
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| 0.0063 | 0.4875 | 0.8967 | 85 | |
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| 0.0060 | 0.4859 | 0.9033 | 86 | |
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| 0.0061 | 0.4804 | 0.9 | 87 | |
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| 0.0058 | 0.4811 | 0.9033 | 88 | |
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| 0.0058 | 0.4805 | 0.9033 | 89 | |
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| 0.0057 | 0.4811 | 0.9033 | 90 | |
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| 0.0057 | 0.4865 | 0.9033 | 91 | |
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| 0.0055 | 0.4864 | 0.9033 | 92 | |
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| 0.0053 | 0.4856 | 0.9033 | 93 | |
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### Framework versions |
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- Transformers 4.34.0 |
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- TensorFlow 2.13.0 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.1 |
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