iki_target_setfit / README.md
ppsingh's picture
Update README.md
47d4760 verified
metadata
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 model that can be used for Text Classification. This SetFit model uses ppsingh/TAPP-multilabel-mpnet as the Sentence Transformer embedding model. A 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 with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
NEGATIVE
  • '(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.'
  • '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.'
  • '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.'
TARGET
  • '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).'
  • '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.'
  • '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.'

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

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.")

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.

  • 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

@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}
}