---
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