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---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Regression_albert_aug_CustomLoss_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Regression_albert_aug_CustomLoss_3
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2368
- Train Mae: 0.5301
- Train Mse: 0.4296
- Train R2-score: 0.7669
- Validation Loss: 0.2410
- Validation Mae: 0.5680
- Validation Mse: 0.4286
- Validation R2-score: 0.6930
- Epoch: 14
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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}
- training_precision: float32
### Training results
| Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
|:----------:|:---------:|:---------:|:--------------:|:---------------:|:--------------:|:--------------:|:-------------------:|:-----:|
| 0.2614 | 0.5480 | 0.4524 | 0.7369 | 0.2408 | 0.5194 | 0.4609 | 0.7578 | 0 |
| 0.2442 | 0.5374 | 0.4362 | 0.7109 | 0.2334 | 0.5376 | 0.4391 | 0.7399 | 1 |
| 0.2431 | 0.5349 | 0.4356 | 0.7503 | 0.2432 | 0.5234 | 0.4657 | 0.7591 | 2 |
| 0.2386 | 0.5250 | 0.4264 | 0.7926 | 0.2348 | 0.5525 | 0.4316 | 0.7203 | 3 |
| 0.2409 | 0.5342 | 0.4325 | 0.7166 | 0.2431 | 0.5233 | 0.4656 | 0.7591 | 4 |
| 0.2400 | 0.5298 | 0.4310 | 0.7553 | 0.2358 | 0.5250 | 0.4490 | 0.7513 | 5 |
| 0.2384 | 0.5274 | 0.4299 | 0.7791 | 0.2341 | 0.5491 | 0.4329 | 0.7253 | 6 |
| 0.2413 | 0.5306 | 0.4335 | 0.7593 | 0.2365 | 0.5583 | 0.4299 | 0.7109 | 7 |
| 0.2381 | 0.5299 | 0.4298 | 0.7784 | 0.2335 | 0.5452 | 0.4347 | 0.7306 | 8 |
| 0.2379 | 0.5280 | 0.4297 | 0.7575 | 0.2335 | 0.5448 | 0.4349 | 0.7312 | 9 |
| 0.2374 | 0.5306 | 0.4309 | 0.8098 | 0.2334 | 0.5441 | 0.4352 | 0.7321 | 10 |
| 0.2381 | 0.5302 | 0.4303 | 0.7428 | 0.2337 | 0.5466 | 0.4340 | 0.7288 | 11 |
| 0.2376 | 0.5323 | 0.4275 | 0.7806 | 0.2333 | 0.5411 | 0.4369 | 0.7358 | 12 |
| 0.2339 | 0.5277 | 0.4217 | 0.7986 | 0.2363 | 0.5232 | 0.4506 | 0.7525 | 13 |
| 0.2368 | 0.5301 | 0.4296 | 0.7669 | 0.2410 | 0.5680 | 0.4286 | 0.6930 | 14 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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