| | --- |
| | 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) | 85447.9679479599 | |
| | | Emissions (Co2eq in kg) | 0.0517058324871173 | |
| | | CPU power (W) | 42.5 | |
| | | GPU power (W) | [No GPU] | |
| | | RAM power (W) | 3.75 | |
| | | CPU energy (kWh) | 1.008757902248039 | |
| | | GPU energy (kWh) | [No GPU] | |
| | | RAM energy (kWh) | 0.0890071661606429 | |
| | | Consumed energy (kWh) | 1.097765068408683 | |
| | | 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) | 0.1644873382998228 | |
| | | Emissions (Co2eq in kg) | 0.03346712077961763 | |
| |
|
| | ## Note |
| |
|
| | 19 juin 2024 |
| |
|
| | ## My Config |
| |
|
| | | Config | Value | |
| | |--------------------------|-----------------| |
| | | checkpoint | albert-base-v2 | |
| | | model_name | ft_16_4e6_base_x4 | |
| | | sequence_length | 400 | |
| | | num_epoch | 6 | |
| | | learning_rate | 4e-06 | |
| | | 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 | 29328 | |
| |
|
| | ## Training and Testing steps |
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| | |
| | Epoch | Train Loss | Test Loss | F-beta Score |
| | ---|---|---|--- |
| | | 0 | 0.000000 | 0.745208 | 0.662503 | |
| | | 1 | 0.344426 | 0.290544 | 0.923843 | |
| | | 2 | 0.217000 | 0.220437 | 0.915448 | |
| | | 3 | 0.171107 | 0.226694 | 0.909053 | |
| | | 4 | 0.132094 | 0.244025 | 0.908018 | |
| | | 5 | 0.091865 | 0.281725 | 0.905994 | |
| | | 6 | 0.067117 | 0.303828 | 0.908379 | |
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