---
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: 23.3572576873636
  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: 0.529
  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: 10031
| Class | Positive Count of Class|
|:-------------|:--------|
| Action | 5416 |
| Plans | 2140 |
| Policy | 1396|
| Target | 2911 |

- Validation Dataset: 932
| Class | Positive Count of Class|
|:-------------|:--------|
| Action | 513 |
| Plans | 198 |
| Policy | 122 |
| Target | 256 |

## 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|
|:-------------:|:---------:|:-----:|:------:|:------:|
|Action	|0.828   	|0.807  |0.817   |	513.0  |
|Plans	        |0.560	    |0.707  |0.625   |	198.0  |
|Policy	|0.727      |0.786  |0.756   |	122.0  |
|Target	    |0.741     |0.886  |0.808  |	256.0  |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.02335 kg of CO2
- **Hours Used**: 0.529 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