---
license: apache-2.0
base_model: climatebert/distilroberta-base-climate-f
tags:
- generated_from_trainer
model-index:
- name: ADAPMIT-multilabel-climatebert
  results: []
datasets:
- GIZ/policy_classification
co2_eq_emissions:
  emissions: 37.5331346075112
  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: 0.659
  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. -->

# ADAPMIT-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: 0.3535
- Precision-micro: 0.8999
- Precision-samples: 0.8559
- Precision-weighted: 0.9001
- Recall-micro: 0.9173
- Recall-samples: 0.8592
- Recall-weighted: 0.9173
- F1-micro: 0.9085
- F1-samples: 0.8521
- F1-weighted: 0.9085

## 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 - 
AdaptationLabel, MitigationLabel - that are relevant to a particular task or application

## Intended uses & limitations

More information needed

## Training and evaluation data

- Training Dataset: 12538
| Class | Positive Count of Class|
|:-------------|:--------|
| AdaptationLabel | 5439 |
| MitigationLabel | 6659 |


- Validation Dataset: 1190
| Class | Positive Count of Class|
|:-------------|:--------|
| AdaptationLabel | 533 |
| MitigationLabel | 604 |

## 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: 5

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.3512        | 1.0   | 784  | 0.3253          | 0.8530          | 0.8273            | 0.8572             | 0.8883       | 0.8311         | 0.8883          | 0.8703   | 0.8238     | 0.8703      |
| 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      |
| 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      |
| 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      |
| 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      |

|label          | precision |recall |f1-score| support|
|:-------------:|:---------:|:-----:|:------:|:------:|
|AdaptationLabel	|0.909   	|0.908  |0.909   |	533.0  |
|MitigationLabel	|0.891	    |0.925  |0.908   |	604.0  |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.0375 kg of CO2
- **Hours Used**: 0.659 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