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---
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
---
<!-- 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-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 |