---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mpnet-multilabel-sector-classifier
  results: []
datasets:
- GIZ/sector_data
co2_eq_emissions: 0.276132
widget:
- text: "Forestry, forestry and wildlife: Vulnerability will be globally high to very high in zones 4 and 5, high to medium in the rest of the country but with strong trends in woodlands (droughts, extreme events);. - Water, sanitation and health: Vulnerability will be globally strong to very strong in zones 4 and 5, strong to medium in the rest of the country but with strong trends in the forested massifs (drought, floods and ground movement)"
  example_title: "Disaster Risk Management (DRM), Water, Environment"
- text: "Change fiscal policies on fossil fuel by 2025 to enable the transition to 100% renewable energy generation in the transportation sector"
  example_title: "Transport, Energy"
- text: "Implementation of the electro-optical channel regulations for the distributed electricians, technicians in other regions and cities. 2- An integrated nationalization that complements the use of smart meter technology inside buildings. 3- Integrated solar photovoltaic in buildings. 4- Support your company and use it from local women s clubs and local producers. Waste. 1- Setting up waste management laws, which encourages the transfer of waste into bottles and bottles, we will burn the waste streams and reduce waste. 1- We use the appropriate regulation in our time to remove electrical and electrical rations from waste. 2- An integrated application for waste management. 3- Investing fire methane on landfill sites. Farming. 1- Nannnai to protect and increase the natural gaunanat"
  example_title: "Social Development, Waste, Urban, Buildings"
  
---

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# mpnet-multilabel-sector-classifier

This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2273
- Precision Micro: 0.8075
- Precision Weighted: 0.8110
- Precision Samples: 0.8365
- Recall Micro: 0.8897
- Recall Weighted: 0.8897
- Recall Samples: 0.8922
- F1-score: 0.8464

## Model description

This model is trained for performing **Multi Label Sector Classification**. 


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6.9e-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: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 8
- weight_decay: 0.001
- gradient_acumulation_steps: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision Micro | Precision Weighted | Precision Samples | Recall Micro | Recall Weighted | Recall Samples | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:---------------:|:--------------:|:--------:|
| 0.4478        | 1.0   | 897  | 0.2277          | 0.6731          | 0.7183             | 0.7460            | 0.8822       | 0.8822          | 0.8989         | 0.7871   |
| 0.2241        | 2.0   | 1794 | 0.1862          | 0.7088          | 0.7485             | 0.7754            | 0.8933       | 0.8933          | 0.9110         | 0.8108   |
| 0.1647        | 3.0   | 2691 | 0.2025          | 0.6785          | 0.7023             | 0.7634            | 0.9124       | 0.9124          | 0.9252         | 0.8077   |
| 0.1232        | 4.0   | 3588 | 0.1839          | 0.7274          | 0.7322             | 0.7976            | 0.9029       | 0.9029          | 0.9134         | 0.8286   |
| 0.0899        | 5.0   | 4485 | 0.1889          | 0.7919          | 0.8007             | 0.8350            | 0.8909       | 0.8909          | 0.9060         | 0.8483   |
| 0.0653        | 6.0   | 5382 | 0.2039          | 0.7478          | 0.7544             | 0.8098            | 0.8973       | 0.8973          | 0.9114         | 0.8346   |
| 0.0462        | 7.0   | 6279 | 0.2149          | 0.7447          | 0.7500             | 0.8060            | 0.8989       | 0.8989          | 0.9107         | 0.8323   |
| 0.0336        | 8.0   | 7176 | 0.2181          | 0.7733          | 0.7780             | 0.8221            | 0.8909       | 0.8909          | 0.9031         | 0.8400   |

## Environmental Impact

*Carbon emissions were estimated using the [codecarbon](https://github.com/mlco2/codecarbon)*

- **Hardware Type:**  16GB T4
- **Hours used:** 3
- **Cloud Provider:** Google Colab
- **Carbon Emitted** : 0.276132
### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3