File size: 7,172 Bytes
67c3304 757907c 67c3304 d1ace90 8484895 112bb87 2c02160 531bc0b ae3a454 5225086 633cfd6 f26e5b3 757907c 67c3304 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/xlnet-1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vedantjumle/xlnet-1
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co./xlnet-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0053
- Validation Loss: 0.4856
- Train Accuracy: 0.9033
- Epoch: 93
## 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': '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}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 5.1007 | 4.9565 | 0.0133 | 0 |
| 5.0503 | 4.8870 | 0.0367 | 1 |
| 4.9095 | 4.6674 | 0.07 | 2 |
| 4.5990 | 4.1706 | 0.2033 | 3 |
| 4.0403 | 3.4616 | 0.4267 | 4 |
| 3.2648 | 2.6274 | 0.6033 | 5 |
| 2.5315 | 1.8851 | 0.71 | 6 |
| 1.8938 | 1.4084 | 0.8033 | 7 |
| 1.3599 | 1.0397 | 0.84 | 8 |
| 0.9752 | 0.7675 | 0.8667 | 9 |
| 0.6995 | 0.6496 | 0.8667 | 10 |
| 0.5132 | 0.5293 | 0.89 | 11 |
| 0.3848 | 0.4618 | 0.9 | 12 |
| 0.2920 | 0.4516 | 0.8733 | 13 |
| 0.2286 | 0.4097 | 0.8967 | 14 |
| 0.1789 | 0.3951 | 0.9 | 15 |
| 0.1512 | 0.3845 | 0.8933 | 16 |
| 0.1320 | 0.3741 | 0.9067 | 17 |
| 0.1116 | 0.3553 | 0.9067 | 18 |
| 0.0935 | 0.3710 | 0.9 | 19 |
| 0.0886 | 0.3831 | 0.9067 | 20 |
| 0.0723 | 0.3490 | 0.91 | 21 |
| 0.0641 | 0.3448 | 0.91 | 22 |
| 0.0601 | 0.3682 | 0.9 | 23 |
| 0.0590 | 0.3716 | 0.9033 | 24 |
| 0.0491 | 0.3619 | 0.91 | 25 |
| 0.0404 | 0.3728 | 0.9033 | 26 |
| 0.0394 | 0.3624 | 0.91 | 27 |
| 0.0394 | 0.3249 | 0.9167 | 28 |
| 0.0387 | 0.3465 | 0.91 | 29 |
| 0.0456 | 0.3580 | 0.91 | 30 |
| 0.0323 | 0.3645 | 0.9133 | 31 |
| 0.0308 | 0.3633 | 0.9133 | 32 |
| 0.0312 | 0.3658 | 0.9033 | 33 |
| 0.0244 | 0.3621 | 0.9067 | 34 |
| 0.0255 | 0.3705 | 0.9067 | 35 |
| 0.0238 | 0.3618 | 0.9067 | 36 |
| 0.0222 | 0.3603 | 0.9067 | 37 |
| 0.0230 | 0.3678 | 0.9067 | 38 |
| 0.0272 | 0.4125 | 0.9033 | 39 |
| 0.0318 | 0.3973 | 0.91 | 40 |
| 0.0262 | 0.3871 | 0.9067 | 41 |
| 0.0299 | 0.3935 | 0.9033 | 42 |
| 0.0285 | 0.4192 | 0.9067 | 43 |
| 0.0206 | 0.4100 | 0.9133 | 44 |
| 0.0188 | 0.4106 | 0.9067 | 45 |
| 0.0179 | 0.4355 | 0.91 | 46 |
| 0.0151 | 0.4091 | 0.9133 | 47 |
| 0.0138 | 0.4046 | 0.9167 | 48 |
| 0.0128 | 0.4063 | 0.91 | 49 |
| 0.0174 | 0.4197 | 0.91 | 50 |
| 0.0247 | 0.4015 | 0.9133 | 51 |
| 0.0159 | 0.4290 | 0.91 | 52 |
| 0.0161 | 0.4353 | 0.9033 | 53 |
| 0.0163 | 0.4568 | 0.9033 | 54 |
| 0.0153 | 0.4428 | 0.8933 | 55 |
| 0.0145 | 0.4273 | 0.9033 | 56 |
| 0.0129 | 0.4315 | 0.8967 | 57 |
| 0.0107 | 0.4265 | 0.8933 | 58 |
| 0.0173 | 0.4303 | 0.8967 | 59 |
| 0.0150 | 0.4386 | 0.8933 | 60 |
| 0.0166 | 0.4308 | 0.91 | 61 |
| 0.0135 | 0.4533 | 0.8933 | 62 |
| 0.0096 | 0.4507 | 0.9 | 63 |
| 0.0091 | 0.4371 | 0.9033 | 64 |
| 0.0089 | 0.4383 | 0.9033 | 65 |
| 0.0083 | 0.4450 | 0.9033 | 66 |
| 0.0080 | 0.4487 | 0.9033 | 67 |
| 0.0082 | 0.4500 | 0.9 | 68 |
| 0.0077 | 0.4528 | 0.9033 | 69 |
| 0.0075 | 0.4516 | 0.9 | 70 |
| 0.0073 | 0.4474 | 0.9 | 71 |
| 0.0222 | 0.4517 | 0.9 | 72 |
| 0.0082 | 0.4778 | 0.9033 | 73 |
| 0.0072 | 0.4674 | 0.9 | 74 |
| 0.0072 | 0.4641 | 0.8967 | 75 |
| 0.0068 | 0.4537 | 0.9 | 76 |
| 0.0066 | 0.4565 | 0.8967 | 77 |
| 0.0063 | 0.4551 | 0.9033 | 78 |
| 0.0078 | 0.4614 | 0.8967 | 79 |
| 0.0107 | 0.4598 | 0.8967 | 80 |
| 0.0069 | 0.4536 | 0.9 | 81 |
| 0.0107 | 0.4594 | 0.9033 | 82 |
| 0.0072 | 0.4353 | 0.9033 | 83 |
| 0.0112 | 0.4995 | 0.9 | 84 |
| 0.0063 | 0.4875 | 0.8967 | 85 |
| 0.0060 | 0.4859 | 0.9033 | 86 |
| 0.0061 | 0.4804 | 0.9 | 87 |
| 0.0058 | 0.4811 | 0.9033 | 88 |
| 0.0058 | 0.4805 | 0.9033 | 89 |
| 0.0057 | 0.4811 | 0.9033 | 90 |
| 0.0057 | 0.4865 | 0.9033 | 91 |
| 0.0055 | 0.4864 | 0.9033 | 92 |
| 0.0053 | 0.4856 | 0.9033 | 93 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|