|
|
--- |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
model-index: |
|
|
- name: timesheet_estimator |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# timesheet_estimator |
|
|
|
|
|
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.5383 |
|
|
- Mse: 0.5383 |
|
|
- Rmse: 0.7337 |
|
|
- Mae: 0.5091 |
|
|
- R2: 0.4827 |
|
|
- Smape: 89.7730 |
|
|
|
|
|
## 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: |
|
|
- learning_rate: 2e-05 |
|
|
- train_batch_size: 32 |
|
|
- eval_batch_size: 32 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 10 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Mae | Mse | R2 | Rmse | Smape | |
|
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:--------:| |
|
|
| No log | 0.46 | 300 | 0.7945 | 0.6404 | 0.7945 | 0.2018 | 0.8914 | 120.6558 | |
|
|
| No log | 0.91 | 600 | 0.6674 | 0.5948 | 0.6674 | 0.3295 | 0.8169 | 109.9213 | |
|
|
| No log | 1.37 | 900 | 0.6795 | 0.5998 | 0.6795 | 0.3174 | 0.8243 | 108.4883 | |
|
|
| 0.7094 | 1.82 | 1200 | 0.6327 | 0.5916 | 0.6327 | 0.3643 | 0.7954 | 111.5772 | |
|
|
| 0.7094 | 2.28 | 1500 | 0.6216 | 0.5712 | 0.6216 | 0.3755 | 0.7884 | 99.9257 | |
|
|
| 0.7094 | 2.73 | 1800 | 0.5770 | 0.5397 | 0.5770 | 0.4203 | 0.7596 | 100.1235 | |
|
|
| 0.5129 | 3.19 | 2100 | 0.5791 | 0.5391 | 0.5791 | 0.4182 | 0.7610 | 99.9525 | |
|
|
| 0.5129 | 3.64 | 2400 | 0.5796 | 0.5421 | 0.5796 | 0.4177 | 0.7613 | 99.3905 | |
|
|
| 0.5129 | 4.1 | 2700 | 0.5720 | 0.5354 | 0.5720 | 0.4254 | 0.7563 | 98.9299 | |
|
|
| 0.4448 | 4.55 | 3000 | 0.5801 | 0.5381 | 0.5801 | 0.4173 | 0.7616 | 96.1430 | |
|
|
| 0.4448 | 5.01 | 3300 | 0.5437 | 0.5185 | 0.5437 | 0.4775 | 0.7373 | 94.1203 | |
|
|
| 0.4448 | 5.46 | 3600 | 0.5111 | 0.4949 | 0.5111 | 0.5088 | 0.7149 | 92.1147 | |
|
|
| 0.4448 | 5.92 | 3900 | 0.5234 | 0.5106 | 0.5234 | 0.4970 | 0.7235 | 95.4636 | |
|
|
| 0.4877 | 6.37 | 4200 | 0.5478 | 0.5249 | 0.5478 | 0.4735 | 0.7402 | 94.5022 | |
|
|
| 0.4877 | 6.83 | 4500 | 0.5172 | 0.5172 | 0.7192 | 0.4998 | 0.5029 | 93.0563 | |
|
|
| 0.4877 | 7.28 | 4800 | 0.5318 | 0.5318 | 0.7293 | 0.5083 | 0.4889 | 90.5273 | |
|
|
| 0.3889 | 7.74 | 5100 | 0.5845 | 0.5845 | 0.7645 | 0.5377 | 0.4383 | 93.8608 | |
|
|
| 0.3889 | 8.19 | 5400 | 0.5315 | 0.5315 | 0.7291 | 0.5014 | 0.4892 | 90.2302 | |
|
|
| 0.3889 | 8.65 | 5700 | 0.5356 | 0.5356 | 0.7319 | 0.5010 | 0.4852 | 88.9946 | |
|
|
| 0.324 | 9.1 | 6000 | 0.5345 | 0.5345 | 0.7311 | 0.5028 | 0.4864 | 89.7148 | |
|
|
| 0.324 | 9.56 | 6300 | 0.5383 | 0.5383 | 0.7337 | 0.5091 | 0.4827 | 89.7730 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.27.0.dev0 |
|
|
- Pytorch 1.13.1 |
|
|
- Datasets 2.9.0 |
|
|
- Tokenizers 0.13.2 |
|
|
|
