--- 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: 'There is, of course, much to digest. I hope that these rubes and those who incited them are locked up, along with the fake electors and their advisors, and those who conspired to convince elections officials to violate the law, and finally, those who have and continue to threaten true Americans just doing their constitution-based jobs. One thing jumps out. Judge McFadden, who seems willing to demand that the government prove its case beyond a reasonable doubt, also seems to be willing to sentence convicted lawbreakers to serious time. That he acquitted the guy who claimed the police let him gives me confidence that these are not sham trials.The thing that I haven’t heard much about are the firings, trials, convictions, and sentences of those LEOs who aided and abetted the traitors. That would include the cops who let Mr. Martin enter the Capitol, and those on Trump’s secret service detail who may have been aiding Trump’s efforts to foment a riot. ' - text: 'Both Vladimir Putin and Yevgeny Prigozhin are international war criminals.Both also undermined US elections in favor of Trump.https://www.reuters.com/world/us/russias-prigozhin-admits-interfering-us-elections-2022-11-07/ ' - text: 'Aaron 100 percent. citizens united was a huge win for Russian citizen Vlad and Chinese citizen Xi. ' - text: 'George Corsetti “Russia did NOT interfere in the 2016 election.”Sorry George, this is not true. Read the Russia report, it details more than a dozen felonies committed by TFG and his family and Campaign personnel during the 2015/16 Campaign along with evidence of Russian hackers and agents directly interfering in the 2016 election. ' - text: 'Ms.Renkl does a nice job here, yet only hints at the decimation to public schools, libraries, governance, and healthcare by Bill Lee and the Red Legislators .Tennessee has a $50 B per year budget, $25B 0f this comes from federal government. It is a wealthy state ranking in the top 16 economically and 3rd in fiscal stability ( USNews).The stability comes from the egregious, wrongheaded use of federal monies earmarked for public schools and healthcare,Governor controls all Federal school and healthcare dollars rather than decimating to citizens. The US tax payer is subsidizing this state as the Governor and legislators deny ACA low cost insurance to WORKING poor and the Governor used for unrelated purposes. . Federal public school monies are used to subsidize private schools and Lee’s pet project:private DeVos/Hillsdale religious charter schools. US tax payers should be made aware of the mishandling of our tax dollars in support of the ultra conservative regime. ' 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: 0.8 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** | 0.8 | ## 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-2") # Run inference preds = model("Aaron 100 percent. citizens united was a huge win for Russian citizen Vlad and Chinese citizen Xi. ") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 6 | 80.325 | 276 | | 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 | 0.4496 | - | | 0.0833 | 50 | 0.1797 | - | | 0.1667 | 100 | 0.0034 | - | | 0.25 | 150 | 0.0003 | - | | 0.3333 | 200 | 0.0002 | - | | 0.4167 | 250 | 0.0002 | - | | 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} } ```