--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: Specific information applicable to Parties, including regional economic integration organizations and their member States, that have reached an agreement to act jointly under Article 4, paragraph 2, of the Paris Agreement, including the Parties that agreed to act jointly and the terms of the agreement, in accordance with Article 4, paragraphs 16–18, of the Paris Agreement. Not applicable. (c). How the Party’s preparation of its nationally determined contribution has been informed by the outcomes of the global stocktake, in accordance with Article 4, paragraph 9, of the Paris Agreement. - text: 'In the shipping and aviation sectors, emission reduction efforts will be focused on distributing eco-friendly ships and enhancing the operational efficiency of aircraft. Agriculture, livestock farming and fisheries: The Republic Korea is introducing various options to accelerate low-carbon farming, for instance, improving irrigation techniques in rice paddies and adopting low-input systems for nitrogen fertilizers.' - text: As part of this commitment, Oman s upstream oil and gas industry is developing economically viable solutions to phase out routine flaring as quickly as possible and ahead of the World Bank s target date. IV. Climate Preparedness and Resilience. The Sultanate of Oman has stepped up its efforts in advancing its expertise and methodologies to better manage the climate change risks over the past five years. The adaptation efforts are underway, and the status of adaptation planning is still at a nascent stage. - text: 'Synergy and coherence 46 VII- Gender and youth 46 VIII- Education and employment 48 ANNEXES. 49 Annex No. 1: Details of mitigation measures, conditional and non-conditional, by sector 49 Annex No.2: List of adaptation actions proposed by sectors. 57 Annex No.3: GCF project portfolio. 63 CONTRIBUTION DENTERMINEE AT NATIONAL LEVEL CDN MAURITANIE LIST OF TABLES Table 1: Summary of funding needs for the CND 2021-2030 updated. 12 Table 2: CND 2021-2030 mitigation measures updated by sector (cumulative cost and reduction potential for the period). 14 Table 3: CND 2021-2030 adaptation measures updated by sector. Error!' - text: In the transport sector, restructuing is planned through a number of large infrastructure initiatives aiming to revive the role of public transport and achieving a relevant share of fuel efficient vehicles. Under both the conditional and unconditional mitigation scenarios, Lebanon will achieve sizeable emission reductions. With regards to adaptation, Lebanon has planned comprehensive sectoral actions related to water, agriculture/forestry and biodiversity, for example related to irrigation, forest management, etc. It also continues developing adaptation strategies in the remaining sectors. pipeline_tag: text-classification inference: false co2_eq_emissions: emissions: 25.8151164022705 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) CPU @ 2.00GHz ram_total_size: 12.674781799316406 hours_used: 0.622 hardware_used: 1 x Tesla T4 base_model: ppsingh/SECTOR-multilabel-mpnet_w --- # SetFit with ppsingh/SECTOR-multilabel-mpnet_w This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ppsingh/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w) 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/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 4 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) ## 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_sector_setfit") # Run inference preds = model("In the shipping and aviation sectors, emission reduction efforts will be focused on distributing eco-friendly ships and enhancing the operational efficiency of aircraft. Agriculture, livestock farming and fisheries: The Republic Korea is introducing various options to accelerate low-carbon farming, for instance, improving irrigation techniques in rice paddies and adopting low-input systems for nitrogen fertilizers.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 35 | 76.164 | 170 | - Training Dataset: 250 | Class | Positive Count of Class| |:-------------|:--------| | Economy-wide | 88 | | Energy | 63 | | Other Sector | 64 | | Transport | 139 | - Validation Dataset: 42 | Class | Positive Count of Class| |:-------------|:--------| | Economy-wide | 15 | | Energy | 11 | | Other Sector | 11 | | Transport | 24 | ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (1, 10) - max_steps: -1 - sampling_strategy: oversampling - 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0005 | 1 | 0.2029 | - | | 0.0993 | 200 | 0.0111 | 0.1124 | | 0.1985 | 400 | 0.0063 | 0.111 | | 0.2978 | 600 | 0.0183 | 0.1214 | | 0.3970 | 800 | 0.0197 | 0.1248 | | 0.4963 | 1000 | 0.0387 | 0.1339 | | 0.5955 | 1200 | 0.0026 | 0.1181 | | 0.6948 | 1400 | 0.0378 | 0.1208 | | 0.7940 | 1600 | 0.0285 | 0.1267 | | 0.8933 | 1800 | 0.0129 | 0.1254 | | 0.9926 | 2000 | 0.0341 | 0.1271 | ### Classifier Training Results | Epoch | Training F1-micro|Training F1-Samples |Training F1-weighted|Validation F1-micro |Validation F1-samples |Validation F1-weighted | |:------:|:----------------:|:------------------:|:------------------:|:------------------:|:--------------------:|:---------------------:| | 0 | 0.954 | 0.972 | 0.945 |0.824 | 0.819 | 0.813 | | 1 | 0.994 | 0.996 | 0.994 |0.850 | 0.832 | 0.852 | | 2 | 0.981 | 0.989 | 0.979 |0.850 | 0.843 | 0.852 | | 3 | 0.995 | 0.997 | 0.995 |0.852 | 0.843 | 0.858 | | 4 | 0.994 | 0.996 | 0.994 |0.852 | 0.843 | 0.858 | | 5 | 0.995 | 0.997 | 0.995 |0.859 | 0.848 | 0.863 | |label | precision |recall |f1-score| support| |:-------------:|:---------:|:-----:|:------:|:------:| |Economy-wide |0.857 |0.800 |0.827 | 15.0 | |Energy |1.00 |0.818 |0.900 | 11.0 | |Other Sector |0.615 |0.727 |0.667 | 11.0 | |Transport |0.958 |0.958 |0.958 | 24.0 | - Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504 - Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848 ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.026 kg of CO2 - **Hours Used**: 0.622 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} } ```