---
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)    | 37473.51775741577  |
| Emissions (Co2eq in kg)  | 0.0226758141771607 |
| CPU power (W)            | 42.5  |
| GPU power (W)            | [No GPU]  |
| RAM power (W)            | 3.75  |
| CPU energy (kWh)         | 0.4423949415524804  |
| GPU energy (kWh)         | [No GPU]  |
| RAM energy (kWh)         | 0.0390346284600596  |
| Consumed energy (kWh)    | 0.4814295700125413  |
| 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)         | 0.07213652168302537  |
| Emissions (Co2eq in kg)  | 0.014677127788321177 |

## Note

19 juin 2024

## My Config

| Config                   | Value           |
|--------------------------|-----------------|
| checkpoint               | albert-base-v2  |
| model_name               | BERTrand_base_x8 |
| sequence_length          | 400  |
| num_epoch                | 6  |
| learning_rate            | 1e-05  |
| batch_size               | 16  |
| 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 | 0.734902 | 0.000000 | 758.000000 | 4.000000 | 766.000000 | 0.000000 |
| 1 | 0.307200 | 0.232703 | 0.892013 | 699.000000 | 63.000000 | 87.000000 | 679.000000 |
| 2 | 0.200334 | 0.331862 | 0.945844 | 607.000000 | 155.000000 | 15.000000 | 751.000000 |
| 3 | 0.162341 | 0.221791 | 0.940379 | 653.000000 | 109.000000 | 31.000000 | 735.000000 |
| 4 | 0.119471 | 0.254642 | 0.910042 | 698.000000 | 64.000000 | 70.000000 | 696.000000 |
| 5 | 0.093992 | 0.267835 | 0.928884 | 666.000000 | 96.000000 | 45.000000 | 721.000000 |
| 6 | 0.058252 | 0.305894 | 0.927024 | 667.000000 | 95.000000 | 47.000000 | 719.000000 |