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---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: During 2021-2030, Thailand s LEDS will be implemented through the NDC roadmap
and sectoral action plans which provide detailed guidance on measures and realistic
actions to achieve the 1st NDC target by 2030, as well as regular monitoring and
evaluation of the progress and achievement. The monitoring and evaluation of the
mitigation measures relating to the Thailand’s LEDS will be carried out to ensure
its effectiveness and efficiency in achieving its objectives and key performance
indicators. Because it is a long-term plan spanning many years during which many
changes can occur, it is envisaged that it will be subject to a comprehensive
review every five years. This is consistent with the approach under the Paris
Agreement that assigned Parties to submit their NDCs to the UNFCCC every five
year.
- text: The NDC also benefited from the reviews and comments of these implementing
partners as well as local and international experts. Special thanks to The Honourable
Molwyn Joseph, Minister for Health, Wellness and the Environment, for his unwavering
commitment to advance this ambitious climate change agenda, while Antigua and
Barbuda faced an outbreak of the COVID-19 pandemic. Significant contributions
to the process were made by a wide-cross section of stakeholders from the public
and private sector, civil society, trade and industry groups and training institutions,
who attended NDC-related workshops, consultations and participated in key stakeholder
interviews organized to inform the NDC update.
- text: Antigua and Barbuda will mainstream gender in its energy planning through
an Inclusive Renewable Energy Strategy. This strategy will recognize and acknowledge,
among other things, the gender norms, and inequalities prevalent in the energy
sector, women and men’s differentiated access to energy, their different energy
needs and preferences, and different impacts that energy access could have on
their livelihoods. Antigua and Barbuda’s plan for an inclusive renewable energy
transition will ensure continued affordable and reliable access to electricity
and other energy services for all.
- text: 'Thailand’s climate actions are divided into short-term, medium-term and long-term
targets up to 2050. For the mitigation actions, short-term targets include: (i)
develop medium- and long-term GHG emission reduction targets and prepare roadmaps
for the implementation by sector, including the GHG emission reduction target
on a voluntary basis (pre-2020 target), Nationally Appropriate Mitigation Actions
(NAMAs) roadmaps, and measurement, reporting, and verification mechanisms, (ii)
establish domestic incentive mechanisms to encourage low carbon development. The
medium-term targets include: (i) reduce GHG emissions from energy and transport
sectors by 7-20% against BAU level by 2020, subject to the level of international
support, (ii) supply at least 25% of energy consumption from renewable energy
sources by 2021 and (iii) increase the ratio of municipalities with more than
10 m2 of green space per capita.'
- text: In the oil sector, the country has benefited from 372 million dollars for
the reduction of gas flaring at the initiative (GGFR - "Global Gas Flaring Reduction")
of the World Bank after having adopted in November 2015 a national reduction plan
flaring and associated gas upgrading. In the electricity sector, the NDC highlights
the development of hydroelectricity which should make it possible to cover 80%
of production in 2025, the remaining 20% ​​being
covered by gas and other renewable energies.
pipeline_tag: text-classification
inference: true
co2_eq_emissions:
emissions: 5.901369050433577
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz
ram_total_size: 12.674789428710938
hours_used: 0.185
hardware_used: 1 x Tesla T4
base_model: ppsingh/TAPP-multilabel-mpnet
---
# SetFit with ppsingh/TAPP-multilabel-mpnet
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ppsingh/TAPP-multilabel-mpnet](https://huggingface.co/ppsingh/TAPP-multilabel-mpnet) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [ppsingh/TAPP-multilabel-mpnet](https://huggingface.co/ppsingh/TAPP-multilabel-mpnet)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
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### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:---------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| NEGATIVE | <ul><li>'(p 70-1).Antigua and Barbuda’s 2021 update to the first Nationally Determined Contribution the most vulnerable in society have been predominantly focused on adaptation measures like building resilience to flooding and hurricanes. The updated NDC ambition provides an opportunity to focus more intently on enabling access to energy efficiency and renewable energy for the most vulnerable, particularly women who are most affected when electricity is not available since the grid is down after an extreme weather event. Nationally, Antigua and Barbuda intends to utilize the SIRF Fund as a mechanism primarily to catalyse and leverage investment in the transition for NGOs, MSMEs and informal sectors that normally cannot access traditional local commercial financing due to perceived high risks.'</li><li>'The transport system cost will be increased by 16.2% compared to the BAU level. Electric trucks and electric pick-ups will account for the highest share of investment followed by electric buses and trucks. In the manufacturing industries, the energy efficiency improvement in the heating and the motor systems and the deployment of CCS require the highest investment in the non-metallic and the chemical industries in 2050. The manufacturing industries system cost will be increased by 15.3% compared to the BAU level.'</li><li>'Figure 1-9: Total GHG emissions by sector (excluding LULUCF) 2000 and 2016 1.2.2 Greenhouse Gas Emission by Sector • Energy Total direct GHG emissions from the Energy sector in 2016 were estimated to be 253,895.61 eq. The majority of GHG emissions in the Energy sector were generated by fuel combustion, consisting mostly of grid-connected electricity and heat production at around eq (42.84%). GHG emissions from Transport, Manufacturing Industries and Construction, and other sectors were 68,260.17 GgCO2 eq eq (6.10%), respectively. Fugitive Emissions from fuel eq or a little over 4.33% of total GHG emissions from the Energy sector. Details of GHG emissions in the Energy sector by gas type and source in 2016 are presented in Figure 1-10. Source: Thailand Third Biennial Update Report, UNFCCC 2020.'</li></ul> |
| TARGET | <ul><li>'DNPM, NFA,. Cocoa. Board,. Spice Board,. Provincial. gov-ernments. in the. Momase. region. Ongoing -. 2025. 340. European Union. Support committed. Priority Sector: Health. By 2030, 100% of the population benefit from introduced health measures to respond to malaria and other climate-sensitive diseases in PNG. Action or Activity. Indicator. Status. Lead. Implementing. Agencies. Supporting. Agencies. Time Frame. Budget (USD). Funding Source. (Existing/Potential). Other Support. Improve vector control. measures, with a priority. of all households having. access to a long-lasting. insecticidal net (LLIN).'</li><li>'Conditionality: With national effort it is intended to increase the attention to vulnerable groups in case of disasters and/or emergencies up to 50% of the target and 100% of the target with international cooperation. Description: In this goal, it is projected to increase coverage from 33% to 50% (211,000 families) of agricultural insurance in attention to the number of families, whose crops were affected by various adverse weather events (flood, drought, frost, hailstorm, among others), in addition to the implementation of comprehensive actions for risk management and adaptation to Climate Change.'</li><li>'By 2030, upgrade watershed health and vitality in at least 20 districts to a higher condition category. By 2030, create an inventory of wetlands in Nepal and sustainably manage vulnerable wetlands. By 2025, enhance the sink capacity of the landuse sector by instituting the Forest Development Fund (FDF) for compensation of plantations and forest restoration. Increase growing stock including Mean Annual Increment in Tarai, Hills and Mountains. Afforest/reforest viable public and private lands, including agroforestry.'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("ppsingh/iki_target_setfit")
# Run inference
preds = model("In the oil sector, the country has benefited from 372 million dollars for the reduction of gas flaring at the initiative (GGFR - \"Global Gas Flaring Reduction\") of the World Bank after having adopted in November 2015 a national reduction plan flaring and associated gas upgrading. In the electricity sector, the NDC highlights the development of hydroelectricity which should make it possible to cover 80% of production in 2025, the remaining 20% ​​being covered by gas and other renewable energies.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:---------|:----|
| Word count | 58 | 116.6632 | 508 |
| Label | Training Sample Count |
|:---------|:----------------------|
| NEGATIVE | 51 |
| TARGET | 44 |
### Training Hyperparameters
- batch_size: (8, 2)
- num_epochs: (1, 0)
- max_steps: -1
- sampling_strategy: undersampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0018 | 1 | 0.3343 | - |
| 0.1783 | 100 | 0.0026 | 0.1965 |
| 0.3565 | 200 | 0.0001 | 0.1995 |
| 0.5348 | 300 | 0.0001 | 0.2105 |
| 0.7130 | 400 | 0.0001 | 0.2153 |
| 0.8913 | 500 | 0.0 | 0.1927 |
### Training Results Classifier
- Classes Representation in Test Data: Target: 9, Negative: 8
- F1-score: 87.8%
- Accuracy: 88.2%
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.006 kg of CO2
- **Hours Used**: 0.185 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x Tesla T4
- **CPU Model**: Intel(R) Xeon(R) CPU @ 2.00GHz
- **RAM Size**: 12.67 GB
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.3.1
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.3.0
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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