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--- |
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license: mit |
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base_model: BAAI/bge-base-en-v1.5 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: SECTOR-multilabel-bge |
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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 |
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should probably proofread and complete it, then remove this comment. --> |
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# SECTOR-multilabel-bge |
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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. |
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*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* |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6114 |
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- Precision-micro: 0.6428 |
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- Precision-samples: 0.7488 |
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- Precision-weighted: 0.6519 |
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- Recall-micro: 0.7855 |
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- Recall-samples: 0.8627 |
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- Recall-weighted: 0.7855 |
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- F1-micro: 0.7071 |
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- F1-samples: 0.7638 |
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- F1-weighted: 0.7109 |
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## Model description |
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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, |
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Coastal Zone,Cross-Cutting Area,Disaster Risk Management (DRM),Economy-wide,Education,Energy,Environment,Health,Industries,LULUCF/Forestry,Social Development,Tourism, |
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Transport,Urban,Waste,Water |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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- Training Dataset: 10123 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| Agriculture | 2235 | |
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| Buildings | 169 | |
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| Coastal Zone | 698| |
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| Cross-Cutting Area | 1853 | |
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| Disaster Risk Management (DRM) | 814 | |
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| Economy-wide | 873 | |
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| Education | 180| |
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| Energy | 2847 | |
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| Environment | 905 | |
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| Health | 662| |
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| Industries | 419 | |
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| LULUCF/Forestry | 1861| |
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| Social Development | 507 | |
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| Tourism | 192 | |
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| Transport | 1173| |
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| Urban | 558 | |
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| Waste | 714| |
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| Water | 1207 | |
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- Validation Dataset: 936 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| Agriculture | 200 | |
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| Buildings | 18 | |
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| Coastal Zone | 71| |
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| Cross-Cutting Area | 180 | |
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| Disaster Risk Management (DRM) | 85 | |
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| Economy-wide | 85 | |
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| Education | 23| |
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| Energy | 254 | |
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| Environment | 91 | |
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| Health | 68| |
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| Industries | 41 | |
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| LULUCF/Forestry | 193| |
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| Social Development | 56 | |
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| Tourism | 28 | |
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| Transport | 107| |
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| Urban | 51 | |
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| Waste | 59| |
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| Water | 106 | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.04e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision-micro | Precision-samples | Precision-weighted | Recall-micro | Recall-samples | Recall-weighted | F1-micro | F1-samples | F1-weighted | |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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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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- **GPU Model**: 1 x Tesla T4 |
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- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.00GHz |
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- **RAM Size**: 12.67 GB |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
