metadata
language: en
tags:
- text-classification
pipeline_tag: text-classification
widget:
- text: >-
GEPS Techno is the pioneer of hybridization of renewable energies at sea.
We imagine, design and commercialize innovative off-grid systems that aim
to generate power at sea, stabilize and collect data. The success of our
low power platforms WAVEPEAL enabled us to scale-up the device up to
WAVEGEM, the 150-kW capacity platform.
Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|---|---|
| Duration (in seconds) | 47077.0361263752 |
| Emissions (Co2eq in kg) | 0.0284870557510473 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.5557696538744712 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.0490382217767338 |
| Consumed energy (kWh) | 0.6048078756512043 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 2 |
| CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
| GPU count | nan |
| GPU model | nan |
Environmental Impact (for one core)
| Metric | Value |
|---|---|
| CPU energy (kWh) | 0.09062329454327225 |
| Emissions (Co2eq in kg) | 0.01843850581616362 |
Note
19 juin 2024
My Config
| Config | Value |
|---|---|
| checkpoint | damgomz/fp_bs16_lr1e4_x2 |
| model_name | BERTrand_x2 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 5e-06 |
| batch_size | 2 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 36660 |
Training and Testing steps
| Epoch | Train Loss | Test Loss | F-beta Score | TN | FP | FN | TP |
|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.697500 | 0.004883 | 757.000000 | 5.000000 | 763.000000 | 3.000000 |
| 1 | 0.277993 | 0.215116 | 0.936614 | 672.000000 | 90.000000 | 39.000000 | 727.000000 |
| 2 | 0.172936 | 0.216522 | 0.935484 | 676.000000 | 86.000000 | 41.000000 | 725.000000 |
| 3 | 0.124307 | 0.222397 | 0.927996 | 693.000000 | 69.000000 | 52.000000 | 714.000000 |
| 4 | 0.062651 | 0.255473 | 0.935808 | 673.000000 | 89.000000 | 40.000000 | 726.000000 |
| 5 | 0.026753 | 0.345185 | 0.920094 | 691.000000 | 71.000000 | 59.000000 | 707.000000 |
| 6 | 0.015767 | 0.408288 | 0.921737 | 689.000000 | 73.000000 | 57.000000 | 709.000000 |