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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: mmiteva/qa_model_upgraded |
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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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# mmiteva/qa_model_upgraded |
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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: 0.3087 |
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- Train End Logits Accuracy: 0.8935 |
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- Train Start Logits Accuracy: 0.8884 |
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- Validation Loss: 1.1857 |
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- Validation End Logits Accuracy: 0.7448 |
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- Validation Start Logits Accuracy: 0.7335 |
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- Epoch: 4 |
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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': 5e-05, 'decay_steps': 68700, '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 | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |
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|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| |
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| 1.3498 | 0.6214 | 0.6049 | 1.0429 | 0.6891 | 0.6754 | 0 | |
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| 0.8373 | 0.7346 | 0.7236 | 0.9979 | 0.7117 | 0.6974 | 1 | |
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| 0.5988 | 0.8012 | 0.7931 | 0.9518 | 0.7267 | 0.7225 | 2 | |
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| 0.4309 | 0.8541 | 0.8465 | 1.0632 | 0.7417 | 0.7320 | 3 | |
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| 0.3087 | 0.8935 | 0.8884 | 1.1857 | 0.7448 | 0.7335 | 4 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- TensorFlow 2.10.1 |
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- Datasets 2.7.1 |
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- Tokenizers 0.12.1 |
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