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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: TAPP-multilabel-climatebert |
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results: [] |
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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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datasets: |
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- GIZ/policy_classification |
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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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# TAPP-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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It achieves the following results on the evaluation set: |
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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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- Precision-micro: 0.7368 |
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- Precision-samples: 0.7425 |
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- Precision-weighted: 0.7469 |
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- Recall-micro: 0.8044 |
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- Recall-samples: 0.7744 |
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- Recall-weighted: 0.8044 |
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- F1-micro: 0.7691 |
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- F1-samples: 0.7384 |
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- F1-weighted: 0.7721 |
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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 four labels - |
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ActionLabel, PlansLabel, PolicyLabel, and TargetLabel - that are relevant to a particular task or application |
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- **Target**: Targets are an intention to achieve a specific result, for example, to reduce GHG emissions to a specific level |
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(a GHG target) or increase energy efficiency or renewable energy to a specific level (a non-GHG target), typically by |
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a certain date. |
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- **Action**: Actions are an intention to implement specific means of achieving GHG reductions, usually in forms of concrete projects. |
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- **Policies**: Policies are domestic planning documents such as policies, regulations or guidlines. |
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- **Plans**:Plans are broader than specific policies or actions, such as a general intention to ‘improve efficiency’, ‘develop renewable energy’, etc. |
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*The terms come from the World Bank's NDC platform and WRI's publication* |
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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: 3.06e-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: 200 |
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- num_epochs: 5 |
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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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| 0.7627 | 0.8 | 500 | 0.6471 | 0.6232 | 0.6727 | 0.6384 | 0.7989 | 0.7741 | 0.7989 | 0.7002 | 0.6929 | 0.7062 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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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 |
