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---
license: apache-2.0
base_model: climatebert/distilroberta-base-climate-f
tags:
- generated_from_trainer
model-index:
- name: CONDITIONAL-multilabel-climatebert
results: []
datasets:
- GIZ/policy_classification
co2_eq_emissions:
emissions: 17.3317785017907
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: 0.384
hardware_used: 1 x Tesla T4
---
<!-- 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. -->
# CONDITIONAL-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:
- Loss: 0.5460
- Precision-micro: 0.5020
- Precision-samples: 0.1954
- Precision-weighted: 0.5047
- Recall-micro: 0.7530
- Recall-samples: 0.1937
- Recall-weighted: 0.7530
- F1-micro: 0.6024
- F1-samples: 0.1927
- F1-weighted: 0.6033
## Model description
The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 2 labels -
ConditionalLabel, UnconditionalLabel - that are relevant to a particular task or application
- **Conditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made conditionally.
- **Unconditional**: In context of climate policy documents if certain Target/Action/Plan/Policy commitment is being made unconditionally.
## Intended uses & limitations
The dataset sometimes does not include the sub-heading/heading which indicates that the paragraph belongs to Conditional/Unconditional category.
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
the full long context had a difficulty in assessing the conditionality of commitments being made in paragraph.
## Training and evaluation data
- Training Dataset: 5901
| Class | Positive Count of Class|
|:-------------|:--------|
| ConditionalLabel | 1986 |
| UnconditionalLabel | 1312 |
- Validation Dataset: 1190
| Class | Positive Count of Class|
|:-------------|:--------|
| ConditionalLabel | 192 |
| UnconditionalLabel | 136 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6.03e-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: 300
- num_epochs: 6
### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.5644 | 1.0 | 369 | 0.4161 | 0.3642 | 0.1391 | 0.4167 | 0.5640 | 0.1416 | 0.5640 | 0.4426 | 0.1389 | 0.4372 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
|label | precision |recall |f1-score| support|
|:-------------:|:---------:|:-----:|:------:|:------:|
|ConditionalLabel |0.477 |0.765 |0.588 | 192.0 |
|UnconditionalLabel |0.543 |0.735 | 0.625 | 136.0 |
|
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.01733 kg of CO2
- **Hours Used**: 0.383 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 |