metadata

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
  - 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
library_name: transformers

mpnet-multilabel-sector-classifier

This model is a fine-tuned version of 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. 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