---
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)    | [More Information Needed]  |
| Emissions (Co2eq in kg)  | [More Information Needed] |
| CPU power (W)            | [NO CPU]  |
| GPU power (W)            | [No GPU]  |
| RAM power (W)            | [More Information Needed]  |
| CPU energy (kWh)         | [No CPU]  |
| GPU energy (kWh)         | [No GPU]  |
| RAM energy (kWh)         | [More Information Needed]  |
| Consumed energy (kWh)    | [More Information Needed]  |
| Country name             | [More Information Needed]  |
| Cloud provider           | [No Cloud]  |
| Cloud region             | [No Cloud]  |
| CPU count                | [No CPU]  |
| CPU model                | [No CPU]  |
| GPU count                | [No GPU]  |
| GPU model                | [No GPU]  |

## Environmental Impact (for one core)

| Metric                   | Value                           |
|--------------------------|---------------------------------|
| CPU energy (kWh)         | [No CPU]  |
| Emissions (Co2eq in kg)  | [More Information Needed] |

## Note

20 Juin 2024

## My Config

| Config                   | Value           |
|--------------------------|-----------------|
| checkpoint               | recovering  |
| model_name               | ft_1_17e6_x4 |
| sequence_length          | 400  |
| num_epoch                | 6  |
| learning_rate            | 1.7e-05  |
| batch_size               | 1  |
| weight_decay             | 0.0  |
| warm_up_prop             | 0.0  |
| drop_out_prob            | 0.1 |
| packing_length           | 100 |
| train_test_split         | 0.2 |
| num_steps                | 1 |

## Training and Testing steps






 
Epoch | Train Loss | Test Loss | F-beta Score
---|---|---|---
| 0 | 0.000000 | 0.704927 | 0.645213 |
| 1 | 0.274418 | 0.249562 | 0.931857 |
| 2 | 0.178998 | 0.212320 | 0.917297 |
| 3 | 0.107964 | 0.280359 | 0.914211 |
| 4 | 0.052445 | 0.321978 | 0.929956 |
| 5 | 0.025102 | 0.381110 | 0.925687 |
| 6 | 0.012569 | 0.428413 | 0.920523 |