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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: oc-01-distilbert-finetuned |
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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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# oc-01-distilbert-finetuned |
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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: 0.0061 |
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- Validation Loss: 0.4666 |
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- Train Recall: 0.9070 |
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- Epoch: 9 |
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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': 6140, '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 | Validation Loss | Train Recall | Epoch | |
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|:----------:|:---------------:|:------------:|:-----:| |
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| 0.3386 | 0.2557 | 0.8915 | 0 | |
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| 0.1989 | 0.2661 | 0.9283 | 1 | |
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| 0.1097 | 0.2809 | 0.9244 | 2 | |
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| 0.0716 | 0.3101 | 0.9244 | 3 | |
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| 0.0310 | 0.4023 | 0.8721 | 4 | |
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| 0.0288 | 0.4877 | 0.9535 | 5 | |
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| 0.0155 | 0.3834 | 0.9109 | 6 | |
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| 0.0105 | 0.4263 | 0.9012 | 7 | |
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| 0.0095 | 0.4746 | 0.9070 | 8 | |
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| 0.0061 | 0.4666 | 0.9070 | 9 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- TensorFlow 2.13.0 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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