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--- |
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license: apache-2.0 |
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base_model: climatebert/distilroberta-base-climate-f |
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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-climatebert |
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results: [] |
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datasets: |
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- GIZ/policy_classification |
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co2_eq_emissions: |
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emissions: 23.3572576873636 |
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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: 12.6747894287109 |
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hours_used: 0.529 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# SECTOR-multilabel-climatebert |
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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. |
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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: 0.6028 |
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- Precision-micro: 0.6395 |
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- Precision-samples: 0.7543 |
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- Precision-weighted: 0.6475 |
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- Recall-micro: 0.7762 |
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- Recall-samples: 0.8583 |
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- Recall-weighted: 0.7762 |
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- F1-micro: 0.7012 |
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- F1-samples: 0.7655 |
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- F1-weighted: 0.7041 |
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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: 10031 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| Action | 5416 | |
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| Plans | 2140 | |
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| Policy | 1396| |
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| Target | 2911 | |
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- Validation Dataset: 932 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| Action | 513 | |
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| Plans | 198 | |
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| Policy | 122 | |
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| Target | 256 | |
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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: 9.07e-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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| 0.6978 | 1.0 | 633 | 0.5968 | 0.3948 | 0.5274 | 0.4982 | 0.7873 | 0.8675 | 0.7873 | 0.5259 | 0.5996 | 0.5793 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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|label | precision |recall |f1-score| support| |
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|:-------------:|:---------:|:-----:|:------:|:------:| |
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|Action |0.828 |0.807 |0.817 | 513.0 | |
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|Plans |0.560 |0.707 |0.625 | 198.0 | |
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|Policy |0.727 |0.786 |0.756 | 122.0 | |
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|Target |0.741 |0.886 |0.808 | 256.0 | |
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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**: 0.02335 kg of CO2 |
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- **Hours Used**: 0.529 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 |
