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---
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
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
- text: Distribution of GHG Emissions by Gas in 2018Determined Contribution at the
National Level Directorate of the Environment Evolution of GHG Emissions by Gas
between 1990 and 2018Determined Contribution at the National Level Directorate
of the Environment 2.2 Objectives for the Reduction of Greenhouse Gas Emissions
by 2030 The Principality of Monaco has set itself the objective, within the framework
of this National Contribution, of reducing its greenhouse gas emissions by 55%
by 2030.Determined Contribution at the National Level Directorate of the Environment
2.3 Main Policies and Measures In order to achieve its objectives by 2030, the
Principality of Monaco has already implemented important policies and measures
example_title: Economy-wide
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: mpnet-multilabel-sector-classifier
results: []
---
<!-- 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. -->
# 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). The carbon emission reported are incluidng the hyperparamter search performed on subset of training data*.
- **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 |