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damgomz/ft_32_16e6_x12

damgomz

transformers

text-classification

--- 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) | 71881.81767988205 | | Emissions (Co2eq in kg) | 0.0434967905069397 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8486029742063748 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0748761558957398 | | Consumed energy (kWh) | 0.923479130102116 | | 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) | 0.13837249903377294 | | Emissions (Co2eq in kg) | 0.02815371192462047 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x12 | | model_name | ft_32_16e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.706009 | 0.371699 | | 1 | 0.303564 | 0.217143 | 0.929244 | | 2 | 0.175745 | 0.231566 | 0.905030 | | 3 | 0.120437 | 0.238707 | 0.928846 | | 4 | 0.068286 | 0.308548 | 0.921530 | | 5 | 0.041702 | 0.314831 | 0.907265 | | 6 | 0.025832 | 0.378665 | 0.924337 |

damgomz/ft_32_4e6_x2

damgomz

transformers

text-classification

--- 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) | 71495.37512636185 | | Emissions (Co2eq in kg) | 0.0432629400180004 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8440406805665959 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0744735764130948 | | Consumed energy (kWh) | 0.9185142569796908 | | 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) | 0.13762859711824657 | | Emissions (Co2eq in kg) | 0.02800235525782506 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_4e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 4e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.697838 | 0.165181 | | 1 | 0.430814 | 0.231088 | 0.928863 | | 2 | 0.197756 | 0.205576 | 0.930748 | | 3 | 0.157784 | 0.203073 | 0.936039 | | 4 | 0.122974 | 0.210342 | 0.930354 | | 5 | 0.090873 | 0.220113 | 0.930634 | | 6 | 0.060774 | 0.264006 | 0.922399 |

damgomz/ft_32_2e6_base_x1

damgomz

transformers

text-classification

--- 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) | 73095.39277100563 | | Emissions (Co2eq in kg) | 0.0442311379684663 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.862929785768026 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0761402545382579 | | Consumed energy (kWh) | 0.9390700403062856 | | 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) | 0.14070863108418583 | | Emissions (Co2eq in kg) | 0.02862902883531054 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_2e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 2e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.719601 | 0.333675 | | 1 | 0.482296 | 0.366204 | 0.876392 | | 2 | 0.298587 | 0.270772 | 0.896133 | | 3 | 0.222985 | 0.245894 | 0.908798 | | 4 | 0.177491 | 0.236516 | 0.912284 | | 5 | 0.137443 | 0.242232 | 0.893873 | | 6 | 0.105196 | 0.269823 | 0.916260 |

damgomz/ft_32_15e6_base_x2

damgomz

transformers

text-classification

--- 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) | 71832.20314526558 | | Emissions (Co2eq in kg) | 0.0434667651147476 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.848017205489013 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0748244563599428 | | Consumed energy (kWh) | 0.9228416618489558 | | 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) | 0.13827699105463623 | | Emissions (Co2eq in kg) | 0.028134279565229015 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_15e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.5e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.725213 | 0.323829 | | 1 | 0.308142 | 0.225736 | 0.909666 | | 2 | 0.182370 | 0.253100 | 0.916715 | | 3 | 0.127769 | 0.271641 | 0.909688 | | 4 | 0.074474 | 0.293281 | 0.907815 | | 5 | 0.042815 | 0.323060 | 0.914852 | | 6 | 0.030504 | 0.367846 | 0.917341 |

damgomz/ft_32_15e6_x1

damgomz

transformers

text-classification

--- 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) | 71484.84540772438 | | Emissions (Co2eq in kg) | 0.0432565779769739 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.843916534531778 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.074462650134663 | | Consumed energy (kWh) | 0.9183791846664384 | | 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) | 0.13760832740986942 | | Emissions (Co2eq in kg) | 0.02799823111802538 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_15e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.5e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.695889 | 0.254652 | | 1 | 0.275330 | 0.215623 | 0.933210 | | 2 | 0.152076 | 0.198205 | 0.933075 | | 3 | 0.098771 | 0.229899 | 0.917546 | | 4 | 0.055592 | 0.266245 | 0.915720 | | 5 | 0.028065 | 0.306953 | 0.918602 | | 6 | 0.013205 | 0.313088 | 0.925786 |

damgomz/ft_32_4e6_base_x4

damgomz

transformers

text-classification

--- 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) | 71845.55230617523 | | Emissions (Co2eq in kg) | 0.0434748378985542 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8481747363578943 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0748383185359342 | | Consumed energy (kWh) | 0.9230130548938292 | | 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) | 0.13830268818938735 | | Emissions (Co2eq in kg) | 0.0281395079865853 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_4e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 4e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.726919 | 0.469267 | | 1 | 0.380991 | 0.280183 | 0.879980 | | 2 | 0.234251 | 0.240912 | 0.927142 | | 3 | 0.188952 | 0.234519 | 0.911161 | | 4 | 0.151393 | 0.247042 | 0.904971 | | 5 | 0.120314 | 0.243643 | 0.921235 | | 6 | 0.085023 | 0.265345 | 0.908701 |

damgomz/ft_32_15e6_x2

damgomz

transformers

text-classification

--- 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) | 71970.71953129768 | | Emissions (Co2eq in kg) | 0.0435505778786255 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8496523629311062 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0749687253393237 | | Consumed energy (kWh) | 0.9246210882704298 | | 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) | 0.13854363509774806 | | Emissions (Co2eq in kg) | 0.028188531816424927 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_15e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.5e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696626 | 0.415477 | | 1 | 0.303863 | 0.197197 | 0.934375 | | 2 | 0.162620 | 0.212696 | 0.920553 | | 3 | 0.106096 | 0.230343 | 0.928876 | | 4 | 0.059013 | 0.286442 | 0.919312 | | 5 | 0.031572 | 0.315769 | 0.928705 | | 6 | 0.018381 | 0.415040 | 0.901857 |

damgomz/ft_32_1e6_x4

damgomz

transformers

text-classification

--- 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) | 73710.30487847328 | | Emissions (Co2eq in kg) | 0.0446032272363886 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8701890616261289 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0767807954127587 | | Consumed energy (kWh) | 0.9469698570388888 | | 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) | 0.14189233689106107 | | Emissions (Co2eq in kg) | 0.02886986941073537 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_1e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.712072 | 0.331035 | | 1 | 0.571019 | 0.398052 | 0.854781 | | 2 | 0.328747 | 0.295188 | 0.873769 | | 3 | 0.265386 | 0.262781 | 0.910609 | | 4 | 0.234499 | 0.247737 | 0.910782 | | 5 | 0.210701 | 0.236986 | 0.905888 | | 6 | 0.192468 | 0.230505 | 0.919231 |

damgomz/ft_32_9e6_base_x12

damgomz

transformers

text-classification

--- 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) | 72108.18771147728 | | Emissions (Co2eq in kg) | 0.0436337686919641 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.8512753254933474 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.075111984584232 | | Consumed energy (kWh) | 0.9263873100775806 | | 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) | 0.13880826134459376 | | Emissions (Co2eq in kg) | 0.028242373520328597 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_9e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 9e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.769496 | 0.568915 | | 1 | 0.394504 | 0.299390 | 0.892960 | | 2 | 0.248500 | 0.255195 | 0.907169 | | 3 | 0.201805 | 0.245713 | 0.927074 | | 4 | 0.172395 | 0.228271 | 0.920754 | | 5 | 0.145818 | 0.238505 | 0.897134 | | 6 | 0.120750 | 0.262823 | 0.918250 |

SwimChoi/villama2-7b-chat-United_Kingdom-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

Rakif215/test_model

Rakif215

transformers

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** Rakif215 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

a1-b2-c3-d4-archana/flan-t5-base-depression_dataset_reddit_cleaned_classification

a1-b2-c3-d4-archana

peft

--- base_model: google/flan-t5-base library_name: peft license: apache-2.0 metrics: - f1 tags: - generated_from_trainer model-index: - name: flan-t5-base-depression_dataset_reddit_cleaned_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-base-depression_dataset_reddit_cleaned_classification This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0360 - F1: 98.05 - Gen Len: 2.494 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

damgomz/ft_32_9e6_base_x4

damgomz

transformers

text-classification

--- 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) | 57740.51975607872 | | Emissions (Co2eq in kg) | 0.0349396951409503 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.681657758170532 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0601459220262864 | | Consumed energy (kWh) | 0.7418036801968183 | | 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) | 0.11115050053045154 | | Emissions (Co2eq in kg) | 0.022615036904464165 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_9e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 9e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.718265 | 0.417621 | | 1 | 0.329715 | 0.237797 | 0.920274 | | 2 | 0.203771 | 0.226090 | 0.916015 | | 3 | 0.154634 | 0.241886 | 0.917662 | | 4 | 0.105754 | 0.272587 | 0.909722 | | 5 | 0.069278 | 0.333512 | 0.902355 | | 6 | 0.047279 | 0.374489 | 0.887363 |

damgomz/ft_32_13e6_x4

damgomz

transformers

text-classification

--- 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) | 59404.27415037155 | | Emissions (Co2eq in kg) | 0.0359464566714897 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.7012992403136343 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0618789654885728 | | Consumed energy (kWh) | 0.7631782058022063 | | 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) | 0.11435322773946523 | | Emissions (Co2eq in kg) | 0.023266674042228857 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_13e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.3e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696288 | 0.094655 | | 1 | 0.309722 | 0.218758 | 0.913903 | | 2 | 0.173660 | 0.217908 | 0.906518 | | 3 | 0.124395 | 0.223244 | 0.920656 | | 4 | 0.073899 | 0.262751 | 0.933872 | | 5 | 0.039802 | 0.328386 | 0.907399 | | 6 | 0.023364 | 0.409061 | 0.914888 |

altamash96/message-classifer

altamash96

--- license: mit ---

damgomz/ft_32_13e6_base_x8

damgomz

transformers

text-classification

--- 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) | 60530.468687057495 | | Emissions (Co2eq in kg) | 0.0366279330673225 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.7145945333186138 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0630520785860717 | | Consumed energy (kWh) | 0.7776466119046849 | | 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) | 0.11652115222258569 | | Emissions (Co2eq in kg) | 0.023707766902430854 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_13e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.3e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.703413 | 0.599719 | | 1 | 0.343454 | 0.234083 | 0.918774 | | 2 | 0.209892 | 0.227700 | 0.915555 | | 3 | 0.160849 | 0.231055 | 0.924774 | | 4 | 0.126931 | 0.250159 | 0.911939 | | 5 | 0.087303 | 0.325753 | 0.916357 | | 6 | 0.058676 | 0.369368 | 0.909896 |

damgomz/ft_32_16e6_x1

damgomz

transformers

text-classification

--- 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) | 76273.86081504822 | | Emissions (Co2eq in kg) | 0.0461544709922368 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9004531301612628 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0794511392223338 | | Consumed energy (kWh) | 0.9799042693835982 | | 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) | 0.1468271820689678 | | Emissions (Co2eq in kg) | 0.02987392881922722 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_16e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696664 | 0.510751 | | 1 | 0.277794 | 0.199627 | 0.946928 | | 2 | 0.155206 | 0.209703 | 0.915917 | | 3 | 0.097123 | 0.240390 | 0.923577 | | 4 | 0.048558 | 0.258102 | 0.916016 | | 5 | 0.025158 | 0.292661 | 0.930326 | | 6 | 0.013297 | 0.377909 | 0.927573 |

damgomz/ft_32_16e6_x2

damgomz

transformers

text-classification

--- 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) | 76589.04898381233 | | Emissions (Co2eq in kg) | 0.0463451953789473 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9041740978658196 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0797794355824589 | | Consumed energy (kWh) | 0.9839535334482776 | | 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) | 0.14743391929383873 | | Emissions (Co2eq in kg) | 0.029997377518659826 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_16e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.697933 | 0.332367 | | 1 | 0.298595 | 0.197205 | 0.934918 | | 2 | 0.159834 | 0.219332 | 0.916928 | | 3 | 0.104084 | 0.223367 | 0.939561 | | 4 | 0.056699 | 0.282072 | 0.914538 | | 5 | 0.031568 | 0.327818 | 0.917533 | | 6 | 0.021717 | 0.372366 | 0.933519 |

damgomz/ft_32_16e6_base_x2

damgomz

transformers

text-classification

--- 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) | 76463.57305526733 | | Emissions (Co2eq in kg) | 0.0462692720003635 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9026928755258524 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0796487308171892 | | Consumed energy (kWh) | 0.9823416063430404 | | 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) | 0.1471923781313896 | | Emissions (Co2eq in kg) | 0.029948232779979704 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_16e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.737350 | 0.165081 | | 1 | 0.311884 | 0.222528 | 0.926436 | | 2 | 0.176962 | 0.214609 | 0.916218 | | 3 | 0.122318 | 0.246520 | 0.919283 | | 4 | 0.072663 | 0.304516 | 0.886896 | | 5 | 0.044193 | 0.331732 | 0.917947 | | 6 | 0.030915 | 0.362305 | 0.895415 |

damgomz/ft_32_16e6_base_x1

damgomz

transformers

text-classification

--- 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) | 76458.34525895119 | | Emissions (Co2eq in kg) | 0.0462661062558712 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9026311467150866 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0796432477960984 | | Consumed energy (kWh) | 0.9822743945111838 | | 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) | 0.14718231462348103 | | Emissions (Co2eq in kg) | 0.029946185226422548 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_16e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.704099 | 0.350817 | | 1 | 0.317000 | 0.214237 | 0.933752 | | 2 | 0.184154 | 0.213382 | 0.927406 | | 3 | 0.137625 | 0.231805 | 0.925672 | | 4 | 0.103309 | 0.263324 | 0.916410 | | 5 | 0.065206 | 0.274044 | 0.924229 | | 6 | 0.047976 | 0.337566 | 0.881619 |

SwimChoi/villama2-7b-chat-Slovenia-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

damgomz/ft_32_16e6_base_x4

damgomz

transformers

text-classification

--- 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) | 76993.88854002953 | | Emissions (Co2eq in kg) | 0.0465901744011517 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9089535264518518 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0802011496941249 | | Consumed energy (kWh) | 0.9891546761459789 | | 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) | 0.14821323543955683 | | Emissions (Co2eq in kg) | 0.03015593967817823 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_16e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.739663 | 0.407901 | | 1 | 0.339062 | 0.228771 | 0.909112 | | 2 | 0.194211 | 0.227631 | 0.942929 | | 3 | 0.142600 | 0.235747 | 0.901380 | | 4 | 0.095002 | 0.263570 | 0.920270 | | 5 | 0.064711 | 0.334313 | 0.924025 | | 6 | 0.040565 | 0.377199 | 0.918595 |

damgomz/ft_32_16e6_x4

damgomz

transformers

text-classification

--- 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) | 77144.10741114616 | | Emissions (Co2eq in kg) | 0.046681072769273 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9107269244251996 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0803576124057173 | | Consumed energy (kWh) | 0.9910845368309172 | | 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) | 0.14850240676645637 | | Emissions (Co2eq in kg) | 0.03021477540269891 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_16e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.712482 | 0.495563 | | 1 | 0.299999 | 0.232740 | 0.914570 | | 2 | 0.173250 | 0.210255 | 0.917777 | | 3 | 0.115689 | 0.240210 | 0.936128 | | 4 | 0.068289 | 0.289696 | 0.909382 | | 5 | 0.041385 | 0.308911 | 0.915930 | | 6 | 0.023509 | 0.373765 | 0.920001 |

damgomz/ft_32_18e6_base_x1

damgomz

transformers

text-classification

--- 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) | 77766.42371606827 | | Emissions (Co2eq in kg) | 0.0470576441493907 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9180736756990344 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0810058374618491 | | Consumed energy (kWh) | 0.9990795131608836 | | 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) | 0.14970036565343142 | | Emissions (Co2eq in kg) | 0.03045851595546007 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_18e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.8e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.700395 | 0.790555 | | 1 | 0.318444 | 0.230828 | 0.920868 | | 2 | 0.189804 | 0.222478 | 0.902729 | | 3 | 0.161528 | 0.230840 | 0.925825 | | 4 | 0.115337 | 0.252489 | 0.905116 | | 5 | 0.078328 | 0.268760 | 0.908056 | | 6 | 0.056382 | 0.281858 | 0.912528 |

damgomz/ft_32_16e6_base_x8

damgomz

transformers

text-classification

--- 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) | 78313.53327870369 | | Emissions (Co2eq in kg) | 0.0473887137294402 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9245326654970666 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0815757765623429 | | Consumed energy (kWh) | 1.0061084420594089 | | 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) | 0.1507535515615046 | | Emissions (Co2eq in kg) | 0.030672800534158943 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_16e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.733605 | 0.521574 | | 1 | 0.343848 | 0.250949 | 0.927429 | | 2 | 0.207227 | 0.216056 | 0.905269 | | 3 | 0.158365 | 0.231901 | 0.928852 | | 4 | 0.115495 | 0.261195 | 0.920515 | | 5 | 0.076285 | 0.315937 | 0.903032 | | 6 | 0.055799 | 0.309163 | 0.927541 |

damgomz/ft_32_18e6_x1

damgomz

transformers

text-classification

--- 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) | 78184.71831536293 | | Emissions (Co2eq in kg) | 0.0473107595238934 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9230118314592392 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0814415670650699 | | Consumed energy (kWh) | 1.0044533985243094 | | 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) | 0.15050558275707365 | | Emissions (Co2eq in kg) | 0.03062234800685048 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_18e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.8e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.698141 | 0.206651 | | 1 | 0.272480 | 0.193901 | 0.939546 | | 2 | 0.154576 | 0.201308 | 0.942076 | | 3 | 0.099094 | 0.219778 | 0.934614 | | 4 | 0.059062 | 0.277845 | 0.919980 | | 5 | 0.028669 | 0.309169 | 0.928023 | | 6 | 0.015430 | 0.360813 | 0.917627 |

damgomz/ft_32_17e6_x4

damgomz

transformers

text-classification

--- 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) | 78798.89624452591 | | Emissions (Co2eq in kg) | 0.0476824025173412 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9302624583818844 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0820812820342679 | | Consumed energy (kWh) | 1.0123437404161528 | | 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) | 0.15168787527071237 | | Emissions (Co2eq in kg) | 0.03086290102910598 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_17e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.713710 | 0.618507 | | 1 | 0.306459 | 0.211496 | 0.934590 | | 2 | 0.172426 | 0.218808 | 0.940250 | | 3 | 0.117376 | 0.263535 | 0.929094 | | 4 | 0.067785 | 0.288533 | 0.914189 | | 5 | 0.043456 | 0.303358 | 0.917680 | | 6 | 0.030474 | 0.370767 | 0.926662 |

damgomz/ft_32_17e6_base_x4

damgomz

transformers

text-classification

--- 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) | 78768.32392835617 | | Emissions (Co2eq in kg) | 0.0476639024126978 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9299015276362512 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0820494375810027 | | Consumed energy (kWh) | 1.011950965217253 | | 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) | 0.15162902356208563 | | Emissions (Co2eq in kg) | 0.0308509268719395 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_17e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.702786 | 0.053460 | | 1 | 0.315886 | 0.238229 | 0.924759 | | 2 | 0.193645 | 0.229385 | 0.923752 | | 3 | 0.136677 | 0.235313 | 0.915139 | | 4 | 0.086468 | 0.321663 | 0.884123 | | 5 | 0.060418 | 0.296657 | 0.920084 | | 6 | 0.041358 | 0.356031 | 0.923231 |

damgomz/ft_32_16e6_x8

damgomz

transformers

text-classification

--- 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) | 78555.43899655342 | | Emissions (Co2eq in kg) | 0.0475350863926364 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9273883515700714 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0818277243653932 | | Consumed energy (kWh) | 1.0092160759354667 | | 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) | 0.15121922006836533 | | Emissions (Co2eq in kg) | 0.030767546940316755 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_16e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.6e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696512 | 0.495578 | | 1 | 0.302255 | 0.226014 | 0.918930 | | 2 | 0.170988 | 0.226232 | 0.908398 | | 3 | 0.115273 | 0.246663 | 0.921976 | | 4 | 0.070741 | 0.280451 | 0.918229 | | 5 | 0.041068 | 0.323950 | 0.912251 | | 6 | 0.027156 | 0.391745 | 0.899214 |

damgomz/ft_32_17e6_x8

damgomz

transformers

text-classification

--- 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) | 79829.65154862404 | | Emissions (Co2eq in kg) | 0.0483061263122423 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9424309913574008 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0831550113228459 | | Consumed energy (kWh) | 1.0255860026802477 | | 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) | 0.15367207923110127 | | Emissions (Co2eq in kg) | 0.031266613523211084 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_17e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.725172 | 0.488614 | | 1 | 0.299050 | 0.215116 | 0.928639 | | 2 | 0.173200 | 0.209131 | 0.928052 | | 3 | 0.117315 | 0.227597 | 0.930839 | | 4 | 0.070195 | 0.272285 | 0.916066 | | 5 | 0.039318 | 0.328803 | 0.928794 | | 6 | 0.024986 | 0.369566 | 0.909873 |

damgomz/ft_32_17e6_base_x8

damgomz

transformers

text-classification

--- 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) | 79839.45226216316 | | Emissions (Co2eq in kg) | 0.0483120636537122 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9425468512781836 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0831652069310347 | | Consumed energy (kWh) | 1.0257120582092154 | | 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) | 0.1536909456046641 | | Emissions (Co2eq in kg) | 0.031270452136013906 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_17e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.712283 | 0.640911 | | 1 | 0.326548 | 0.247295 | 0.926940 | | 2 | 0.206238 | 0.237259 | 0.930069 | | 3 | 0.159045 | 0.226758 | 0.922457 | | 4 | 0.117338 | 0.288397 | 0.922248 | | 5 | 0.077770 | 0.307476 | 0.915256 | | 6 | 0.056569 | 0.378800 | 0.908784 |

damgomz/ft_32_17e6_x12

damgomz

transformers

text-classification

--- 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) | 80426.77821731567 | | Emissions (Co2eq in kg) | 0.048667475162368 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9494807264938968 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0837770634122195 | | Consumed energy (kWh) | 1.033257789906117 | | 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) | 0.15482154806833265 | | Emissions (Co2eq in kg) | 0.0315004881351153 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x12 | | model_name | ft_32_17e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.698811 | 0.596278 | | 1 | 0.295764 | 0.222173 | 0.927828 | | 2 | 0.172616 | 0.233945 | 0.924010 | | 3 | 0.119810 | 0.259121 | 0.915427 | | 4 | 0.072792 | 0.272543 | 0.914879 | | 5 | 0.040004 | 0.330694 | 0.929110 | | 6 | 0.025648 | 0.365092 | 0.922332 |

damgomz/ft_32_17e6_base_x12

damgomz

transformers

text-classification

--- 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) | 80765.59874010086 | | Emissions (Co2eq in kg) | 0.0488724939203312 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9534805622928684 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0841299750700593 | | Consumed energy (kWh) | 1.0376105373629272 | | 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) | 0.15547377757469413 | | Emissions (Co2eq in kg) | 0.031633192839872835 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_17e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.732474 | 0.231398 | | 1 | 0.375652 | 0.278516 | 0.908269 | | 2 | 0.221592 | 0.247247 | 0.896841 | | 3 | 0.184791 | 0.228275 | 0.906988 | | 4 | 0.152636 | 0.245225 | 0.922379 | | 5 | 0.113951 | 0.248734 | 0.918360 | | 6 | 0.084203 | 0.296184 | 0.914779 |

damgomz/ft_32_7e6_base_x1

damgomz

transformers

text-classification

--- 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) | 79004.84612965584 | | Emissions (Co2eq in kg) | 0.0478070245138415 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9326938064159624 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0822957800862689 | | Consumed energy (kWh) | 1.0149895865022311 | | 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) | 0.15208432879958747 | | Emissions (Co2eq in kg) | 0.0309435647341152 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_7e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.727935 | 0.566018 | | 1 | 0.358678 | 0.239306 | 0.930819 | | 2 | 0.200254 | 0.203475 | 0.928775 | | 3 | 0.146464 | 0.210787 | 0.921165 | | 4 | 0.101517 | 0.239812 | 0.920853 | | 5 | 0.065537 | 0.267798 | 0.910738 | | 6 | 0.042361 | 0.303130 | 0.932235 |

srihari5544/whisper-small-en-scratch-2

srihari5544

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-small-en-scratch-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-en-scratch-2 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5575 - Wer: 33.9105 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 6 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | No log | 0.2222 | 2 | 1.8510 | 44.5887 | | No log | 0.4444 | 4 | 1.8490 | 44.5887 | | 0.7847 | 0.6667 | 6 | 1.8453 | 44.7330 | | 0.7847 | 0.8889 | 8 | 1.8406 | 44.7330 | | 0.7387 | 1.1111 | 10 | 1.8346 | 44.8773 | | 0.7387 | 1.3333 | 12 | 1.8272 | 45.0216 | | 0.7387 | 1.5556 | 14 | 1.8185 | 45.4545 | | 0.7199 | 1.7778 | 16 | 1.8079 | 45.5988 | | 0.7199 | 2.0 | 18 | 1.7952 | 45.5988 | | 0.7153 | 2.2222 | 20 | 1.7810 | 45.1659 | | 0.7153 | 2.4444 | 22 | 1.7643 | 45.0216 | | 0.7153 | 2.6667 | 24 | 1.7443 | 34.3434 | | 0.6205 | 2.8889 | 26 | 1.7225 | 34.1991 | | 0.6205 | 3.1111 | 28 | 1.7001 | 34.1991 | | 0.4817 | 3.3333 | 30 | 1.6743 | 33.6219 | | 0.4817 | 3.5556 | 32 | 1.6421 | 33.6219 | | 0.4817 | 3.7778 | 34 | 1.6030 | 33.9105 | | 0.3973 | 4.0 | 36 | 1.5575 | 33.9105 | ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.19.3.dev0 - Tokenizers 0.19.1

damgomz/ft_32_7e6_x1

damgomz

transformers

text-classification

--- 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) | 78978.28361129761 | | Emissions (Co2eq in kg) | 0.0477909535721322 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9323802866213852 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0822680981715519 | | Consumed energy (kWh) | 1.0146483847929388 | | 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) | 0.15203319595174786 | | Emissions (Co2eq in kg) | 0.03093316108109156 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_7e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.703596 | 0.346171 | | 1 | 0.324011 | 0.236833 | 0.924990 | | 2 | 0.178597 | 0.196135 | 0.936993 | | 3 | 0.127945 | 0.200144 | 0.928396 | | 4 | 0.091947 | 0.225701 | 0.912197 | | 5 | 0.053299 | 0.244373 | 0.933727 | | 6 | 0.030095 | 0.281922 | 0.926978 |

damgomz/ft_32_7e6_x2

damgomz

transformers

text-classification

--- 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) | 79255.22461080551 | | Emissions (Co2eq in kg) | 0.047958526166105 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9356495326735894 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0825565811266503 | | Consumed energy (kWh) | 1.0182061138002385 | | 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) | 0.1525663073758006 | | Emissions (Co2eq in kg) | 0.031041629639232154 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_7e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.699140 | 0.660155 | | 1 | 0.363280 | 0.211358 | 0.923057 | | 2 | 0.177077 | 0.191385 | 0.931281 | | 3 | 0.131015 | 0.206242 | 0.931328 | | 4 | 0.087442 | 0.244335 | 0.924667 | | 5 | 0.048868 | 0.297485 | 0.904074 | | 6 | 0.029791 | 0.331672 | 0.915473 |

SwimChoi/villama2-7b-chat-Cyprus-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

damgomz/ft_32_7e6_base_x2

damgomz

transformers

text-classification

--- 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) | 79470.73570871353 | | Emissions (Co2eq in kg) | 0.0480889349404615 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.938193734708262 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.08278108409519 | | Consumed energy (kWh) | 1.0209748188034555 | | 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) | 0.15298116623927355 | | Emissions (Co2eq in kg) | 0.031126038152579465 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_7e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.707066 | 0.335475 | | 1 | 0.340416 | 0.250506 | 0.891045 | | 2 | 0.202082 | 0.236799 | 0.888755 | | 3 | 0.151717 | 0.224433 | 0.911224 | | 4 | 0.104353 | 0.256283 | 0.904543 | | 5 | 0.061494 | 0.298868 | 0.908277 | | 6 | 0.035558 | 0.332564 | 0.908750 |

damgomz/ft_32_7e6_base_x4

damgomz

transformers

text-classification

--- 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) | 79800.8502380848 | | Emissions (Co2eq in kg) | 0.0482886922008511 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.94209092052132 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.083124939032644 | | Consumed energy (kWh) | 1.0252158595539629 | | 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) | 0.15361663670831321 | | Emissions (Co2eq in kg) | 0.03125533300991654 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_7e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.718756 | 0.500131 | | 1 | 0.353712 | 0.241377 | 0.916281 | | 2 | 0.213126 | 0.211512 | 0.917583 | | 3 | 0.161509 | 0.222965 | 0.923510 | | 4 | 0.125749 | 0.234407 | 0.927739 | | 5 | 0.080237 | 0.272451 | 0.918019 | | 6 | 0.049158 | 0.324561 | 0.907007 |

damgomz/ft_32_7e6_x4

damgomz

transformers

text-classification

--- 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) | 80159.37039136887 | | Emissions (Co2eq in kg) | 0.0485056404355235 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.946323495596483 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0834983857971926 | | Consumed energy (kWh) | 1.0298218813936757 | | 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) | 0.15430678800338507 | | Emissions (Co2eq in kg) | 0.03139575340328614 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_7e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 7e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.714926 | 0.621487 | | 1 | 0.349946 | 0.245496 | 0.917076 | | 2 | 0.194886 | 0.215978 | 0.924458 | | 3 | 0.148132 | 0.220138 | 0.924500 | | 4 | 0.112454 | 0.221735 | 0.927324 | | 5 | 0.074575 | 0.272739 | 0.915914 | | 6 | 0.043918 | 0.300753 | 0.916970 |

damgomz/ft_32_17e6_x2

damgomz

transformers

text-classification

--- 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) | 79599.91120457649 | | Emissions (Co2eq in kg) | 0.0481670780880689 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9397184047894326 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0829154689726731 | | Consumed energy (kWh) | 1.0226338737621052 | | 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) | 0.15322982906880975 | | Emissions (Co2eq in kg) | 0.031176631888459122 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_17e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.7e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.705081 | 0.501642 | | 1 | 0.301390 | 0.212497 | 0.932608 | | 2 | 0.161391 | 0.201730 | 0.930413 | | 3 | 0.109724 | 0.211987 | 0.929595 | | 4 | 0.056979 | 0.286972 | 0.941885 | | 5 | 0.034072 | 0.319155 | 0.931504 | | 6 | 0.018354 | 0.374343 | 0.921303 |

damgomz/ft_32_6e6_x8

damgomz

transformers

text-classification

--- 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) | 81029.61869692802 | | Emissions (Co2eq in kg) | 0.0490322391045145 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9565971863961876 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0844048964579901 | | Consumed energy (kWh) | 1.041002082854176 | | 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) | 0.15598201599158645 | | Emissions (Co2eq in kg) | 0.031736600656296805 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_6e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696089 | 0.349678 | | 1 | 0.352716 | 0.238475 | 0.913756 | | 2 | 0.203627 | 0.220733 | 0.915962 | | 3 | 0.159582 | 0.217955 | 0.927139 | | 4 | 0.124792 | 0.237107 | 0.911855 | | 5 | 0.090320 | 0.270415 | 0.914128 | | 6 | 0.056342 | 0.299888 | 0.930272 |

damgomz/ft_32_3e6_base_x1

damgomz

transformers

text-classification

--- 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) | 79268.96590352058 | | Emissions (Co2eq in kg) | 0.047966836155913 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9358116411315084 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0825709018275143 | | Consumed energy (kWh) | 1.0183825429590248 | | 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) | 0.15259275936427713 | | Emissions (Co2eq in kg) | 0.03104701164554556 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_3e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 3e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.709158 | 0.275495 | | 1 | 0.434058 | 0.312007 | 0.896704 | | 2 | 0.249555 | 0.246421 | 0.912820 | | 3 | 0.187484 | 0.217297 | 0.907058 | | 4 | 0.146818 | 0.226701 | 0.910997 | | 5 | 0.108932 | 0.238091 | 0.906553 | | 6 | 0.075487 | 0.250010 | 0.921942 |

damgomz/ft_32_6e6_base_x2

damgomz

transformers

text-classification

--- 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) | 81544.04263401031 | | Emissions (Co2eq in kg) | 0.0493435342201205 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9626704126213974 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0849407680355013 | | Consumed energy (kWh) | 1.0476111806568973 | | 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) | 0.15697228207046984 | | Emissions (Co2eq in kg) | 0.03193808336498737 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_6e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.704487 | 0.355798 | | 1 | 0.341921 | 0.251525 | 0.905432 | | 2 | 0.204760 | 0.210480 | 0.920269 | | 3 | 0.155029 | 0.230605 | 0.899533 | | 4 | 0.109544 | 0.237654 | 0.918624 | | 5 | 0.065636 | 0.299642 | 0.914945 | | 6 | 0.036088 | 0.349576 | 0.904576 |

damgomz/ft_32_6e6_x1

damgomz

transformers

text-classification

--- 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) | 81167.52666306496 | | Emissions (Co2eq in kg) | 0.0491156926103222 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.958225337481499 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0845485443962114 | | Consumed energy (kWh) | 1.0427738818777144 | | 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) | 0.15624748882640002 | | Emissions (Co2eq in kg) | 0.03179061460970044 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_6e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.708064 | 0.369049 | | 1 | 0.341608 | 0.213487 | 0.915795 | | 2 | 0.181108 | 0.197728 | 0.919906 | | 3 | 0.134788 | 0.205607 | 0.918615 | | 4 | 0.096347 | 0.213107 | 0.937047 | | 5 | 0.059694 | 0.242867 | 0.925012 | | 6 | 0.033484 | 0.280116 | 0.923403 |

damgomz/ft_32_2e6_x4

damgomz

transformers

text-classification

--- 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) | 79966.68921732903 | | Emissions (Co2eq in kg) | 0.0483890442038945 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9440487454518672 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0832976846429208 | | Consumed energy (kWh) | 1.027346430094787 | | 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) | 0.15393587674335837 | | Emissions (Co2eq in kg) | 0.03132028661012053 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_2e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 2e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.706277 | 0.486549 | | 1 | 0.466249 | 0.305492 | 0.888577 | | 2 | 0.260374 | 0.250720 | 0.907067 | | 3 | 0.210482 | 0.230002 | 0.909887 | | 4 | 0.181776 | 0.219238 | 0.916580 | | 5 | 0.157157 | 0.215187 | 0.927132 | | 6 | 0.137032 | 0.218023 | 0.916048 |

damgomz/ft_32_6e6_base_x1

damgomz

transformers

text-classification

--- 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) | 81303.61992526054 | | Emissions (Co2eq in kg) | 0.0491980418320612 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9598319297734268 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0846903061039744 | | Consumed energy (kWh) | 1.0445222358773971 | | 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) | 0.15650946835612656 | | Emissions (Co2eq in kg) | 0.03184391780406038 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_6e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.742219 | 0.760623 | | 1 | 0.379147 | 0.253497 | 0.905509 | | 2 | 0.210643 | 0.224062 | 0.935292 | | 3 | 0.154627 | 0.227763 | 0.930814 | | 4 | 0.110174 | 0.232698 | 0.911840 | | 5 | 0.076161 | 0.253944 | 0.917156 | | 6 | 0.047911 | 0.286995 | 0.921704 |

damgomz/ft_32_6e6_x2

damgomz

transformers

text-classification

--- 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) | 81511.28000450134 | | Emissions (Co2eq in kg) | 0.0493237009703858 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9622834697269724 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0849066317652662 | | Consumed energy (kWh) | 1.0471901014922378 | | 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) | 0.15690921400866506 | | Emissions (Co2eq in kg) | 0.031925251335096355 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_6e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.700447 | 0.604789 | | 1 | 0.380918 | 0.225158 | 0.915259 | | 2 | 0.185228 | 0.197471 | 0.926696 | | 3 | 0.142736 | 0.203819 | 0.924086 | | 4 | 0.102383 | 0.220093 | 0.932317 | | 5 | 0.065159 | 0.254997 | 0.921107 | | 6 | 0.038284 | 0.294963 | 0.923176 |

damgomz/ft_32_6e6_x12

damgomz

transformers

text-classification

--- 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) | 81758.97024178505 | | Emissions (Co2eq in kg) | 0.0494735859419808 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9652076878360556 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0851646172525982 | | Consumed energy (kWh) | 1.0503723050886578 | | 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) | 0.15738601771543623 | | Emissions (Co2eq in kg) | 0.03202226334469914 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x12 | | model_name | ft_32_6e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.704507 | 0.496770 | | 1 | 0.359602 | 0.251930 | 0.894508 | | 2 | 0.208636 | 0.213533 | 0.929166 | | 3 | 0.166296 | 0.224096 | 0.906348 | | 4 | 0.128563 | 0.232381 | 0.920644 | | 5 | 0.092740 | 0.257756 | 0.925921 | | 6 | 0.060820 | 0.297630 | 0.923177 |

damgomz/ft_32_6e6_x4

damgomz

transformers

text-classification

--- 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) | 82235.88673067093 | | Emissions (Co2eq in kg) | 0.0497621632536577 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9708376775056118 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0856614043325184 | | Consumed energy (kWh) | 1.056499081838129 | | 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) | 0.15830408195654153 | | Emissions (Co2eq in kg) | 0.03220905563617944 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_6e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.700966 | 0.495226 | | 1 | 0.351622 | 0.237150 | 0.923190 | | 2 | 0.194934 | 0.203977 | 0.921933 | | 3 | 0.148990 | 0.212348 | 0.931770 | | 4 | 0.109384 | 0.231842 | 0.937159 | | 5 | 0.078790 | 0.257602 | 0.928530 | | 6 | 0.048718 | 0.304421 | 0.906031 |

damgomz/ft_32_6e6_base_x12

damgomz

transformers

text-classification

--- 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 | albert-base-v2 | | model_name | ft_32_6e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.724442 | 0.535691 | | 1 | 0.386584 | 0.296452 | 0.891890 | | 2 | 0.263757 | 0.269844 | 0.882921 | | 3 | 0.222154 | 0.248667 | 0.915979 | | 4 | 0.190100 | 0.235574 | 0.917539 |

damgomz/ft_32_1e6_base_x1

damgomz

transformers

text-classification

--- 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) | 83741.29759025574 | | Emissions (Co2eq in kg) | 0.0506731160404761 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9886099754863296 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0872295192266502 | | Consumed energy (kWh) | 1.0758394947129823 | | 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) | 0.16120199786124229 | | Emissions (Co2eq in kg) | 0.032798674889516835 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_1e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.801658 | 0.334963 | | 1 | 0.565470 | 0.464311 | 0.856138 | | 2 | 0.406829 | 0.365098 | 0.884232 | | 3 | 0.316148 | 0.301531 | 0.881326 | | 4 | 0.255616 | 0.268551 | 0.910092 | | 5 | 0.212830 | 0.246640 | 0.913674 | | 6 | 0.181439 | 0.245415 | 0.905188 |

damgomz/ft_32_6e6_base_x4

damgomz

transformers

text-classification

--- 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) | 81948.49645328522 | | Emissions (Co2eq in kg) | 0.0495882706839072 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.967445109701324 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.085362063902368 | | Consumed energy (kWh) | 1.052807173603686 | | 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) | 0.15775085567257402 | | Emissions (Co2eq in kg) | 0.03209649444420338 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_6e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.709260 | 0.345444 | | 1 | 0.352131 | 0.265789 | 0.904976 | | 2 | 0.216744 | 0.220877 | 0.917889 | | 3 | 0.166817 | 0.221011 | 0.913739 | | 4 | 0.127526 | 0.226639 | 0.931894 | | 5 | 0.084004 | 0.271546 | 0.922001 | | 6 | 0.059928 | 0.307502 | 0.899673 |

damgomz/ft_32_19e6_base_x1

damgomz

transformers

text-classification

--- 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) | 81521.76889538765 | | Emissions (Co2eq in kg) | 0.0493300581815189 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9624074856905492 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0849175855716069 | | Consumed energy (kWh) | 1.047325071262159 | | 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) | 0.15692940512362122 | | Emissions (Co2eq in kg) | 0.03192935948402683 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_19e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.697413 | 0.498850 | | 1 | 0.309828 | 0.229587 | 0.914338 | | 2 | 0.185570 | 0.260364 | 0.896540 | | 3 | 0.144719 | 0.223334 | 0.931744 | | 4 | 0.100979 | 0.234904 | 0.919179 | | 5 | 0.074212 | 0.260365 | 0.928091 | | 6 | 0.052888 | 0.309077 | 0.922794 |

damgomz/ft_32_2e6_x2

damgomz

transformers

text-classification

--- 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) | 80470.50708627701 | | Emissions (Co2eq in kg) | 0.0486939097815886 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9499965548243796 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0838224677354101 | | Consumed energy (kWh) | 1.033819022559789 | | 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) | 0.15490572614108322 | | Emissions (Co2eq in kg) | 0.031517615275458495 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_2e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 2e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.704877 | 0.786814 | | 1 | 0.542718 | 0.339129 | 0.885753 | | 2 | 0.267687 | 0.239245 | 0.910360 | | 3 | 0.206070 | 0.218004 | 0.907588 | | 4 | 0.176140 | 0.204541 | 0.932401 | | 5 | 0.152299 | 0.204936 | 0.930888 | | 6 | 0.132149 | 0.207065 | 0.925418 |

damgomz/ft_32_5e6_x1

damgomz

transformers

text-classification

--- 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) | 83936.26286959648 | | Emissions (Co2eq in kg) | 0.0507910899241483 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9909116231575594 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0874325717459121 | | Consumed energy (kWh) | 1.078344194903471 | | 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) | 0.16157730602397322 | | Emissions (Co2eq in kg) | 0.03287503629059195 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_5e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.717080 | 0.404168 | | 1 | 0.370401 | 0.234197 | 0.926642 | | 2 | 0.194178 | 0.193134 | 0.931831 | | 3 | 0.149313 | 0.206890 | 0.920385 | | 4 | 0.111487 | 0.208223 | 0.928962 | | 5 | 0.076829 | 0.224699 | 0.919291 | | 6 | 0.044347 | 0.252501 | 0.924167 |

damgomz/ft_32_19e6_x2

damgomz

transformers

text-classification

--- 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) | 81744.97626900673 | | Emissions (Co2eq in kg) | 0.049465129258145 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9650425936813166 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0851501677917937 | | Consumed energy (kWh) | 1.0501927614731128 | | 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) | 0.15735907931783794 | | Emissions (Co2eq in kg) | 0.03201678237202763 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_19e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.727473 | 0.165280 | | 1 | 0.305742 | 0.209376 | 0.944935 | | 2 | 0.163984 | 0.199954 | 0.931879 | | 3 | 0.102745 | 0.261541 | 0.917667 | | 4 | 0.060469 | 0.274702 | 0.918155 | | 5 | 0.034150 | 0.323645 | 0.931082 | | 6 | 0.026382 | 0.337212 | 0.919652 |

damgomz/ft_32_19e6_x1

damgomz

transformers

text-classification

--- 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) | 81857.15504932404 | | Emissions (Co2eq in kg) | 0.0495329941011243 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9663666981958676 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0852668998407818 | | Consumed energy (kWh) | 1.0516335980366511 | | 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) | 0.15757502346994876 | | Emissions (Co2eq in kg) | 0.03206071906098524 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_19e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.711379 | 0.730108 | | 1 | 0.276182 | 0.191061 | 0.935506 | | 2 | 0.154496 | 0.223484 | 0.916736 | | 3 | 0.095163 | 0.227535 | 0.939098 | | 4 | 0.051238 | 0.283583 | 0.934291 | | 5 | 0.023429 | 0.328065 | 0.918598 | | 6 | 0.019536 | 0.370221 | 0.923137 |

damgomz/ft_32_5e6_base_x2

damgomz

transformers

text-classification

--- 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) | 84216.49479651451 | | Emissions (Co2eq in kg) | 0.0509606668864246 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9942199889325468 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0877244896650316 | | Consumed energy (kWh) | 1.0819444785975798 | | 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) | 0.1621167524832904 | | Emissions (Co2eq in kg) | 0.03298479379530151 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_5e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.723977 | 0.220560 | | 1 | 0.358270 | 0.253965 | 0.922467 | | 2 | 0.210130 | 0.225206 | 0.917343 | | 3 | 0.156564 | 0.218143 | 0.935316 | | 4 | 0.115515 | 0.240640 | 0.923552 | | 5 | 0.070426 | 0.274872 | 0.918056 | | 6 | 0.038166 | 0.319556 | 0.915421 |

damgomz/ft_32_1e6_x2

damgomz

transformers

text-classification

--- 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) | 83842.70004177094 | | Emissions (Co2eq in kg) | 0.050734471040808 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9898069643634896 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0873351566277441 | | Consumed energy (kWh) | 1.0771421209912329 | | 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) | 0.16139719758040905 | | Emissions (Co2eq in kg) | 0.032838390849693616 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_1e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.699989 | 0.331379 | | 1 | 0.665445 | 0.571562 | 0.837237 | | 2 | 0.401150 | 0.314154 | 0.883665 | | 3 | 0.269103 | 0.247632 | 0.913446 | | 4 | 0.222599 | 0.227815 | 0.919434 | | 5 | 0.197380 | 0.213939 | 0.925075 | | 6 | 0.180440 | 0.206377 | 0.925302 |

damgomz/ft_32_19e6_base_x2

damgomz

transformers

text-classification

--- 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) | 82016.95650601387 | | Emissions (Co2eq in kg) | 0.0496296998664142 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9682533590194252 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0854333963873485 | | Consumed energy (kWh) | 1.05368675540677 | | 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) | 0.1578826412740767 | | Emissions (Co2eq in kg) | 0.03212330796485543 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_19e6_base_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.784040 | 0.495607 | | 1 | 0.318247 | 0.241446 | 0.934972 | | 2 | 0.182249 | 0.210981 | 0.923626 | | 3 | 0.126879 | 0.228292 | 0.908654 | | 4 | 0.076337 | 0.313764 | 0.928976 | | 5 | 0.054462 | 0.306271 | 0.910717 | | 6 | 0.038489 | 0.326108 | 0.918542 |

damgomz/ft_32_1e6_x1

damgomz

transformers

text-classification

--- 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) | 83898.19526410103 | | Emissions (Co2eq in kg) | 0.0507680587805448 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9904622792965836 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0873929420478643 | | Consumed energy (kWh) | 1.0778552213444477 | | 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) | 0.16150402588339446 | | Emissions (Co2eq in kg) | 0.03286012647843957 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/ThunBERT_bs16_lr5_MLM | | model_name | ft_32_1e6_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696478 | 0.232135 | | 1 | 0.552451 | 0.438652 | 0.874495 | | 2 | 0.374388 | 0.330575 | 0.893278 | | 3 | 0.284570 | 0.267519 | 0.913257 | | 4 | 0.233110 | 0.236401 | 0.920771 | | 5 | 0.202751 | 0.223335 | 0.908936 | | 6 | 0.182598 | 0.212633 | 0.923205 |

damgomz/ft_32_5e6_base_x1

damgomz

transformers

text-classification

--- 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) | 84141.62449789047 | | Emissions (Co2eq in kg) | 0.0509153560592317 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9933359973132604 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0876464883926012 | | Consumed energy (kWh) | 1.080982485705861 | | 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) | 0.16197262715843916 | | Emissions (Co2eq in kg) | 0.0329554695950071 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_5e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.730071 | 0.550302 | | 1 | 0.374210 | 0.248296 | 0.901349 | | 2 | 0.206695 | 0.234511 | 0.913291 | | 3 | 0.152858 | 0.228780 | 0.930295 | | 4 | 0.111139 | 0.244988 | 0.917061 | | 5 | 0.070761 | 0.259285 | 0.911558 | | 6 | 0.046198 | 0.291387 | 0.909853 |

damgomz/ft_32_5e6_x2

damgomz

transformers

text-classification

--- 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) | 84278.55794286728 | | Emissions (Co2eq in kg) | 0.0509982140895369 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9949525065354152 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0877891356704137 | | Consumed energy (kWh) | 1.0827416422058342 | | 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) | 0.1622362240400195 | | Emissions (Co2eq in kg) | 0.03300910186095635 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x2 | | model_name | ft_32_5e6_x2 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.717108 | 0.666354 | | 1 | 0.412750 | 0.241664 | 0.900959 | | 2 | 0.198323 | 0.198621 | 0.929248 | | 3 | 0.150829 | 0.204761 | 0.916151 | | 4 | 0.114953 | 0.221281 | 0.935168 | | 5 | 0.078120 | 0.246305 | 0.926568 | | 6 | 0.048869 | 0.282362 | 0.922933 |

damgomz/ft_32_19e6_x4

damgomz

transformers

text-classification

--- 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) | 84531.86149334908 | | Emissions (Co2eq in kg) | 0.0511514947529426 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9979429230343956 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0880530166047314 | | Consumed energy (kWh) | 1.08599593963913 | | 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) | 0.16272383337469695 | | Emissions (Co2eq in kg) | 0.03310831241822838 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_19e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.707783 | 0.637775 | | 1 | 0.311302 | 0.222628 | 0.929990 | | 2 | 0.169911 | 0.215388 | 0.939025 | | 3 | 0.110805 | 0.250348 | 0.918396 | | 4 | 0.063846 | 0.282927 | 0.928378 | | 5 | 0.041853 | 0.303873 | 0.926867 | | 6 | 0.023700 | 0.414345 | 0.931528 |

damgomz/ft_32_19e6_base_x4

damgomz

transformers

text-classification

--- 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) | 82536.04255104065 | | Emissions (Co2eq in kg) | 0.0499437995576998 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9743813026997792 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.085974094376713 | | Consumed energy (kWh) | 1.060355397076493 | | 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) | 0.15888188191075323 | | Emissions (Co2eq in kg) | 0.03232661666582425 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_19e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.716812 | 0.321399 | | 1 | 0.316622 | 0.222771 | 0.923284 | | 2 | 0.188987 | 0.254623 | 0.899390 | | 3 | 0.140403 | 0.233587 | 0.925130 | | 4 | 0.091719 | 0.278762 | 0.922702 | | 5 | 0.055096 | 0.395175 | 0.882975 | | 6 | 0.044405 | 0.355451 | 0.910667 |

buddhadilesh/my-pet-dog-srg

buddhadilesh

--- license: creativeml-openrail-m tags: - Text-to-Image - ' Diffusers' - ' Safetensors ' - ' StableDiffusionPipeline ' - 'NxtWave-GenAI-Webinar ' - 'stable-diffusion ' --- ### Dog Dreambooth model trained by buddhadilesh following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 22L61A0585 Sample pictures of this concept:

damgomz/ft_32_5e6_base_x4

damgomz

transformers

text-classification

--- 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) | 84655.95785808563 | | Emissions (Co2eq in kg) | 0.0512265887715501 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9994080464348196 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0881822121913236 | | Consumed energy (kWh) | 1.0875902586261474 | | 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) | 0.16296271887681482 | | Emissions (Co2eq in kg) | 0.0331569168277502 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_5e6_base_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.726668 | 0.334553 | | 1 | 0.362076 | 0.270883 | 0.878418 | | 2 | 0.223458 | 0.230738 | 0.894364 | | 3 | 0.178340 | 0.232899 | 0.913810 | | 4 | 0.141190 | 0.229558 | 0.913377 | | 5 | 0.103393 | 0.273028 | 0.896759 | | 6 | 0.073538 | 0.281520 | 0.908772 |

khongtrunght/khongtrunght

khongtrunght

Entry not found

damgomz/ft_32_5e6_x4

damgomz

transformers

text-classification

--- 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) | 84593.03540968895 | | Emissions (Co2eq in kg) | 0.0511885078659687 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9986650677652844 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0881166962305703 | | Consumed energy (kWh) | 1.0867817639958537 | | 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) | 0.16284159316365124 | | Emissions (Co2eq in kg) | 0.03313227220212817 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x4 | | model_name | ft_32_5e6_x4 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.726340 | 0.332935 | | 1 | 0.366748 | 0.236639 | 0.913753 | | 2 | 0.202514 | 0.216440 | 0.925653 | | 3 | 0.158706 | 0.207701 | 0.930613 | | 4 | 0.125466 | 0.231257 | 0.904056 | | 5 | 0.097189 | 0.246146 | 0.928232 | | 6 | 0.065838 | 0.261989 | 0.925164 |

damgomz/ft_32_6e6_base_x8

damgomz

transformers

text-classification

--- 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) | 83275.29592871666 | | Emissions (Co2eq in kg) | 0.0503911391020655 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9831087263792726 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0867441239225366 | | Consumed energy (kWh) | 1.06985285030181 | | 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) | 0.16030494466277959 | | Emissions (Co2eq in kg) | 0.03261615757208069 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_6e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 6e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.718706 | 0.428597 | | 1 | 0.368201 | 0.260952 | 0.920472 | | 2 | 0.229549 | 0.232955 | 0.919445 | | 3 | 0.191444 | 0.227946 | 0.900556 | | 4 | 0.155176 | 0.238332 | 0.898591 | | 5 | 0.121049 | 0.265901 | 0.924556 | | 6 | 0.089754 | 0.278236 | 0.909430 |

SwimChoi/villama2-7b-chat-Sweden-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

tapan247/Llama-2-7b-chat-finetune

tapan247

transformers

text-generation

Entry not found

damgomz/ft_32_5e6_base_x8

damgomz

transformers

text-classification

--- 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) | 85703.25652909279 | | Emissions (Co2eq in kg) | 0.051860334927213 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0117721012261205 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0892732041222352 | | Consumed energy (kWh) | 1.1010453053483589 | | 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) | 0.16497876881850362 | | Emissions (Co2eq in kg) | 0.03356710880722801 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_5e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.718306 | 0.012435 | | 1 | 0.368546 | 0.267537 | 0.921596 | | 2 | 0.228472 | 0.228982 | 0.898315 | | 3 | 0.185974 | 0.233243 | 0.933199 | | 4 | 0.158744 | 0.224832 | 0.909920 | | 5 | 0.131466 | 0.234063 | 0.926528 | | 6 | 0.098023 | 0.270619 | 0.917607 |

damgomz/ft_32_19e6_x8

damgomz

transformers

text-classification

--- 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) | 85653.45368814468 | | Emissions (Co2eq in kg) | 0.0518301931545655 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0111840223819015 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0892213438431421 | | Consumed energy (kWh) | 1.1004053662250386 | | 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) | 0.1648828983496785 | | Emissions (Co2eq in kg) | 0.03354760269452334 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_19e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.702052 | 0.497521 | | 1 | 0.298530 | 0.223112 | 0.928086 | | 2 | 0.168823 | 0.224202 | 0.920357 | | 3 | 0.112629 | 0.247916 | 0.919562 | | 4 | 0.065288 | 0.281048 | 0.936415 | | 5 | 0.037261 | 0.348494 | 0.896289 | | 6 | 0.025527 | 0.416870 | 0.930228 |

damgomz/ft_32_18e6_base_x8

damgomz

transformers

text-classification

--- 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) | 83661.49552226067 | | Emissions (Co2eq in kg) | 0.0506248307469959 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9876679645523444 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0871463864592213 | | Consumed energy (kWh) | 1.0748143510115693 | | 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) | 0.16104837888035178 | | Emissions (Co2eq in kg) | 0.03276741907955209 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_18e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.8e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.729164 | 0.515696 | | 1 | 0.327088 | 0.233053 | 0.922262 | | 2 | 0.200008 | 0.226412 | 0.926072 | | 3 | 0.154432 | 0.243826 | 0.923200 | | 4 | 0.107959 | 0.308675 | 0.907737 | | 5 | 0.078035 | 0.288907 | 0.927639 | | 6 | 0.048632 | 0.382458 | 0.909777 |

damgomz/ft_32_5e6_x12

damgomz

transformers

text-classification

--- 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_bs16_lr5_x12 | | model_name | ft_32_5e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.701135 | 0.039939 | | 1 | 0.374848 | 0.262630 | 0.917082 | | 2 | 0.223393 | 0.223830 | 0.924577 |

damgomz/ft_32_5e6_base_x12

damgomz

transformers

text-classification

--- 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) | 84307.6551721096 | | Emissions (Co2eq in kg) | 0.0510158327103399 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.99529624085625 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0878194617899756 | | Consumed energy (kWh) | 1.0831157026462264 | | 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) | 0.16229223620631097 | | Emissions (Co2eq in kg) | 0.033020498275742924 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_5e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.718784 | 0.668353 | | 1 | 0.402769 | 0.321245 | 0.851871 | | 2 | 0.282093 | 0.276767 | 0.878442 | | 3 | 0.234059 | 0.246080 | 0.916013 | | 4 | 0.203769 | 0.232428 | 0.910324 | | 5 | 0.177173 | 0.232535 | 0.919689 | | 6 | 0.155451 | 0.230514 | 0.917727 |

damgomz/ft_32_2e6_x8

damgomz

transformers

text-classification

--- 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) | 82297.30823588371 | | Emissions (Co2eq in kg) | 0.0497993389093254 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 0.9715629698327852 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0857253873003026 | | Consumed energy (kWh) | 1.0572883571330844 | | 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) | 0.15842231835407614 | | Emissions (Co2eq in kg) | 0.03223311239238779 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_2e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 2e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.696429 | 0.619710 | | 1 | 0.466246 | 0.305018 | 0.875728 | | 2 | 0.267149 | 0.252896 | 0.906483 | | 3 | 0.223972 | 0.232961 | 0.912724 | | 4 | 0.196987 | 0.223361 | 0.913181 | | 5 | 0.176798 | 0.225579 | 0.920709 | | 6 | 0.161701 | 0.231319 | 0.918238 |

damgomz/ft_32_5e6_x8

damgomz

transformers

text-classification

--- 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) | 85913.62610125542 | | Emissions (Co2eq in kg) | 0.0519876239158291 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0142554613759092 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0894923128927751 | | Consumed energy (kWh) | 1.10374777426868 | | 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) | 0.16538373024491668 | | Emissions (Co2eq in kg) | 0.03364950355632504 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x8 | | model_name | ft_32_5e6_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 5e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.704706 | 0.165325 | | 1 | 0.372469 | 0.276302 | 0.883169 | | 2 | 0.216595 | 0.220360 | 0.921653 | | 3 | 0.174007 | 0.211170 | 0.935329 | | 4 | 0.140274 | 0.217598 | 0.934812 | | 5 | 0.107311 | 0.253637 | 0.894713 | | 6 | 0.078157 | 0.265494 | 0.921922 |

damgomz/ft_32_19e6_base_x8

damgomz

transformers

text-classification

--- 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) | 85531.66389083862 | | Emissions (Co2eq in kg) | 0.0517564975194601 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0097462786599969 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.089094457618644 | | Consumed energy (kWh) | 1.0988407362786388 | | 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) | 0.16464845298986436 | | Emissions (Co2eq in kg) | 0.03349990169057846 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_19e6_base_x8 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.690710 | 0.749326 | | 1 | 0.333250 | 0.246641 | 0.907186 | | 2 | 0.202962 | 0.219654 | 0.926463 | | 3 | 0.155026 | 0.235011 | 0.911817 | | 4 | 0.110104 | 0.257781 | 0.924271 | | 5 | 0.082243 | 0.309697 | 0.904224 | | 6 | 0.052416 | 0.345349 | 0.920317 |

damgomz/ft_32_18e6_base_x12

damgomz

transformers

text-classification

--- 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 | albert-base-v2 | | model_name | ft_32_18e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.8e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.707339 | 0.780756 | | 1 | 0.370445 | 0.253259 | 0.920701 | | 2 | 0.220112 | 0.240859 | 0.891298 | | 3 | 0.184156 | 0.228855 | 0.913678 | | 4 | 0.147339 | 0.233711 | 0.923496 |

damgomz/ft_32_2e6_x12

damgomz

transformers

text-classification

--- 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_bs16_lr5_x12 | | model_name | ft_32_2e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 2e-06 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.705599 | 0.413386 | | 1 | 0.467405 | 0.326145 | 0.890617 |

prabinpanta0/celsius-to-fahrenheit

prabinpanta0

tensorflow

--- license: mit language: en metrics: mean_squared_error library_name: tensorflow tags: - temperature-conversion - celsius-to-fahrenheit - tensorflow - neural-network datasets: - prabinpanta0/celsius-to-fahrenheit --- # My Temperature Conversion Model This model is a simple neural network that converts temperatures from Celsius to Fahrenheit. ## Model Description This model was created as a practice exercise for the course "Intro to TensorFlow for Deep Learning" from Udacity, given by TensorFlow. It was trained on a dataset of temperature values in Celsius and their corresponding values in Fahrenheit. The model uses a small neural network built with TensorFlow. ## Usage To use this model, you can load it with TensorFlow and make predictions as shown below: ```python import tensorflow as tf model = tf.keras.models.load_model('celsius-to-fahrenheit') prediction = model.predict([100.0]) print(f"Prediction for 100°C in Fahrenheit: {prediction[0][0]}") ``` ## Training The model was trained using the following parameters: - Optimizer: Adam - Loss function: Mean Squared Error - Epochs: 1000 - Batch size: 10 ## Metrics The model was evaluated based on the Mean Squared Error loss during training.  ## Model Output  ## Datasets The model was trained on the [prabinpanta0/celsius-to-fahrenheit](https://huggingface.co/datasets/prabinpanta0/celsius-to-fahrenheit) dataset. ## License This model is released under the MIT license.

damgomz/ft_32_18e6_x12

damgomz

transformers

text-classification

--- 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_bs16_lr5_x12 | | model_name | ft_32_18e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.8e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.733001 | 0.833548 | | 1 | 0.291751 | 0.222184 | 0.939866 | | 2 | 0.179155 | 0.220241 | 0.923579 | | 3 | 0.114086 | 0.227378 | 0.936924 | | 4 | 0.068266 | 0.294327 | 0.933305 | | 5 | 0.036952 | 0.358650 | 0.919452 | | 6 | 0.029002 | 0.350685 | 0.930841 |

damgomz/ft_32_19e6_x12

damgomz

transformers

text-classification

--- 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) | 86260.43604326248 | | Emissions (Co2eq in kg) | 0.0521974785330882 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0183495854083031 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.089853606309245 | | Consumed energy (kWh) | 1.108203191717549 | | 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) | 0.16605133938328026 | | Emissions (Co2eq in kg) | 0.0337853374502778 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | damgomz/fp_bs16_lr5_x12 | | model_name | ft_32_19e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.697563 | 0.368138 | | 1 | 0.297268 | 0.229660 | 0.937037 | | 2 | 0.170018 | 0.217992 | 0.918936 | | 3 | 0.111955 | 0.250201 | 0.932553 | | 4 | 0.065125 | 0.296230 | 0.912781 | | 5 | 0.042337 | 0.353860 | 0.933457 | | 6 | 0.025348 | 0.390722 | 0.921170 |

damgomz/ft_32_19e6_base_x12

damgomz

transformers

text-classification

--- 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) | 86500.26889634132 | | Emissions (Co2eq in kg) | 0.0523426177021578 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.0211812222340055 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.0901034150806569 | | Consumed energy (kWh) | 1.1112846373146603 | | 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) | 0.16651301762545703 | | Emissions (Co2eq in kg) | 0.03387927198440035 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_19e6_base_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.9e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.703764 | 0.206445 | | 1 | 0.361659 | 0.262179 | 0.924176 | | 2 | 0.222014 | 0.237443 | 0.886685 | | 3 | 0.188105 | 0.230483 | 0.910450 | | 4 | 0.152888 | 0.242579 | 0.923003 | | 5 | 0.113482 | 0.288091 | 0.910115 | | 6 | 0.083856 | 0.315568 | 0.904766 |

ariaze/ARAZmixPony

ariaze

--- license: unknown ---

hngan/sdxl300

hngan

Entry not found

CreeperCatcher/AIstinct

CreeperCatcher

Entry not found

SwimChoi/villama2-7b-chat-Estonia-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

woweenie/v71-sd21-curated2-5e6-cd0.02-embeddingperturb1-3k-half

woweenie

Entry not found

woweenie/v71-sd21-curated2-5e6-cd0.02-embeddingperturb1-3k

woweenie

diffusers

text-to-image

Entry not found

SwimChoi/villama2-7b-chat-Switzerland-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

slelab/AES8

slelab

Entry not found

ardipm/predictive_credit_card

ardipm

Entry not found

SwimChoi/villama2-7b-chat-Portugal-lora

SwimChoi

peft

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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(2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.1.dev0

damgomz/ft_32_15e6_x12

damgomz

transformers

text-classification

--- 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_bs16_lr5_x12 | | model_name | ft_32_15e6_x12 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.5e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.713766 | 0.835124 | | 1 | 0.322328 | 0.217589 | 0.910550 | | 2 | 0.177534 | 0.208082 | 0.919247 |

damgomz/ft_32_11e6_base_x1

damgomz

transformers

text-classification

--- 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) | 108600.56103658676 | | Emissions (Co2eq in kg) | 0.0657157773761293 | | CPU power (W) | 42.5 | | GPU power (W) | [No GPU] | | RAM power (W) | 3.75 | | CPU energy (kWh) | 1.28208679255512 | | GPU energy (kWh) | [No GPU] | | RAM energy (kWh) | 0.1131230198932193 | | Consumed energy (kWh) | 1.395209812448341 | | 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) | 0.20905607999542952 | | Emissions (Co2eq in kg) | 0.04253521973932981 | ## Note 19 juin 2024 ## My Config | Config | Value | |--------------------------|-----------------| | checkpoint | albert-base-v2 | | model_name | ft_32_11e6_base_x1 | | sequence_length | 400 | | num_epoch | 6 | | learning_rate | 1.1e-05 | | batch_size | 32 | | 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 Epoch | Train Loss | Test Loss | F-beta Score ---|---|---|--- | 0 | 0.000000 | 0.775399 | 0.278800 | | 1 | 0.342542 | 0.227123 | 0.912421 | | 2 | 0.195072 | 0.210132 | 0.930485 | | 3 | 0.144655 | 0.222816 | 0.926510 | | 4 | 0.102367 | 0.240379 | 0.915922 | | 5 | 0.068007 | 0.254159 | 0.908042 | | 6 | 0.046899 | 0.296062 | 0.932938 |