--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: He is Male, his heart rate is 70, he walks 8700 steps daily, and is Normal Weight. He slept at 23 hrs. Yesterday, he slept from 23hrs to 7 hrs, with a duration of 580 minutes and 4 interruptions. The day before yesterday, he slept from 22 hrs to 6 hrs, with a duration of 490 minutes and 2 interruptions. - text: She is Female, her heart rate is 87, she walks 5300 steps daily and is Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6 hrs, with a duration of 510 minutes and 5 interruptions. The day before yesterday, she slept from 21 hrs to 6 hrs, with a duration of 410 minutes and 3 interruptions. - text: She is Female, her heart rate is 83, she walks 4700 steps daily and is Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6 hrs, with a duration of 490 minutes and 5 interruptions. The day before yesterday, she slept from 21 hrs to 5 hrs, with a duration of 390 minutes and 2 interruptions. - text: She is Female, her heart rate is 87, she walks 5700 steps daily and is Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6 hrs, with a duration of 540 minutes and 5 interruptions. The day before yesterday, she slept from 21 hrs to 6 hrs, with a duration of 430 minutes and 3 interruptions. - text: She is Female, her heart rate is 85, she walks 5400 steps daily and is Overweight. She slept at 22 hrs. Yesterday, she slept from 21 hrs to 6 hrs, with a duration of 530 minutes and 5 interruptions. The day before yesterday, she slept from 21 hrs to 5 hrs, with a duration of 400 minutes and 2 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: 0.8235294117647058 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:** 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 | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 |