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---
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 |