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---
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 |