---
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
---

<!-- 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. -->

# IKT_classifier_economywide_best

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: 0.1595
- 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