metadata

license: apache-2.0
base_model: sentence-transformers/all-mpnet-base-v2
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: IKT_classifier_economywide_best
    results: []
widget:
  - text: >-
      Forestry, forestry and wildlife: "One million trees have been planted in
      the embankments, river/ canal banks to mitigate carbon emission and 2725.1
      ha marsh lands were rehabilitated and included in fisheries culture to
      enhance livelihood activities by the Ministry of Livestock and fisheries.
      Surface Water Use and Rainwater Harvesting Several city water supply
      authorities are implementing projects to increase surface water use and
      reducing ground water use. These projects will reduce energy consumption
      for pumping groundwater and contribute to GHG emission reduction."
    example_title: NEGATIVE
  - text: >-
      "CA global solution is needed to address a global problem. Along with the
      rest of the global community, Singapore will play our part to reduce
      emissions in support of the long-term temperature goal of the Paris
      Agreement. We have put forth a long-term low- emissions development
      strategy (LEDS) that aspires to halve emissions from its peak to 33 MtCO2e
      by 2050, with a view to achieving net-zero emissions as soon as viable in
      the second half of the century."
    example_title: ECONOMY-WIDE

IKT_classifier_economywide_best

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: 0.1595
Precision Macro: 0.9521
Precision Weighted: 0.9531
Recall Macro: 0.9533
Recall Weighted: 0.9528
F1-score: 0.9526
Accuracy: 0.9528

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: 7.132195091261459e-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: 300.0
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Macro	Precision Weighted	Recall Macro	Recall Weighted	F1-score	Accuracy
No log	1.0	60	0.1380	0.9521	0.9531	0.9533	0.9528	0.9526	0.9528
No log	2.0	120	0.1855	0.9523	0.9545	0.9547	0.9528	0.9527	0.9528
No log	3.0	180	0.1977	0.9523	0.9545	0.9547	0.9528	0.9527	0.9528
No log	4.0	240	0.1249	0.9723	0.9718	0.9708	0.9717	0.9715	0.9717
No log	5.0	300	0.1595	0.9521	0.9531	0.9533	0.9528	0.9526	0.9528

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3