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--- |
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license: cc-by-4.0 |
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base_model: deepset/minilm-uncased-squad2 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: badokorach/minilm-uncased-finetuned-agic2-051223 |
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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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# badokorach/minilm-uncased-finetuned-agic2-051223 |
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This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co./deepset/minilm-uncased-squad2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.6997 |
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- Validation Loss: 0.0 |
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- Epoch: 19 |
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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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 1300, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02} |
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- training_precision: mixed_float16 |
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### Training results |
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| Train Loss | Validation Loss | Epoch | |
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|:----------:|:---------------:|:-----:| |
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| 2.3883 | 0.0 | 0 | |
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| 1.9254 | 0.0 | 1 | |
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| 1.7108 | 0.0 | 2 | |
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| 1.5497 | 0.0 | 3 | |
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| 1.4126 | 0.0 | 4 | |
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| 1.2711 | 0.0 | 5 | |
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| 1.1776 | 0.0 | 6 | |
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| 1.0960 | 0.0 | 7 | |
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| 1.0484 | 0.0 | 8 | |
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| 0.9780 | 0.0 | 9 | |
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| 0.9238 | 0.0 | 10 | |
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| 0.8748 | 0.0 | 11 | |
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| 0.8300 | 0.0 | 12 | |
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| 0.8056 | 0.0 | 13 | |
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| 0.7757 | 0.0 | 14 | |
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| 0.7537 | 0.0 | 15 | |
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| 0.7272 | 0.0 | 16 | |
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| 0.7454 | 0.0 | 17 | |
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| 0.6981 | 0.0 | 18 | |
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| 0.6997 | 0.0 | 19 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.14.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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