--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: She is Female, her heart rate is 63, she walks 5000 steps daily and is Underweight. She slept at 22 hrs. Yesterday, she slept from 21.0 hrs to 5.0 hrs, with a duration of 380.0 minutes and 1 interruptions. The day before yesterday, she slept from 22.0 hrs to 7.0 hrs, with a duration of 440.0 minutes and 0 interruptions. - text: He is Male, his heart rate is 70, he walks 8500 steps daily, and is Normal. He slept at 23 hrs. Yesterday, he slept from 23.0hrs to 8.0 hrs, with a duration of 350.0 minutes and 3 interruptions. The day before yesterday, he slept from 22.0 hrs to 6.0 hrs, with a duration of 390.0 minutes and 1 interruptions. - text: She is Female, her heart rate is 85, she walks 3000 steps daily and is Overweight. She slept at 5 hrs. Yesterday, she slept from 6.0 hrs to 8.0 hrs, with a duration of 280.0 minutes and 2 interruptions. The day before yesterday, she slept from 5.0 hrs to 9.0 hrs, with a duration of 320.0 minutes and 7 interruptions. - text: He is Male, his heart rate is 92, he walks 7500 steps daily, and is Normal. He slept at 4 hrs. Yesterday, he slept from 5.0hrs to 9.0 hrs, with a duration of 320.0 minutes and 3 interruptions. The day before yesterday, he slept from 4.0 hrs to 10.0 hrs, with a duration of 350.0 minutes and 2 interruptions. - text: He is Male, his heart rate is 93, he walks 9800 steps daily, and is Normal. He slept at 0 hrs. Yesterday, he slept from 23.0hrs to 7.0 hrs, with a duration of 460.0 minutes and 0 interruptions. The day before yesterday, he slept from 23.0 hrs to 7.0 hrs, with a duration of 425.0 minutes and 1 interruptions. pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.6666666666666666 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | | | 2 | | | 0 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.6667 | ## 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("reecursion/few-shot-stress-detection") # Run inference preds = model("He is Male, his heart rate is 92, he walks 7500 steps daily, and is Normal. He slept at 4 hrs. Yesterday, he slept from 5.0hrs to 9.0 hrs, with a duration of 320.0 minutes and 3 interruptions. The day before yesterday, he slept from 4.0 hrs to 10.0 hrs, with a duration of 350.0 minutes and 2 interruptions.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 59 | 59.5 | 60 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 2 | | 1 | 10 | | 2 | 4 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - 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 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-----:|:----:|:-------------:|:---------------:| | 0.025 | 1 | 0.421 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.6.1 - Transformers: 4.38.2 - PyTorch: 2.2.1+cu121 - Datasets: 2.18.0 - 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} } ```