--- 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 ### 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 | | | TARGET | | ## 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.") ``` ## 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} } ```