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--- |
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license: mit |
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base_model: BAAI/bge-base-en-v1.5 |
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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-bge |
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results: [] |
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datasets: |
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- GIZ/policy_classification |
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co2_eq_emissions: |
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emissions: 71.4552917731392 |
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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 @ 2.30GHz |
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ram_total_size: 12.6747894287109 |
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hours_used: 1.36 |
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hardware_used: 1 x Tesla T4 |
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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-bge |
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This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) on the [Policy-Classification](https://huggingface.co./datasets/GIZ/policy_classification) dataset. |
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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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It achieves the following results on the evaluation set: |
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- Precision-micro: 0.7772 |
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- Precision-samples: 0.7644 |
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- Precision-weighted: 0.7756 |
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- Recall-micro: 0.8329 |
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- Recall-samples: 0.7920 |
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- Recall-weighted: 0.8329 |
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- F1-micro: 0.8041 |
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- F1-samples: 0.7609 |
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- F1-weighted: 0.8029 |
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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: 7.4e-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: 7 |
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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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| 0.7161 | 1.0 | 627 | 0.6322 | 0.5931 | 0.6373 | 0.6274 | 0.8219 | 0.7833 | 0.8219 | 0.6890 | 0.6728 | 0.7000 | |
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| 0.4549 | 2.0 | 1254 | 0.5420 | 0.6639 | 0.6891 | 0.7049 | 0.8090 | 0.7684 | 0.8090 | 0.7293 | 0.7048 | 0.7409 | |
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| 0.2599 | 3.0 | 1881 | 0.6966 | 0.7354 | 0.7396 | 0.7346 | 0.8219 | 0.7845 | 0.8219 | 0.7762 | 0.7425 | 0.7713 | |
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| 0.1405 | 4.0 | 2508 | 0.7530 | 0.7569 | 0.7494 | 0.7569 | 0.8292 | 0.7899 | 0.8292 | 0.7914 | 0.7505 | 0.7905 | |
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| 0.0681 | 5.0 | 3135 | 0.8234 | 0.7596 | 0.7535 | 0.7599 | 0.8356 | 0.7945 | 0.8356 | 0.7958 | 0.7546 | 0.7953 | |
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| 0.0291 | 6.0 | 3762 | 0.8849 | 0.7773 | 0.7640 | 0.7776 | 0.8301 | 0.7890 | 0.8301 | 0.8028 | 0.7597 | 0.8027 | |
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| 0.0147 | 7.0 | 4389 | 0.9217 | 0.7772 | 0.7644 | 0.7756 | 0.8329 | 0.7920 | 0.8329 | 0.8041 | 0.7609 | 0.8029 | |
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|label | precision |recall |f1-score| support| |
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|:-------------:|:---------:|:-----:|:------:|:------:| |
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|Action |0.826 |0.883 |0.853 | 513.0 | |
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|Plans |0.653 |0.646 |0.649 | 198.0 | |
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|Policy |0.726 |0.803 |0.762 | 122.0 | |
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|Target |0.791 |0.890 |0.838 | 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**: 0.07145 kg of CO2 |
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- **Hours Used**: 1.36 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 @ 2.30GHz |
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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 |
