--- license: mit base_model: BAAI/bge-base-en-v1.5 tags: - generated_from_trainer model-index: - name: SECTOR-multilabel-bge results: [] datasets: - GIZ/policy_classification co2_eq_emissions: emissions: 58.1932553246115 source: codecarbon training_type: fine-tuning on_cloud: true cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz ram_total_size: 12.6747817993164 hours_used: 1.43 hardware_used: 1 x Tesla T4 --- <!-- 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. --> # SECTOR-multilabel-bge This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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: 0.6114 - Precision-micro: 0.6428 - Precision-samples: 0.7488 - Precision-weighted: 0.6519 - Recall-micro: 0.7855 - Recall-samples: 0.8627 - Recall-weighted: 0.7855 - F1-micro: 0.7071 - F1-samples: 0.7638 - F1-weighted: 0.7109 ## 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: 7.04e-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 | |:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:| | 0.7077 | 1.0 | 633 | 0.5490 | 0.4226 | 0.5465 | 0.4954 | 0.8211 | 0.8908 | 0.8211 | 0.5580 | 0.6243 | 0.5977 | | 0.4546 | 2.0 | 1266 | 0.5009 | 0.4899 | 0.6127 | 0.5202 | 0.8438 | 0.9023 | 0.8438 | 0.6199 | 0.6822 | 0.6366 | | 0.3105 | 3.0 | 1899 | 0.4947 | 0.5005 | 0.6593 | 0.5317 | 0.8508 | 0.8970 | 0.8508 | 0.6303 | 0.7125 | 0.6474 | | 0.2044 | 4.0 | 2532 | 0.5430 | 0.5757 | 0.7044 | 0.5970 | 0.8106 | 0.8801 | 0.8106 | 0.6733 | 0.7379 | 0.6834 | | 0.1314 | 5.0 | 3165 | 0.5633 | 0.6132 | 0.7385 | 0.6271 | 0.8065 | 0.8772 | 0.8065 | 0.6967 | 0.7606 | 0.7032 | | 0.0892 | 6.0 | 3798 | 0.6073 | 0.6425 | 0.7499 | 0.6545 | 0.7844 | 0.8610 | 0.7844 | 0.7064 | 0.7634 | 0.7113 | | 0.0721 | 7.0 | 4431 | 0.6114 | 0.6428 | 0.7488 | 0.6519 | 0.7855 | 0.8627 | 0.7855 | 0.7071 | 0.7638 | 0.7109 | |label | precision |recall |f1-score| support| |:-------------:|:---------:|:-----:|:------:|:------:| | Agriculture | 0.740 | 0.840|0.786|200| | Buildings | 0.535 |0.833|0.652|18| | Coastal Zone | 0.579|0.718|0.641|71| | Cross-Cutting Area | 0.551 |0.738|0.631|180| | Disaster Risk Management (DRM) | 0.642 |0.717|0.67|85| | Economy-wide | 0.401 |0.600| 0.481|85| | Education | 0.652|0.652|0.652|23| | Energy | 0.771 |0.862|0.814|254| | Environment | 0.539 |0.747|0.626|91| | Health | 0.743|0.808|0.774|68| | Industries | 0.648|0.853|0.736|41| | LULUCF/Forestry | 0.728|0.849|0.784|193| | Social Development | 0.661 | 0.767|0.710|56| | Tourism | 0.586 |0.607|0.596|28| | Transport | 0.715|0.822|0.765|107| | Urban | 0.414 |0.568|0.479|51| | Waste | 0.662|0.898|0.762|59| | Water | 0.601 |.783|0.680|106| ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.05819 kg of CO2 - **Hours Used**: 1.43 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