deepiit98/Human_Development_Index-clustered

1---2license: mit3tags:4- generated_from_keras_callback5model-index:6- name: deepiit98/Human_Development_Index-clustered7  results: []8---9 10<!-- This model card has been generated automatically according to the information Keras had access to. You should11probably proofread and complete it, then remove this comment. -->12 13# deepiit98/Human_Development_Index-clustered14 15This model is a fine-tuned version of [nandysoham16/4-clustered_aug](https://huggingface.co/nandysoham16/4-clustered_aug) on an unknown dataset.16It achieves the following results on the evaluation set:17- Train Loss: 0.277218- Train End Logits Accuracy: 0.930619- Train Start Logits Accuracy: 0.944420- Validation Loss: 0.026221- Validation End Logits Accuracy: 1.022- Validation Start Logits Accuracy: 1.023- Epoch: 024 25## Model description26 27More information needed28 29## Intended uses & limitations30 31More information needed32 33## Training and evaluation data34 35More information needed36 37## Training procedure38 39### Training hyperparameters40 41The following hyperparameters were used during training:42- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}43- training_precision: float3244 45### Training results46 47| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |48|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|49| 0.2772     | 0.9306                    | 0.9444                      | 0.0262          | 1.0                            | 1.0                              | 0     |50 51 52### Framework versions53 54- Transformers 4.26.055- TensorFlow 2.9.256- Datasets 2.9.057- Tokenizers 0.13.258