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) | 37993.74807047844 |
| Emissions (Co2eq in kg) | 0.0229906147552034 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 0.4485365634348655 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.0395765287543337 |
| Consumed energy (kWh) | 0.4881130921891984 |
| 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) | 0.07313796503567101 |
| Emissions (Co2eq in kg) | 0.014880884660937389 |
Note
19 juin 2024
My Config
| Config | Value |
|---|---|
| checkpoint | albert-base-v2 |
| model_name | BERTrand_base_x2 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 9e-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 | 36660 |
Training and Testing steps
| Epoch | Train Loss | Test Loss | F-beta Score | TN | FP | FN | TP |
|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.762512 | 0.001627 | 753.000000 | 9.000000 | 765.000000 | 1.000000 |
| 1 | 0.285383 | 0.236045 | 0.945052 | 638.000000 | 124.000000 | 23.000000 | 743.000000 |
| 2 | 0.171418 | 0.223179 | 0.937339 | 662.000000 | 100.000000 | 36.000000 | 730.000000 |
| 3 | 0.113694 | 0.209987 | 0.938465 | 671.000000 | 91.000000 | 37.000000 | 729.000000 |
| 4 | 0.064221 | 0.242213 | 0.933953 | 674.000000 | 88.000000 | 42.000000 | 724.000000 |
| 5 | 0.041251 | 0.297383 | 0.924348 | 669.000000 | 93.000000 | 50.000000 | 716.000000 |
| 6 | 0.029554 | 0.291359 | 0.926215 | 690.000000 | 72.000000 | 53.000000 | 713.000000 |