--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: He is Male, his heart rate is 148, he walks 10000 steps daily, and is Normal. He slept at 1 hrs. Yesterday, he slept from 2.0hrs to 3.0 hrs, with a duration of 90.0 minutes and 0 interruptions. The day before yesterday, he slept from 22.0 hrs to 6.0 hrs, with a duration of 485.0 minutes and 0 interruptions. - text: She is Female, her heart rate is 68, she walks 11000 steps daily and is Normal. She slept at 1 hrs. Yesterday, she slept from 1.0 hrs to 9.0 hrs, with a duration of 495.0 minutes and 0 interruptions. The day before yesterday, she slept from 1.0 hrs to 10.0 hrs, with a duration of 540.0 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: 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. - text: He is Male, his heart rate is 75, he walks 11000 steps daily, and is Normal. He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs, with a duration of 400.0 minutes and 2 interruptions. The day before yesterday, he slept from 1.0 hrs to 8.0 hrs, with a duration of 450.0 minutes and 3 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: 0.8 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** | 0.8 | ## 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("reecursion/few-shot-stress-detection") # Run inference preds = model("He is Male, his heart rate is 75, he walks 11000 steps daily, and is Normal. He slept at 2 hrs. Yesterday, he slept from 3.0hrs to 7.0 hrs, with a duration of 400.0 minutes and 2 interruptions. The day before yesterday, he slept from 1.0 hrs to 8.0 hrs, with a duration of 450.0 minutes and 3 interruptions.") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 59 | 59.5 | 60 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 2 | | 1 | 6 | | 2 | 2 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 15 - 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0526 | 1 | 0.4337 | - | ### 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} } ```