---
license: mit
base_model: BAAI/bge-base-en-v1.5
tags:
- generated_from_trainer
model-index:
- name: SECTOR-multilabel-bge
  results: []
datasets:
- GIZ/policy_classification

co2_eq_emissions:
  emissions: 58.1932553246115
  source: codecarbon
  training_type: fine-tuning
  on_cloud: true
  cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
  ram_total_size: 12.6747817993164
  hours_used: 1.43
  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. -->

# SECTOR-multilabel-bge

This model is a fine-tuned version of [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [Policy-Classification](https://huggingface.co/datasets/GIZ/policy_classification) dataset.

*The loss function BCEWithLogitsLoss is modified with pos_weight to focus on recall, therefore instead of loss the evaluation metrics are used to assess the model performance during training*
It achieves the following results on the evaluation set:
- Loss: 0.6114
- Precision-micro: 0.6428
- Precision-samples: 0.7488
- Precision-weighted: 0.6519
- Recall-micro: 0.7855
- Recall-samples: 0.8627
- Recall-weighted: 0.7855
- F1-micro: 0.7071
- F1-samples: 0.7638
- F1-weighted: 0.7109

## Model description

The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict Sector labels - Agriculture,Buildings,
Coastal Zone,Cross-Cutting Area,Disaster Risk Management (DRM),Economy-wide,Education,Energy,Environment,Health,Industries,LULUCF/Forestry,Social Development,Tourism,
Transport,Urban,Waste,Water

## Intended uses & limitations

More information needed

## Training and evaluation data

- Training Dataset: 10123
| Class | Positive Count of Class|
|:-------------|:--------|
| Agriculture | 2235 |
| Buildings | 169 |
| Coastal Zone | 698|
| Cross-Cutting Area | 1853 |
| Disaster Risk Management (DRM) | 814 |
| Economy-wide | 873 |
| Education | 180|
| Energy | 2847 |
| Environment | 905 |
| Health | 662|
| Industries | 419 |
| LULUCF/Forestry | 1861|
| Social Development | 507 |
| Tourism | 192 |
| Transport | 1173|
| Urban | 558 |
| Waste | 714|
| Water | 1207 |

- Validation Dataset: 936
| Class | Positive Count of Class|
|:-------------|:--------|
| Agriculture | 200 |
| Buildings | 18 |
| Coastal Zone | 71|
| Cross-Cutting Area | 180 |
| Disaster Risk Management (DRM) | 85 |
| Economy-wide | 85 |
| Education | 23|
| Energy | 254 |
| Environment | 91 |
| Health | 68|
| Industries | 41 |
| LULUCF/Forestry | 193|
| Social Development | 56 |
| Tourism | 28 |
| Transport | 107|
| Urban | 51 |
| Waste | 59|
| Water | 106 |

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7.04e-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: 7

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:-----------------:|:------------------:|:------------:|:--------------:|:---------------:|:--------:|:----------:|:-----------:|
| 0.7077        | 1.0   | 633  | 0.5490          | 0.4226          | 0.5465            | 0.4954             | 0.8211       | 0.8908         | 0.8211          | 0.5580   | 0.6243     | 0.5977      |
| 0.4546        | 2.0   | 1266 | 0.5009          | 0.4899          | 0.6127            | 0.5202             | 0.8438       | 0.9023         | 0.8438          | 0.6199   | 0.6822     | 0.6366      |
| 0.3105        | 3.0   | 1899 | 0.4947          | 0.5005          | 0.6593            | 0.5317             | 0.8508       | 0.8970         | 0.8508          | 0.6303   | 0.7125     | 0.6474      |
| 0.2044        | 4.0   | 2532 | 0.5430          | 0.5757          | 0.7044            | 0.5970             | 0.8106       | 0.8801         | 0.8106          | 0.6733   | 0.7379     | 0.6834      |
| 0.1314        | 5.0   | 3165 | 0.5633          | 0.6132          | 0.7385            | 0.6271             | 0.8065       | 0.8772         | 0.8065          | 0.6967   | 0.7606     | 0.7032      |
| 0.0892        | 6.0   | 3798 | 0.6073          | 0.6425          | 0.7499            | 0.6545             | 0.7844       | 0.8610         | 0.7844          | 0.7064   | 0.7634     | 0.7113      |
| 0.0721        | 7.0   | 4431 | 0.6114          | 0.6428          | 0.7488            | 0.6519             | 0.7855       | 0.8627         | 0.7855          | 0.7071   | 0.7638     | 0.7109      |

|label          | precision |recall |f1-score| support|
|:-------------:|:---------:|:-----:|:------:|:------:|
| Agriculture | 0.740 | 0.840|0.786|200|
| Buildings | 0.535 |0.833|0.652|18|
| Coastal Zone | 0.579|0.718|0.641|71|
| Cross-Cutting Area | 0.551 |0.738|0.631|180|
| Disaster Risk Management (DRM) | 0.642 |0.717|0.67|85|
| Economy-wide | 0.401 |0.600|	0.481|85|
| Education | 0.652|0.652|0.652|23|
| Energy | 0.771 |0.862|0.814|254|
| Environment | 0.539 |0.747|0.626|91|
| Health | 0.743|0.808|0.774|68|
| Industries | 0.648|0.853|0.736|41|
| LULUCF/Forestry | 0.728|0.849|0.784|193|
| Social Development | 0.661 |	0.767|0.710|56|
| Tourism | 0.586 |0.607|0.596|28|
| Transport | 0.715|0.822|0.765|107|
| Urban | 0.414 |0.568|0.479|51|
| Waste | 0.662|0.898|0.762|59|
| Water | 0.601 |.783|0.680|106|

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.05819 kg of CO2
- **Hours Used**: 1.43 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