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---
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license: apache-2.0
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base_model: albert-base-v2
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tags:
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- generated_from_keras_callback
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model-index:
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- name: Kemasu/albert-base-v2-finetuned-squad
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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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# Kemasu/albert-base-v2-finetuned-squad
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.6529
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- Train End Logits Accuracy: 0.8116
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- Train Start Logits Accuracy: 0.7711
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- Validation Loss: 0.8559
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- Validation End Logits Accuracy: 0.7604
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- Validation Start Logits Accuracy: 0.7262
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- Epoch: 1
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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': 11078, '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 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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| 0.9838 | 0.7333 | 0.6924 | 0.8735 | 0.7515 | 0.7178 | 0 |
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| 0.6529 | 0.8116 | 0.7711 | 0.8559 | 0.7604 | 0.7262 | 1 |
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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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