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--- |
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license: mit |
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base_model: badokorach/mobilebert-uncased-finetuned-agic-060124 |
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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-squad2-agric-17024 |
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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-squad2-agric-17024 |
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This model is a fine-tuned version of [badokorach/mobilebert-uncased-finetuned-agic-060124](https://huggingface.co/badokorach/mobilebert-uncased-finetuned-agic-060124) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0258 |
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- Validation Loss: 0.0 |
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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: {'inner_optimizer': {'module': 'keras.optimizers', 'class_name': 'Adam', 'config': {'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': None, 'class_name': 'CustomLearningRateScheduler', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2736, 'warmup_steps': 304, 'end_learning_rate': 1e-05}, 'registered_name': 'CustomLearningRateScheduler'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}, 'registered_name': None}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} |
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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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| 4.9158 | 0.0 | 0 | |
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| 0.2937 | 0.0 | 1 | |
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| 0.0747 | 0.0 | 2 | |
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| 0.0398 | 0.0 | 3 | |
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| 0.0258 | 0.0 | 4 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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