---
license: apache-2.0
base_model: climatebert/distilroberta-base-climate-f
tags:
- generated_from_trainer
model-index:
- name: TAPP-multilabel-climatebert
  results: []
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
datasets:
- GIZ/policy_classification
---

<!-- 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. -->

# TAPP-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.
It achieves the following results on the evaluation set:

*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*

- Precision-micro: 0.7368
- Precision-samples: 0.7425
- Precision-weighted: 0.7469
- Recall-micro: 0.8044
- Recall-samples: 0.7744
- Recall-weighted: 0.8044
- F1-micro: 0.7691
- F1-samples: 0.7384
- F1-weighted: 0.7721

## Model description

The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict four labels - 
ActionLabel, PlansLabel, PolicyLabel, and TargetLabel - that are relevant to a particular task or application
- **Target**: Targets are an intention to achieve a specific result, for example, to reduce GHG emissions to a specific level 
            (a GHG target) or increase energy efficiency or renewable energy to a specific level (a non-GHG target), typically by  
            a certain date.
- **Action**: Actions are an intention to implement specific means of achieving GHG reductions, usually in forms of concrete projects.
- **Policies**: Policies are domestic planning documents such as policies, regulations or guidlines.
- **Plans**:Plans  are broader than specific policies or actions, such as a general intention to ‘improve efficiency’, ‘develop renewable energy’, etc.

*The terms come from the World Bank's NDC platform and WRI's publication*

## 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: 3.06e-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: 200
- num_epochs: 5

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.7627        | 0.8   | 500  | 0.6471          | 0.6232          | 0.6727            | 0.6384             | 0.7989       | 0.7741         | 0.7989          | 0.7002   | 0.6929     | 0.7062      |
| 0.5542        | 1.59  | 1000 | 0.6114          | 0.6393          | 0.6754            | 0.6671             | 0.8154       | 0.7833         | 0.8154          | 0.7167   | 0.6999     | 0.7279      |
| 0.4219        | 2.39  | 1500 | 0.6145          | 0.7196          | 0.7236            | 0.7311             | 0.7989       | 0.7645         | 0.7989          | 0.7572   | 0.7231     | 0.7613      |
| 0.3268        | 3.19  | 2000 | 0.6363          | 0.7272          | 0.7383            | 0.7358             | 0.8053       | 0.7738         | 0.8053          | 0.7643   | 0.7374     | 0.7672      |
| 0.2477        | 3.99  | 2500 | 0.6509          | 0.7315          | 0.7351            | 0.7439             | 0.8007       | 0.7689         | 0.8007          | 0.7646   | 0.7319     | 0.7686      |
| 0.1989        | 4.78  | 3000 | 0.6527          | 0.7368          | 0.7425            | 0.7469             | 0.8044       | 0.7744         | 0.8044          | 0.7691   | 0.7384     | 0.7721      |

|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