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 mobility
      - Public transport improvement
  - text: >-
      Gas Turbine power plant (570 MW)  Installation of prepaid meter 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  Modal shift from road to rail
      (10% modal shift of passenger-km) through different Transport projects
      such as BRT, MRT in major cities, Multi-modal hub creation, Padma Bridge
      etc.
    example_title:
      - Active mobility
      - Public transport improvement
      - Electric mobility
  - text: >-
      Moreover, establish 34 new road axes on the Nile, construct 1,000 bridges
      and tunnels, construct paved roads within the governorates, and utilize
      modern asphalt recycling technologies to reduce environmental impacts.
      This would improve interconnections between cities and decrease commuting
      time and fuel consumption for road vehicles.
    example_title:
      - 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: 1.0515
Precision Micro: 0.2570
Precision Weighted: 0.2809
Precision Samples: 0.2896
Recall Micro: 0.6815
Recall Weighted: 0.6815
Recall Samples: 0.7119
F1-score: 0.3907
Accuracy: 0.0095

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	313	1.2909	0.1858	0.2078	0.1957	0.7185	0.7185	0.7222	0.2977	0.0
1.262	2.0	626	1.0875	0.2099	0.2605	0.2295	0.7852	0.7852	0.8071	0.3431	0.0
1.262	3.0	939	1.0171	0.2284	0.2612	0.2539	0.7630	0.7630	0.7746	0.3643	0.0095
1.0059	4.0	1252	1.0510	0.2519	0.2764	0.2914	0.7259	0.7259	0.7563	0.4013	0.0095
0.8421	5.0	1565	1.0515	0.2570	0.2809	0.2896	0.6815	0.6815	0.7119	0.3907	0.0095

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3