readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: Ashraf-kasem/custom_gpt2_frames_text_continue
    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. -->
  
  # Ashraf-kasem/custom_gpt2_frames_text_continue
  
  This model is a fine-tuned version of [Ashraf-kasem/custom_gpt2_frames_text_continue](https://huggingface.co/Ashraf-kasem/custom_gpt2_frames_text_continue) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 0.6337
  - Validation Loss: 2.3028
  - 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': 'Adam', 'learning_rate': {'class_name': 'LinearWarmup', 'config': {'after_warmup_lr_sched': {'initial_learning_rate': 5e-05, 'decay_steps': 628900, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'warmup_steps': 125780, 'warmup_learning_rate': 0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  - training_precision: mixed_float16
  
  ### Training results
  
  | Train Loss | Validation Loss | Epoch |
  |:----------:|:---------------:|:-----:|
  | 1.0060     | 2.0768          | 0     |
  | 1.0147     | 2.0771          | 1     |
  | 1.0238     | 2.0821          | 2     |
  | 1.0331     | 2.0851          | 3     |
  | 1.0422     | 2.0870          | 4     |
  | 1.0525     | 2.0945          | 5     |
  | 1.0618     | 2.1005          | 6     |
  | 1.0718     | 2.1014          | 7     |
  | 1.0823     | 2.1056          | 8     |
  | 1.0921     | 2.1099          | 9     |
  | 1.1028     | 2.1106          | 10    |
  | 1.1127     | 2.1127          | 11    |
  | 1.1230     | 2.1183          | 12    |
  | 1.1329     | 2.1207          | 13    |
  | 1.1423     | 2.1270          | 14    |
  | 1.1521     | 2.1234          | 15    |
  | 1.1614     | 2.1283          | 16    |
  | 1.1700     | 2.1236          | 17    |
  | 1.1784     | 2.1320          | 18    |
  | 1.1864     | 2.1359          | 19    |
  | 1.1873     | 2.1272          | 20    |
  | 1.1766     | 2.1250          | 21    |
  | 1.1652     | 2.1260          | 22    |
  | 1.1537     | 2.1224          | 23    |
  | 1.1415     | 2.1278          | 24    |
  | 1.1296     | 2.1254          | 25    |
  | 1.1178     | 2.1213          | 26    |
  | 1.1059     | 2.1301          | 27    |
  | 1.0950     | 2.1253          | 28    |
  | 1.0838     | 2.1264          | 29    |
  | 1.0729     | 2.1273          | 30    |
  | 1.0625     | 2.1355          | 31    |
  | 1.0519     | 2.1345          | 32    |
  | 1.0414     | 2.1364          | 33    |
  | 1.0317     | 2.1324          | 34    |
  | 1.0217     | 2.1410          | 35    |
  | 1.0126     | 2.1428          | 36    |
  | 1.0027     | 2.1427          | 37    |
  | 0.9936     | 2.1494          | 38    |
  | 0.9846     | 2.1502          | 39    |
  | 0.9752     | 2.1490          | 40    |
  | 0.9665     | 2.1501          | 41    |
  | 0.9582     | 2.1552          | 42    |
  | 0.9497     | 2.1533          | 43    |
  | 0.9411     | 2.1621          | 44    |
  | 0.9331     | 2.1618          | 45    |
  | 0.9248     | 2.1655          | 46    |
  | 0.9172     | 2.1755          | 47    |
  | 0.9093     | 2.1759          | 48    |
  | 0.9014     | 2.1751          | 49    |
  | 0.8942     | 2.1813          | 50    |
  | 0.8867     | 2.1831          | 51    |
  | 0.8795     | 2.1856          | 52    |
  | 0.8723     | 2.1909          | 53    |
  | 0.8651     | 2.1950          | 54    |
  | 0.8581     | 2.1955          | 55    |
  | 0.8511     | 2.2007          | 56    |
  | 0.8444     | 2.2002          | 57    |
  | 0.8380     | 2.2078          | 58    |
  | 0.8312     | 2.2077          | 59    |
  | 0.8246     | 2.2161          | 60    |
  | 0.8186     | 2.2103          | 61    |
  | 0.8120     | 2.2180          | 62    |
  | 0.8053     | 2.2202          | 63    |
  | 0.7994     | 2.2232          | 64    |
  | 0.7934     | 2.2290          | 65    |
  | 0.7872     | 2.2301          | 66    |
  | 0.7816     | 2.2327          | 67    |
  | 0.7757     | 2.2369          | 68    |
  | 0.7698     | 2.2408          | 69    |
  | 0.7640     | 2.2439          | 70    |
  | 0.7582     | 2.2451          | 71    |
  | 0.7528     | 2.2505          | 72    |
  | 0.7475     | 2.2524          | 73    |
  | 0.7420     | 2.2520          | 74    |
  | 0.7366     | 2.2561          | 75    |
  | 0.7313     | 2.2616          | 76    |
  | 0.7260     | 2.2628          | 77    |
  | 0.7211     | 2.2654          | 78    |
  | 0.7158     | 2.2701          | 79    |
  | 0.7107     | 2.2704          | 80    |
  | 0.7061     | 2.2743          | 81    |
  | 0.7008     | 2.2749          | 82    |
  | 0.6962     | 2.2769          | 83    |
  | 0.6916     | 2.2813          | 84    |
  | 0.6869     | 2.2838          | 85    |
  | 0