--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: >- Unconditional Reduction The level of reduction planned unconditionally is expected to be up to 35% by 2030 as compared to the Business As Usual (BAU) scenario, taking 2005 as the reference year. Conditional Reduction In a conditional mitigation scenario Angola plans to reduce further its emissions. Therefore, the mitigation options identified in this scenario are expected to reduce an additional 15% below BAU emission levels by 2030. - text: >- Measure 300 MW total installed biomass power capacity in the country by Sector Energy GHG mitigation target 84 ktCO2e on average per year between 2020 and 2030 Monitoring procedures Newly added biomass capacity will be monitored on an annual basis by the Department of Climate Change of the Ministry of Natural Resources and Environment using data from the Ministry of Energy and Mines Comments - Installed capacity as of 2019 is around 40MW Measure 30% Electric Vehicles penetration for 2-wheelers and passengers cars in national vehicles mix Sector Transport GHG mitigation target 30 ktCO2e on average per year between 2020 and 2030 Monitoring procedures Share of Electric Vehicles in national vehicle mix will be monitored on an annual basis by the Department of Climate Change of the Ministry of Natural Resources and Environment using data from the Ministry of Public Works and Transport. - text: "� Australia adopts a target of net zero emissions by 2050. This is an economy-wide target,\_covering all sectors and gases included in Australia’s national inventory. � In order to achieve net zero by 2050, Australia commits to seven low emissions technology stretch goals - ambitious but realistic goals to bring priority low emissions technologies to economic parity with existing mature technologies." - text: >- The GoP has taken a series of major initiatives as outlined in chapters 4 and 5. Hence, Pakistan intends to set a cumulative ambitious conditional target of overall 50% reduction of its projected emissions by 2030, with 15% from the country’s own resources and 35% subject to provision of international grant finance that would require USD 101 billion just for energy transition. 7.1 HIGH PRIORITY ACTIONS Addressing the Global Climate Summit at the United Nations in December 2020, the Prime Minister of Pakistan made an announcement to reduce future GHG emissions on a high priority basis if international financial and technical resources were made available: MITIGATION: 1. - text: >- This document enfolds Iceland’s first communication on its long-term strategy (LTS), to be updated when further analysis and policy documents are published on the matter. Iceland is committed to reducing its overall greenhouse gas emissions and reaching climate neutrality no later than 2040 and become fossil fuel free in 2050, which should set Iceland on a path to net negative emissions. pipeline_tag: text-classification inference: false co2_eq_emissions: emissions: 268.4261122496047 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: Intel(R) Xeon(R) CPU @ 2.20GHz ram_total_size: 12.674789428710938 hours_used: 2.03 hardware_used: 1 x Tesla V100-SXM2-16GB base_model: BAAI/bge-base-en-v1.5 datasets: - GIZ/policy_classification --- # SetFit with BAAI/bge-base-en-v1.5 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) 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 The purpose of this model is to predict multiple labels simultaneously from a given input data. Specifically, the model will predict 3 labels - GHGLabel, NetzeroLabel, NonGHGLabel- that are relevant to a particular task or application - **GHGLabel**: GHG targets refer to contributions framed as targeted \ outcomes in GHG terms - **NetzeroLabel**: Identifies if it contains Netzero Target or not. - **NonGHGLabel**: Target not in terms of GHG, like energy efficiency, expansion of Solar Energy production etc. ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 3 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("GIZ/SUBTARGET_multilabel_bge") # Run inference preds = model("This document enfolds Iceland’s first communication on its long-term strategy (LTS), to be updated when further analysis and policy documents are published on the matter. Iceland is committed to reducing its overall greenhouse gas emissions and reaching climate neutrality no later than 2040 and become fossil fuel free in 2050, which should set Iceland on a path to net negative emissions.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 19 | 78.5467 | 173 | - Training Dataset: 728 | Class | Positive Count of Class| |:-------------|:--------| | GHGLabel | 440 | | NetzeroLabel | 120 | | NonGHGLabel | 259| - Validation Dataset: 80 | Class | Positive Count of Class| |:-------------|:--------| | GHGLabel | 49 | | NetzeroLabel | 11 | | NonGHGLabel | 30| ### Training Hyperparameters - batch_size: (8, 2) - num_epochs: (1, 0) - max_steps: -1 - sampling_strategy: undersampling - body_learning_rate: (6.86e-06, 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 ### Embedding Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:-----:|:-------------:|:---------------:| | 0.0000 | 1 | 0.2227 | - | | 0.1519 | 5000 | 0.015 | 0.0831 | | 0.3038 | 10000 | 0.0146 | 0.0924 | | 0.4557 | 15000 | 0.0197 | 0.0827 | | 0.6076 | 20000 | 0.0031 | 0.0883 | | 0.7595 | 25000 | 0.0439 | 0.0865 | | 0.9114 | 30000 | 0.0029 | 0.0914 | |label | precision |recall |f1-score| support| |:-------------:|:---------:|:-----:|:------:|:------:| |GHG |0.884 |0.938 |0.910 | 49.0 | |Netzero |0.846 |1.000 |0.916 | 11.0 | |NonGHG |0.903 |0.933 |0.918 | 30.0 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Carbon Emitted**: 0.268 kg of CO2 - **Hours Used**: 2.03 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x Tesla V100-SXM2-16GB - **CPU Model**: Intel(R) Xeon(R) CPU @ 2.20GHz - **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: 2.17.0 - Tokenizers: 0.15.2 ## 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} } ```