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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: ADAPMIT-multilabel-climatebert |
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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: 37.5331346075112 |
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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: 0.659 |
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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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# ADAPMIT-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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- Loss: 0.3535 |
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- Precision-micro: 0.8999 |
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- Precision-samples: 0.8559 |
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- Precision-weighted: 0.9001 |
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- Recall-micro: 0.9173 |
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- Recall-samples: 0.8592 |
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- Recall-weighted: 0.9173 |
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- F1-micro: 0.9085 |
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- F1-samples: 0.8521 |
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- F1-weighted: 0.9085 |
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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 2 labels - |
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AdaptationLabel, MitigationLabel - that are relevant to a particular task or application |
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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: 12538 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| AdaptationLabel | 5439 | |
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| MitigationLabel | 6659 | |
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- Validation Dataset: 1190 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| AdaptationLabel | 533 | |
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| MitigationLabel | 604 | |
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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: 6.03e-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: 300 |
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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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| 0.3512 | 1.0 | 784 | 0.3253 | 0.8530 | 0.8273 | 0.8572 | 0.8883 | 0.8311 | 0.8883 | 0.8703 | 0.8238 | 0.8703 | |
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| 0.2152 | 2.0 | 1568 | 0.2604 | 0.8999 | 0.8580 | 0.9002 | 0.9094 | 0.8521 | 0.9094 | 0.9046 | 0.8510 | 0.9046 | |
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| 0.1348 | 3.0 | 2352 | 0.2908 | 0.9038 | 0.8626 | 0.9059 | 0.9173 | 0.8588 | 0.9173 | 0.9105 | 0.8566 | 0.9107 | |
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| 0.0767 | 4.0 | 3136 | 0.3367 | 0.8999 | 0.8563 | 0.9000 | 0.9173 | 0.8588 | 0.9173 | 0.9085 | 0.8524 | 0.9085 | |
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| 0.0475 | 5.0 | 3920 | 0.3535 | 0.8999 | 0.8559 | 0.9001 | 0.9173 | 0.8592 | 0.9173 | 0.9085 | 0.8521 | 0.9085 | |
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|label | precision |recall |f1-score| support| |
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|:-------------:|:---------:|:-----:|:------:|:------:| |
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|AdaptationLabel |0.909 |0.908 |0.909 | 533.0 | |
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|MitigationLabel |0.891 |0.925 |0.908 | 604.0 | |
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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**: 0.0375 kg of CO2 |
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- **Hours Used**: 0.659 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 |
