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---
license: mit
mask_token: "[MASK]"
tags:
- generated_from_keras_callback
model-index:
- name: tf-tpu/roberta-base-epochs-100
results: []
widget:
- text: Goal of my life is to [MASK].
---
<!-- 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. -->
# tf-tpu/roberta-base-epochs-100
This model is a fine-tuned version of [roberta-base](https://huggingface.co./roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0414
- Train Accuracy: 0.1136
- Validation Loss: 1.0103
- Validation Accuracy: 0.1144
- Epoch: 99
## 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': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 55765, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 2935, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 7.2121 | 0.0274 | 5.7188 | 0.0346 | 0 |
| 5.4335 | 0.0414 | 5.2266 | 0.0439 | 1 |
| 5.1579 | 0.0445 | 5.0625 | 0.0441 | 2 |
| 5.0231 | 0.0447 | 4.9453 | 0.0446 | 3 |
| 4.9323 | 0.0448 | 4.8633 | 0.0443 | 4 |
| 4.8672 | 0.0449 | 4.8789 | 0.0440 | 5 |
| 4.8200 | 0.0449 | 4.8164 | 0.0441 | 6 |
| 4.7841 | 0.0449 | 4.7734 | 0.0450 | 7 |
| 4.7546 | 0.0449 | 4.7539 | 0.0441 | 8 |
| 4.7288 | 0.0449 | 4.7305 | 0.0447 | 9 |
| 4.7084 | 0.0449 | 4.7422 | 0.0443 | 10 |
| 4.6884 | 0.0450 | 4.7148 | 0.0437 | 11 |
| 4.6764 | 0.0449 | 4.7070 | 0.0441 | 12 |
| 4.6637 | 0.0449 | 4.7227 | 0.0435 | 13 |
| 4.5963 | 0.0449 | 4.5195 | 0.0444 | 14 |
| 4.3462 | 0.0468 | 4.0742 | 0.0515 | 15 |
| 3.4139 | 0.0650 | 2.6348 | 0.0797 | 16 |
| 2.5336 | 0.0817 | 2.1816 | 0.0888 | 17 |
| 2.1859 | 0.0888 | 1.9648 | 0.0930 | 18 |
| 2.0043 | 0.0925 | 1.8154 | 0.0961 | 19 |
| 1.8887 | 0.0948 | 1.7129 | 0.0993 | 20 |
| 1.8058 | 0.0965 | 1.6729 | 0.0996 | 21 |
| 1.7402 | 0.0979 | 1.6191 | 0.1010 | 22 |
| 1.6861 | 0.0990 | 1.5693 | 0.1024 | 23 |
| 1.6327 | 0.1001 | 1.5273 | 0.1035 | 24 |
| 1.5906 | 0.1010 | 1.4766 | 0.1042 | 25 |
| 1.5545 | 0.1018 | 1.4561 | 0.1031 | 26 |
| 1.5231 | 0.1024 | 1.4365 | 0.1054 | 27 |
| 1.4957 | 0.1030 | 1.3975 | 0.1046 | 28 |
| 1.4700 | 0.1036 | 1.3789 | 0.1061 | 29 |
| 1.4466 | 0.1041 | 1.3262 | 0.1070 | 30 |
| 1.4253 | 0.1046 | 1.3223 | 0.1072 | 31 |
| 1.4059 | 0.1050 | 1.3096 | 0.1070 | 32 |
| 1.3873 | 0.1054 | 1.3164 | 0.1072 | 33 |
| 1.3703 | 0.1058 | 1.2861 | 0.1072 | 34 |
| 1.3550 | 0.1062 | 1.2705 | 0.1082 | 35 |
| 1.3398 | 0.1065 | 1.2578 | 0.1082 | 36 |
| 1.3260 | 0.1068 | 1.25 | 0.1096 | 37 |
| 1.3127 | 0.1071 | 1.2266 | 0.1102 | 38 |
| 1.2996 | 0.1074 | 1.2305 | 0.1098 | 39 |
| 1.2891 | 0.1077 | 1.2139 | 0.1088 | 40 |
| 1.2783 | 0.1079 | 1.2158 | 0.1093 | 41 |
| 1.2674 | 0.1081 | 1.1787 | 0.1114 | 42 |
| 1.2570 | 0.1084 | 1.1709 | 0.1107 | 43 |
| 1.2478 | 0.1086 | 1.1709 | 0.1104 | 44 |
| 1.2390 | 0.1088 | 1.1777 | 0.1101 | 45 |
| 1.2305 | 0.1090 | 1.1738 | 0.1111 | 46 |
| 1.2215 | 0.1092 | 1.1533 | 0.1112 | 47 |
| 1.2140 | 0.1094 | 1.1514 | 0.1117 | 48 |
| 1.2068 | 0.1096 | 1.1621 | 0.1119 | 49 |
| 1.1991 | 0.1097 | 1.1416 | 0.1108 | 50 |
| 1.1927 | 0.1099 | 1.1279 | 0.1113 | 51 |
| 1.1854 | 0.1101 | 1.1147 | 0.1123 | 52 |
| 1.1800 | 0.1102 | 1.125 | 0.1116 | 53 |
| 1.1727 | 0.1104 | 1.1167 | 0.1116 | 54 |
| 1.1679 | 0.1105 | 1.0884 | 0.1122 | 55 |
| 1.1613 | 0.1106 | 1.1084 | 0.1120 | 56 |
| 1.1563 | 0.1107 | 1.1035 | 0.1119 | 57 |
| 1.1517 | 0.1109 | 1.1035 | 0.1124 | 58 |
| 1.1454 | 0.1111 | 1.0718 | 0.1128 | 59 |
| 1.1403 | 0.1111 | 1.0874 | 0.1123 | 60 |
| 1.1360 | 0.1112 | 1.0742 | 0.1145 | 61 |
| 1.1318 | 0.1114 | 1.0811 | 0.1131 | 62 |
| 1.1277 | 0.1114 | 1.0723 | 0.1129 | 63 |
| 1.1226 | 0.1116 | 1.0640 | 0.1124 | 64 |
| 1.1186 | 0.1117 | 1.0840 | 0.1117 | 65 |
| 1.1144 | 0.1118 | 1.0522 | 0.1139 | 66 |
| 1.1111 | 0.1119 | 1.0557 | 0.1132 | 67 |
| 1.1069 | 0.1119 | 1.0718 | 0.1124 | 68 |
| 1.1038 | 0.1120 | 1.0376 | 0.1135 | 69 |
| 1.1007 | 0.1121 | 1.0537 | 0.1138 | 70 |
| 1.0975 | 0.1121 | 1.0503 | 0.1134 | 71 |
| 1.0941 | 0.1122 | 1.0317 | 0.1140 | 72 |
| 1.0902 | 0.1124 | 1.0439 | 0.1145 | 73 |
| 1.0881 | 0.1124 | 1.0352 | 0.1145 | 74 |
| 1.0839 | 0.1125 | 1.0449 | 0.1144 | 75 |
| 1.0821 | 0.1125 | 1.0229 | 0.1148 | 76 |
| 1.0791 | 0.1126 | 1.0244 | 0.1148 | 77 |
| 1.0764 | 0.1127 | 1.0366 | 0.1141 | 78 |
| 1.0741 | 0.1128 | 1.0308 | 0.1134 | 79 |
| 1.0716 | 0.1128 | 1.0400 | 0.1137 | 80 |
| 1.0688 | 0.1129 | 1.0225 | 0.1140 | 81 |
| 1.0664 | 0.1129 | 1.0269 | 0.1139 | 82 |
| 1.0643 | 0.1129 | 1.0156 | 0.1146 | 83 |
| 1.0629 | 0.1131 | 1.0127 | 0.1149 | 84 |
| 1.0602 | 0.1131 | 1.0420 | 0.1132 | 85 |
| 1.0580 | 0.1132 | 1.0205 | 0.1149 | 86 |
| 1.0568 | 0.1132 | 1.0024 | 0.1159 | 87 |
| 1.0547 | 0.1132 | 1.0210 | 0.1144 | 88 |
| 1.0536 | 0.1133 | 1.0176 | 0.1143 | 89 |
| 1.0522 | 0.1133 | 0.9951 | 0.1134 | 90 |
| 1.0505 | 0.1134 | 1.0283 | 0.1136 | 91 |
| 1.0484 | 0.1134 | 1.0063 | 0.1141 | 92 |
| 1.0482 | 0.1134 | 0.9917 | 0.1141 | 93 |
| 1.0463 | 0.1135 | 1.0244 | 0.1145 | 94 |
| 1.0458 | 0.1134 | 1.0220 | 0.1143 | 95 |
| 1.0448 | 0.1135 | 0.9785 | 0.1147 | 96 |
| 1.0435 | 0.1135 | 0.9771 | 0.1155 | 97 |
| 1.0433 | 0.1135 | 0.9946 | 0.1137 | 98 |
| 1.0414 | 0.1136 | 1.0103 | 0.1144 | 99 |
### Framework versions
- Transformers 4.27.0.dev0
- TensorFlow 2.9.1
- Tokenizers 0.13.2
|