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SECTOR-multilabel-bge_f

Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Carbon Emissions
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@@ -8,6 +8,16 @@ model-index:
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  results: []
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  datasets:
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  - GIZ/policy_classification
 
 
 
 
 
 
 
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -114,29 +124,29 @@ The following hyperparameters were used during training:
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  |label | precision |recall |f1-score| support|
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  |:-------------:|:---------:|:-----:|:------:|:------:|
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- | Agriculture | 0.720 | 0.850|0.780|200|
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- | Buildings | 0.636 |0.777|0.700|18|
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- | Coastal Zone | 0.562|0.760|0.646|71|
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- | Cross-Cutting Area | 0.569 |0.777|0.657|180|
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- | Disaster Risk Management (DRM) | 0.567 |0.694|0.624|85|
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- | Economy-wide | 0.461 |0.635| 0.534|85|
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- | Education | 0.608|0.608|0.608|23|
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- | Energy | 0.816 |0.838|0.827|254|
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- | Environment | 0.561 |0.703|0.624|91|
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- | Health | 0.708|0.750|0.728|68|
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- | Industries | 0.660 |0.902|0.762|41|
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- | LULUCF/Forestry | 0.676|0.844|0.751|193|
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- | Social Development | 0.593 | 0.678|0.633|56|
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- | Tourism | 0.551 |0.571|0.561|28|
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- | Transport | 0.700|0.766|0.732|107|
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  | Urban | 0.414 |0.568|0.479|51|
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- | Waste | 0.658|0.881|0.753|59|
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- | Water | 0.602 |0.773|0.677|106|
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  ### Environmental Impact
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  Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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- - **Carbon Emitted**: 0.02867 kg of CO2
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- - **Hours Used**: 0.706 hours
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  ### Training Hardware
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  - **On Cloud**: yes
 
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  results: []
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  datasets:
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  - GIZ/policy_classification
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+
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+ co2_eq_emissions:
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+ emissions: 58.1932553246115
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+ source: codecarbon
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+ training_type: fine-tuning
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+ on_cloud: true
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+ cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
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+ ram_total_size: 12.6747817993164
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+ hours_used: 1.43
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+ hardware_used: 1 x Tesla T4
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  |label | precision |recall |f1-score| support|
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  |:-------------:|:---------:|:-----:|:------:|:------:|
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+ | Agriculture | 0.740 | 0.840|0.786|200|
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+ | Buildings | 0.535 |0.833|0.652|18|
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+ | Coastal Zone | 0.579|0.718|0.641|71|
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+ | Cross-Cutting Area | 0.551 |0.738|0.631|180|
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+ | Disaster Risk Management (DRM) | 0.642 |0.717|0.67|85|
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+ | Economy-wide | 0.401 |0.600| 0.481|85|
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+ | Education | 0.652|0.652|0.652|23|
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+ | Energy | 0.771 |0.862|0.814|254|
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+ | Environment | 0.539 |0.747|0.626|91|
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+ | Health | 0.743|0.808|0.774|68|
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+ | Industries | 0.648|0.853|0.736|41|
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+ | LULUCF/Forestry | 0.728|0.849|0.784|193|
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+ | Social Development | 0.661 | 0.767|0.710|56|
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+ | Tourism | 0.586 |0.607|0.596|28|
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+ | Transport | 0.715|0.822|0.765|107|
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  | Urban | 0.414 |0.568|0.479|51|
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+ | Waste | 0.662|0.898|0.762|59|
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+ | Water | 0.601 |.783|0.680|106|
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  ### Environmental Impact
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  Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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+ - **Carbon Emitted**: 0.05819 kg of CO2
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+ - **Hours Used**: 1.43 hours
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  ### Training Hardware
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  - **On Cloud**: yes