--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: Buses are more simple - you just buy a ticket . - text: As citizens of village , we totally care about environment of our village . - text: So , finally I suggest that it would be a great idea to combine the different types of activities , both popular and the newest . - text: Had 12 years old . - text: On the other hand , I have the theoretical knowledge to use new the technologies this great project requires . 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: 0.15543478260869564 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 8 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 | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 3 | | | 5 | | | 4 | | | 2 | | | 6 | | | 0 | | | 1 | | | 7 | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.1554 | ## 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("HelgeKn/BEA2019-multi-class-10") # Run inference preds = model("Had 12 years old .") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 3 | 21.3375 | 56 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 10 | | 1 | 10 | | 2 | 10 | | 3 | 10 | | 4 | 10 | | 5 | 10 | | 6 | 10 | | 7 | 10 | ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (2, 2) - 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 | |:-----:|:----:|:-------------:|:---------------:| | 0.005 | 1 | 0.2242 | - | | 0.25 | 50 | 0.1786 | - | | 0.5 | 100 | 0.1831 | - | | 0.75 | 150 | 0.0221 | - | | 1.0 | 200 | 0.0127 | - | | 1.25 | 250 | 0.0064 | - | | 1.5 | 300 | 0.0045 | - | | 1.75 | 350 | 0.0028 | - | | 2.0 | 400 | 0.002 | - | ### Framework Versions - Python: 3.9.13 - SetFit: 1.0.1 - Sentence Transformers: 2.2.2 - Transformers: 4.36.0 - PyTorch: 2.1.1+cpu - Datasets: 2.15.0 - Tokenizers: 0.15.0 ## 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} } ```