--- base_model: sentence-transformers/all-mpnet-base-v2 library_name: setfit metrics: - accuracy pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'John Ondespot Help me out. So Yellen has to tell the President that they cannot afford to pay bondholders in the favour of US civil servants and military and homeless to keep society rolling and let the big banks hold out for money down the line? To float the entire USA financial system from collapse but also from societal rioting on Capitol Hill? I am getting this? Cause the more I read this is quite a debt watched by the major credit leaders of the US commercial and credit banking system? ' - text: 'Independent I disagree that, in your words, Lula "is the biggest thief in Brazil''s history." The excellent Guardian article you cite requires a careful reading to the end. To me, it seems like the Brazilian parliamentary system practically encourages corruption and has been rife with corruption in most administrations. Lula too fell into corruption to gain political support to enact his social reforms when faced with a minority in Congress. (This reminds me of the leftist Peruvian president who tried to dissolve the conservative dominated Congress that block any of his reforms.) Lula resorted to bribes to get support from minority parties. From the Guardian article: "Although illegal, this allowed the Workers’ Party to get things done. Lula’s first term delivered impressive progress on alleviating poverty, social spending and environmental controls."At the same time, "it was the Workers’ Party that had put in place the judicial reforms that allowed the investigation to go ahead. There would have been no Car Wash if the government had not appointed, in September 2013, an independent attorney general."So maybe Lula will prove to be a better president today. ' - text: 'The reality is that in Brazil the level of corruption has exceeded all limits, our system is similar to the American one, but imagine that a former president convicted of corruption in which he should have served a sentence of 9 years in 2018 was released for cheating by the judiciary and could still run for office (which is illegal under our constitution).Lula is not just a communist, he is the "kingpin" these protests are a sample of the desperation of people who fear for their freedom and integrity. ' - text: 'The ‘Trump of the Tropics’ Goes Bust The definitive challenge for Luiz Inácio Lula da Silva: to be president for all the people. SÃO PAULO, Brazil — As a shocked nation watched live on television and social media, thousands of radical supporters of a defeated president marched on the seat of the federal government, convinced that an election had been stolen. The mob ransacked the Congress, the Supreme Court and the presidential palace. It took the authorities several hours to arrest hundreds of people and finally restore order. The definitive challenge for Luiz Inácio Lula da Silva: to be president for all the people. ' - text: 'Friends,Speaker McCarthy and Representative Taylor Greene aren''t the problems---WE ARE!!!! And, by we, I mean the people who registered and voted for them. These clowns aren''t in the House of Representatives by osmosis, our fellow citizens voted them into office. Obviously, some Americans want the US to be run this way. But if you don''t, you can do something about it. Find out who''s going to be running for office in your area (county, city, state, federal) and start asking them questions? Are they running to represent you or someone else? Go ahead and ask them personal questions, tell them you read about it on "deepfake" website. But more importantly, don''t complain online. You can do something to stop them. It''s a simple 4 step process: 1) Clean out your ears! 2) Support the people you think will actually help you. 3) Register and 4) Vote. Yes, vote. Vote it like my life depends on it because it does! ' inference: true model-index: - name: SetFit with sentence-transformers/all-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 1.0 name: Accuracy --- # SetFit with sentence-transformers/all-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 384 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 | |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | yes | | | no | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 1.0 | ## 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("davidadamczyk/setfit-model-7") # Run inference preds = model("John Ondespot Help me out. So Yellen has to tell the President that they cannot afford to pay bondholders in the favour of US civil servants and military and homeless to keep society rolling and let the big banks hold out for money down the line? To float the entire USA financial system from collapse but also from societal rioting on Capitol Hill? I am getting this? Cause the more I read this is quite a debt watched by the major credit leaders of the US commercial and credit banking system? ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 23 | 107.2 | 272 | | Label | Training Sample Count | |:------|:----------------------| | no | 18 | | yes | 22 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 120 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0017 | 1 | 0.3073 | - | | 0.0833 | 50 | 0.1154 | - | | 0.1667 | 100 | 0.0012 | - | | 0.25 | 150 | 0.0002 | - | | 0.3333 | 200 | 0.0002 | - | | 0.4167 | 250 | 0.0001 | - | | 0.5 | 300 | 0.0001 | - | | 0.5833 | 350 | 0.0001 | - | | 0.6667 | 400 | 0.0001 | - | | 0.75 | 450 | 0.0001 | - | | 0.8333 | 500 | 0.0001 | - | | 0.9167 | 550 | 0.0001 | - | | 1.0 | 600 | 0.0001 | - | ### Framework Versions - Python: 3.10.13 - SetFit: 1.1.0 - Sentence Transformers: 3.0.1 - Transformers: 4.45.2 - PyTorch: 2.4.0+cu124 - Datasets: 2.21.0 - Tokenizers: 0.20.0 ## 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} } ```