--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer base_model: sentence-transformers/paraphrase-mpnet-base-v2 datasets: - dendimaki/v1 metrics: - accuracy widget: - text: so you know you said that layer three maybe sounded interesting - text: just this like sense of energy thats aliveness and aliveness tingly aliveness - text: id say is pretty or really the dominant state unless i really focus on location one and even then - text: pervading presence - text: nonduality for you pipeline_tag: text-classification inference: true model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: dendimaki/v1 type: dendimaki/v1 split: test metrics: - type: accuracy value: 0.46352941176470586 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [dendimaki/v1](https://huggingface.co/datasets/dendimaki/v1) dataset 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:** 26 classes - **Training Dataset:** [dendimaki/v1](https://huggingface.co/datasets/dendimaki/v1) ### 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 | |:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 20 | | | 26 | | | 27 | | | 18 | | | 28 | | | 16 | | | 17 | | | 25 | | | 19 | | | 15 | | | 8 | | | 14 | | | 22 | | | 3 | | | 4 | | | 6 | | | 21 | | | 10 | | | 24 | | | 0 | | | 11 | | | 1 | | | 9 | | | 5 | | | 12 | | | 23 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.4635 | ## 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("dendimaki/fewshot-model") # Run inference preds = model("pervading presence") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 21.9052 | 247 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 2 | | 1 | 1 | | 3 | 5 | | 4 | 2 | | 5 | 4 | | 6 | 11 | | 8 | 1 | | 9 | 2 | | 10 | 1 | | 11 | 2 | | 12 | 3 | | 14 | 4 | | 15 | 8 | | 16 | 8 | | 17 | 11 | | 18 | 28 | | 19 | 25 | | 20 | 14 | | 21 | 4 | | 22 | 7 | | 23 | 2 | | 24 | 1 | | 25 | 13 | | 26 | 30 | | 27 | 36 | | 28 | 7 | ### 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0017 | 1 | 0.252 | - | | 0.0862 | 50 | 0.1891 | - | | 0.1724 | 100 | 0.1793 | - | | 0.2586 | 150 | 0.1848 | - | | 0.3448 | 200 | 0.1033 | - | | 0.4310 | 250 | 0.0473 | - | | 0.5172 | 300 | 0.1213 | - | | 0.6034 | 350 | 0.0343 | - | | 0.6897 | 400 | 0.0276 | - | | 0.7759 | 450 | 0.0262 | - | | 0.8621 | 500 | 0.0425 | - | | 0.9483 | 550 | 0.0482 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.40.1 - PyTorch: 2.2.1+cu121 - Datasets: 2.19.0 - Tokenizers: 0.19.1 ## 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} } ```