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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: ratish/DBERT_CleanDesc_COLLISION_v10.2 |
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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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# ratish/DBERT_CleanDesc_COLLISION_v10.2 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.5332 |
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- Validation Loss: 1.5183 |
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- Train Accuracy: 0.5641 |
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- Epoch: 8 |
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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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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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| 1.6309 | 1.7112 | 0.3077 | 0 | |
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| 1.4582 | 1.6871 | 0.3077 | 1 | |
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| 1.3074 | 1.5190 | 0.5128 | 2 | |
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| 1.1524 | 1.4848 | 0.5385 | 3 | |
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| 0.9636 | 1.4063 | 0.5128 | 4 | |
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| 0.8722 | 1.4418 | 0.5897 | 5 | |
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| 0.7233 | 1.4191 | 0.5897 | 6 | |
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| 0.6482 | 1.4759 | 0.5897 | 7 | |
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| 0.5332 | 1.5183 | 0.5641 | 8 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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