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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: CONDITIONAL-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: 17.3317785017907 |
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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: 0.384 |
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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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# CONDITIONAL-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: 0.5460 |
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- Precision-micro: 0.5020 |
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- Precision-samples: 0.1954 |
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- Precision-weighted: 0.5047 |
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- Recall-micro: 0.7530 |
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- Recall-samples: 0.1937 |
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- Recall-weighted: 0.7530 |
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- F1-micro: 0.6024 |
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- F1-samples: 0.1927 |
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- F1-weighted: 0.6033 |
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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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ConditionalLabel, UnconditionalLabel - that are relevant to a particular task or application |
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- **Conditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made conditionally. |
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- **Unconditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made unconditionally. |
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## Intended uses & limitations |
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The dataset sometimes does not include the sub-heading/heading which indicates that the paragraph belongs to Conditional/Unconditional category. |
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But has been copied from the relevant document from those sub-headings. This makes the assessment of Conditonality very difficult. Annotator when given only the paragraph without |
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the full long context had a difficulty in assessing the conditionality of commitments being made in paragraph. |
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## Training and evaluation data |
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- Training Dataset: 5901 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| ConditionalLabel | 1986 | |
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| UnconditionalLabel | 1312 | |
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- Validation Dataset: 1190 |
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| Class | Positive Count of Class| |
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|:-------------|:--------| |
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| ConditionalLabel | 192 | |
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| UnconditionalLabel | 136 | |
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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: 6 |
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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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| 0.5644 | 1.0 | 369 | 0.4161 | 0.3642 | 0.1391 | 0.4167 | 0.5640 | 0.1416 | 0.5640 | 0.4426 | 0.1389 | 0.4372 | |
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| 0.429 | 2.0 | 738 | 0.3616 | 0.4420 | 0.1803 | 0.4794 | 0.6860 | 0.1769 | 0.6860 | 0.5376 | 0.1768 | 0.5473 | |
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| 0.2657 | 3.0 | 1107 | 0.4233 | 0.4126 | 0.1950 | 0.4229 | 0.7774 | 0.1987 | 0.7774 | 0.5391 | 0.1944 | 0.5418 | |
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| 0.1482 | 4.0 | 1476 | 0.4301 | 0.4910 | 0.1891 | 0.4944 | 0.7470 | 0.1908 | 0.7470 | 0.5925 | 0.1882 | 0.5924 | |
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| 0.069 | 5.0 | 1845 | 0.5016 | 0.5126 | 0.1920 | 0.5193 | 0.7439 | 0.1912 | 0.7439 | 0.6070 | 0.1899 | 0.6090 | |
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| 0.0353 | 6.0 | 2214 | 0.5460 | 0.5020 | 0.1954 | 0.5047 | 0.7530 | 0.1937 | 0.7530 | 0.6024 | 0.1927 | 0.6033 | |
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|label | precision |recall |f1-score| support| |
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|:-------------:|:---------:|:-----:|:------:|:------:| |
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|ConditionalLabel |0.477 |0.765 |0.588 | 192.0 | |
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|UnconditionalLabel |0.543 |0.735 | 0.625 | 136.0 | |
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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**: 0.01733 kg of CO2 |
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- **Hours Used**: 0.383 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 |
