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--- |
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license: mit |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: huynhdoo/distilcamembert-base-finetuned-jva-missions-report |
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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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# huynhdoo/distilcamembert-base-finetuned-jva-missions-report |
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This model is a fine-tuned version of [cmarkea/distilcamembert-base](https://huggingface.co/cmarkea/distilcamembert-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0359 |
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- Validation Loss: 0.9036 |
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- Train F1: 0.0447 |
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- Epoch: 14 |
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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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3000, '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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train F1 | Epoch | |
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|:----------:|:---------------:|:--------:|:-----:| |
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| 0.5225 | 0.4756 | 0.3575 | 0 | |
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| 0.4079 | 0.4294 | 0.2961 | 1 | |
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| 0.3439 | 0.5053 | 0.2961 | 2 | |
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| 0.2765 | 0.5106 | 0.2346 | 3 | |
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| 0.2044 | 0.5352 | 0.1788 | 4 | |
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| 0.1774 | 0.6706 | 0.1341 | 5 | |
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| 0.1690 | 0.8693 | 0.1676 | 6 | |
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| 0.1143 | 0.7711 | 0.0726 | 7 | |
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| 0.0930 | 0.9906 | 0.0950 | 8 | |
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| 0.1091 | 0.9093 | 0.1117 | 9 | |
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| 0.0576 | 0.8518 | 0.0894 | 10 | |
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| 0.0500 | 1.2538 | 0.0950 | 11 | |
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| 0.0541 | 0.7193 | 0.0838 | 12 | |
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| 0.0461 | 0.9906 | 0.0503 | 13 | |
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| 0.0359 | 0.9036 | 0.0447 | 14 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- TensorFlow 2.9.2 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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