metadata

license: apache-2.0
base_model: sentence-transformers/all-mpnet-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: IKT_classifier_mitigation_best
    results: []
widget:
  - text: >-
      Existing Gas Turbine power plant (570 MW)  Installation of prepaid meter
       Bring down total T&D loss to a single digit by 2030 Transport 
      Improvement of road traffic congestion improvement in fuel efficiency) 
      Widening of roads (2 to 4 lanes) and improving road quality  Construct
      NMT and bicycle lanes  Electronic Road Pricing (ERP) or congestion
      charging  Reduction of private cars and encourage electric and hybrid
      vehicles  Development of Urban Transport Master Plans (UTMP) to improve
      transport systems in line with the Urban Plan/ City Plan for all major
      cities and urban area  Introducing Intelligent Transport System (ITS)
      based public transport management system to ensure better performance,
      enhance reliability, safety and service  Establish charging station
      network and electric buses in major cities  Modal shift from road to rail
      (25% modal shift of passenger-km) through different Transport projects
      such as BRT, MRT in major cities, Multi-modal hub creation, new bridges
      etc.
    example_title:
      - Active mob.
      - Public transport improvement
  - text: >-
      Energy efficiency improvement measures include market transformation to
      energy efficient lighting that showed significant drop in electricity
      consumption that reached 40% in some buildings as well as improved energy
      efficiency in industrial sector through energy management systems and
      simple energy optimization measures. • Low Carbon Transport: The further
      expansion in the Greater Cairo underground metro network included the
      operation of stage 4 of length 11.5 km (Phase I: 2019, Phase II: 2020) of
      the third Cairo metro line as a progress towards achieving the modal shift
      to low carbon mass transit.14 The third line is the first metro to link
      east and west Cairo and is expected to serve 2 million passenger trips per
      day.15 The concept of high quality service buses has been introduced to
      Egypt targeting car owners to use the newly public transportation system
      that is integrated with the existing mass transit systems.
    example_title:
      - Public transport improvement
  - text: >-
      Potential Actions Unconditional Contribution The targeted GHG emission
      reduction for unconditional contributions will be implemented through a
      set of mitigation actions. The potential mitigations actions are
      elaborated in Table 4. Table 4: Possible Mitigation Actions to deliver the
      Unconditional Contribution Sector Description Actions by 2030 Energy Power
       Implementation of renewable energy projects  Enhanced efficiency of
      existing power plants  Use of improved technology for power generation
      Transport  Improvement of fuel efficiency for transport sub- sector 
      Increase use of less emission- based transport system and improve Inland
      Water Transport System Power  Implementation of renewable energy projects
      of 911.8 MW  Grid-connected Solar-581 MW, Wind-149 MW, MW, Solar
      Mini-grid-56.8 MW  Installation of new Combined Cycle Gas based power
      plant (3208 MW)  Efficiency improvement of Existing Gas Turbine power
      plant (570 MW)  Installation of prepaid meter Transport  Improvement of
      road traffic congestion improvement in
    example_title:
      - Vehicle impr.
      - Improve infrastructure

IKT_classifier_mitigation_best

This model is a fine-tuned version of sentence-transformers/all-mpnet-base-v2 on the GIZ/policy_qa_v0_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.6517
Precision Micro: 0.3667
Precision Weighted: 0.4273
Precision Samples: 0.4539
Recall Micro: 0.7543
Recall Weighted: 0.7543
Recall Samples: 0.7982
F1-score: 0.5422
Accuracy: 0.1654

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3.6181464293180716e-05
train_batch_size: 3
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300.0
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Micro	Precision Weighted	Precision Samples	Recall Micro	Recall Weighted	Recall Samples	F1-score	Accuracy
No log	1.0	398	1.0635	0.1718	0.2238	0.1763	0.7714	0.7714	0.7945	0.2794	0.0
1.2442	2.0	796	0.8827	0.2167	0.2522	0.2388	0.7543	0.7543	0.7863	0.3518	0.0
0.9539	3.0	1194	0.7579	0.2710	0.3279	0.2979	0.7543	0.7543	0.7932	0.4134	0.0150
0.8265	4.0	1592	0.6773	0.3377	0.3943	0.3937	0.7429	0.7429	0.7901	0.4961	0.0752
0.8265	5.0	1990	0.6517	0.3667	0.4273	0.4539	0.7543	0.7543	0.7982	0.5422	0.1654

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3