--- license: apache-2.0 base_model: climatebert/distilroberta-base-climate-f tags: - generated_from_trainer model-index: - name: SECTOR-multilabel-climatebert results: [] datasets: - GIZ/policy_classification co2_eq_emissions: emissions: 28.6797414394632 source: codecarbon training_type: fine-tuning on_cloud: true cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz ram_total_size: 12.6747894287109 hours_used: 0.706 hardware_used: 1 x Tesla T4 --- # SECTOR-multilabel-climatebert This model is a fine-tuned version of [climatebert/distilroberta-base-climate-f](https://huggingface.co/climatebert/distilroberta-base-climate-f) on the [Policy-Classification](https://huggingface.co/datasets/GIZ/policy_classification) dataset. *The loss function BCEWithLogitsLoss is modified with pos_weight to focus on recall, therefore instead of loss the evaluation metrics are used to assess the model performance during training* It achieves the following results on the evaluation set: - Loss: 0.6028 - Precision-micro: 0.6395 - Precision-samples: 0.7543 - Precision-weighted: 0.6475 - Recall-micro: 0.7762 - Recall-samples: 0.8583 - Recall-weighted: 0.7762 - F1-micro: 0.7012 - F1-samples: 0.7655 - F1-weighted: 0.7041 ## Model description The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict Sector labels - Agriculture,Buildings, Coastal Zone,Cross-Cutting Area,Disaster Risk Management (DRM),Economy-wide,Education,Energy,Environment,Health,Industries,LULUCF/Forestry,Social Development,Tourism, Transport,Urban,Waste,Water ## Intended uses & limitations More information needed ## Training and evaluation data - Training Dataset: 10123 | Class | Positive Count of Class| |:-------------|:--------| | Agriculture | 2235 | | Buildings | 169 | | Coastal Zone | 698| | Cross-Cutting Area | 1853 | | Disaster Risk Management (DRM) | 814 | | Economy-wide | 873 | | Education | 180| | Energy | 2847 | | Environment | 905 | | Health | 662| | Industries | 419 | | LULUCF/Forestry | 1861| | Social Development | 507 | | Tourism | 192 | | Transport | 1173| | Urban | 558 | | Waste | 714| | Water | 1207 | - Validation Dataset: 936 | Class | Positive Count of Class| |:-------------|:--------| | Agriculture | 200 | | Buildings | 18 | | Coastal Zone | 71| | Cross-Cutting Area | 180 | | Disaster Risk Management (DRM) | 85 | | Economy-wide | 85 | | Education | 23| | Energy | 254 | | Environment | 91 | | Health | 68| | Industries | 41 | | LULUCF/Forestry | 193| | Social Development | 56 | | Tourism | 28 | | Transport | 107| | Urban | 51 | | Waste | 59| | Water | 106 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.07e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 300 - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:| | 0.6978 | 1.0 | 633 | 0.5968 | 0.3948 | 0.5274 | 0.4982 | 0.7873 | 0.8675 | 0.7873 | 0.5259 | 0.5996 | 0.5793 | | 0.485 | 2.0 | 1266 | 0.5255 | 0.5089 | 0.6365 | 0.5469 | 0.7984 | 0.8749 | 0.7984 | 0.6216 | 0.6907 | 0.6384 | | 0.3657 | 3.0 | 1899 | 0.5248 | 0.4984 | 0.6617 | 0.5397 | 0.8141 | 0.8769 | 0.8141 | 0.6183 | 0.7066 | 0.6393 | | 0.2585 | 4.0 | 2532 | 0.5457 | 0.5807 | 0.7148 | 0.5992 | 0.8007 | 0.8752 | 0.8007 | 0.6732 | 0.7449 | 0.6813 | | 0.1841 | 5.0 | 3165 | 0.5551 | 0.6016 | 0.7426 | 0.6192 | 0.7937 | 0.8677 | 0.7937 | 0.6844 | 0.7590 | 0.6917 | | 0.1359 | 6.0 | 3798 | 0.5913 | 0.6349 | 0.7506 | 0.6449 | 0.7844 | 0.8676 | 0.7844 | 0.7018 | 0.7667 | 0.7057 | | 0.1133 | 7.0 | 4431 | 0.6028 | 0.6395 | 0.7543 | 0.6475 | 0.7762 | 0.8583 | 0.7762 | 0.7012 | 0.7655 | 0.7041 | |label | precision |recall |f1-score| support| |:-------------:|:---------:|:-----:|:------:|:------:| | Agriculture | 0.720 | 0.850|0.780|200| | Buildings | 0.636 |0.777|0.700|18| | Coastal Zone | 0.562|0.760|0.646|71| | Cross-Cutting Area | 0.569 |0.777|0.657|180| | Disaster Risk Management (DRM) | 0.567 |0.694|0.624|85| | Economy-wide | 0.461 |0.635| 0.534|85| | Education | 0.608|0.608|0.608|23| | Energy | 0.816 |0.838|0.827|254| | Environment | 0.561 |0.703|0.624|91| | Health | 0.708|0.750|0.728|68| | Industries | 0.660 |0.902|0.762|41| | LULUCF/Forestry | 0.676|0.844|0.751|193| | Social Development | 0.593 | 0.678|0.633|56| | Tourism | 0.551 |0.571|0.561|28| | Transport | 0.700|0.766|0.732|107| | Urban | 0.414 |0.568|0.479|51| | Waste | 0.658|0.881|0.753|59| | Water | 0.602 |0.773|0.677|106| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.02867 kg of CO2 - **Hours Used**: 0.706 hours ### Training Hardware - **On Cloud**: yes - **GPU Model**: 1 x Tesla T4 - **CPU Model**: Intel(R) Xeon(R) CPU @ 2.00GHz - **RAM Size**: 12.67 GB ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2