| | --- |
| | 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 |
| |
|
| | 19 juin 2024 |
| |
|
| | ## My Config |
| |
|
| | | Config | Value | |
| | |--------------------------|-----------------| |
| | | checkpoint | damgomz/fp_bs32_lr1e4_x8 | |
| | | model_name | B_x8 | |
| | | sequence_length | 400 | |
| | | num_epoch | 2 | |
| | | learning_rate | 1.8e-05 | |
| | | batch_size | 8 | |
| | | weight_decay | 0.0 | |
| | | warm_up_prop | 0.0 | |
| | | drop_out_prob | 0.1 | |
| | | packing_length | 100 | |
| | | train_test_split | 0.2 | |
| | | num_steps | 12220 | |
| |
|
| | ## Training and Testing steps |
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| | Epoch | Train Loss | Test Loss | F-beta Score | TN | FP | FN | TP |
| | ---|---|---|---|---|---|---|--- |
| | | 0 | 0.000000 | 0.695801 | 0.000000 | 761.000000 | 1.000000 | 766.000000 | 0.000000 | |
| | | 1 | 0.247787 | 0.225137 | 0.917837 | 686.000000 | 76.000000 | 60.000000 | 706.000000 | |
| | | 2 | 0.160151 | 0.223627 | 0.929874 | 657.000000 | 105.000000 | 42.000000 | 724.000000 | |
| |
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