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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: Regression_albert_aug_CustomLoss_3 |
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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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# Regression_albert_aug_CustomLoss_3 |
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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: 0.2368 |
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- Train Mae: 0.5301 |
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- Train Mse: 0.4296 |
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- Train R2-score: 0.7669 |
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- Validation Loss: 0.2410 |
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- Validation Mae: 0.5680 |
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- Validation Mse: 0.4286 |
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- Validation R2-score: 0.6930 |
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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', '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': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch | |
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|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:| |
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| 0.2614 | 0.5480 | 0.4524 | 0.7369 | 0.2408 | 0.5194 | 0.4609 | 0.7578 | 0 | |
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| 0.2442 | 0.5374 | 0.4362 | 0.7109 | 0.2334 | 0.5376 | 0.4391 | 0.7399 | 1 | |
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| 0.2431 | 0.5349 | 0.4356 | 0.7503 | 0.2432 | 0.5234 | 0.4657 | 0.7591 | 2 | |
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| 0.2386 | 0.5250 | 0.4264 | 0.7926 | 0.2348 | 0.5525 | 0.4316 | 0.7203 | 3 | |
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| 0.2409 | 0.5342 | 0.4325 | 0.7166 | 0.2431 | 0.5233 | 0.4656 | 0.7591 | 4 | |
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| 0.2400 | 0.5298 | 0.4310 | 0.7553 | 0.2358 | 0.5250 | 0.4490 | 0.7513 | 5 | |
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| 0.2384 | 0.5274 | 0.4299 | 0.7791 | 0.2341 | 0.5491 | 0.4329 | 0.7253 | 6 | |
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| 0.2413 | 0.5306 | 0.4335 | 0.7593 | 0.2365 | 0.5583 | 0.4299 | 0.7109 | 7 | |
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| 0.2381 | 0.5299 | 0.4298 | 0.7784 | 0.2335 | 0.5452 | 0.4347 | 0.7306 | 8 | |
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| 0.2379 | 0.5280 | 0.4297 | 0.7575 | 0.2335 | 0.5448 | 0.4349 | 0.7312 | 9 | |
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| 0.2374 | 0.5306 | 0.4309 | 0.8098 | 0.2334 | 0.5441 | 0.4352 | 0.7321 | 10 | |
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| 0.2381 | 0.5302 | 0.4303 | 0.7428 | 0.2337 | 0.5466 | 0.4340 | 0.7288 | 11 | |
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| 0.2376 | 0.5323 | 0.4275 | 0.7806 | 0.2333 | 0.5411 | 0.4369 | 0.7358 | 12 | |
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| 0.2339 | 0.5277 | 0.4217 | 0.7986 | 0.2363 | 0.5232 | 0.4506 | 0.7525 | 13 | |
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| 0.2368 | 0.5301 | 0.4296 | 0.7669 | 0.2410 | 0.5680 | 0.4286 | 0.6930 | 14 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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