---
license: apache-2.0
tags:
- generated_from_trainer
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: 0.1916
- F1-score: 0.9527

## 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: 9.375102561418467e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100.0
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 30   | 0.4243          | 0.9150   |
| No log        | 2.0   | 60   | 0.2486          | 0.9145   |
| No log        | 3.0   | 90   | 0.1950          | 0.9245   |
| No log        | 4.0   | 120  | 0.1953          | 0.9527   |
| No log        | 5.0   | 150  | 0.1916          | 0.9527   |

## Environmental Impact

*Carbon emissions were estimated using the [codecarbon](https://github.com/mlco2/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

[codecarbon INFO @ 20:45:15] Energy consumed for RAM : 0.005929 kWh. RAM Power : 9.54426097869873 W
[codecarbon INFO @ 20:45:15] Energy consumed for all GPUs : 0.042682 kWh. Total GPU Power : 36.884 W
[codecarbon INFO @ 20:45:15] Energy consumed for all CPUs : 0.026424 kWh. Total CPU Power : 42.5 W
[codecarbon INFO @ 20:45:15] 0.075035 kWh of electricity used since the beginning.
0.03666153971974974

### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3