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--- |
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license: apache-2.0 |
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base_model: distilroberta-base |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: rubakha/bert |
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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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# rubakha/bert |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co./distilroberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.3174 |
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- Train Accuracy: 0.899 |
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- Validation Loss: 0.3158 |
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- Validation Accuracy: 0.8990 |
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- Train Precision: 0.9000 |
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- Train Recall: 0.899 |
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- Train F1: 0.8974 |
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- Epoch: 2 |
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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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5000, '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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Train Precision | Train Recall | Train F1 | Epoch | |
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|:----------:|:--------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:-----:| |
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| 1.5173 | 0.691 | 0.8650 | 0.6910 | 0.6989 | 0.691 | 0.6859 | 0 | |
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| 0.5919 | 0.8635 | 0.3998 | 0.8635 | 0.8635 | 0.8635 | 0.8618 | 1 | |
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| 0.3174 | 0.899 | 0.3158 | 0.8990 | 0.9000 | 0.899 | 0.8974 | 2 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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