--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 'Wonderful person aboard!' - text: jan o lukin ala e pilin sina. - text: Nothing…I’m just loudly complaining, I’ll get over it tomorrow. - text: HEY THERE - text: Pizza cutter 2 metrics: - accuracy pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 1.0 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 128 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 | |:----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | toki pona | | | other | | ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 1.0 | ## 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("johnpaulbin/toki-pona-classifier-v2") # Run inference preds = model(["Hello!", "toki!"]) ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 10.5705 | 61 | | Label | Training Sample Count | |:----------|:----------------------| | other | 2035 | | toki pona | 2000 | ### Training Hyperparameters - batch_size: (12, 12) - num_epochs: (2, 2) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 1 - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - l2_weight: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0015 | 1 | 0.3252 | - | | 0.0743 | 50 | 0.2704 | - | | 0.1486 | 100 | 0.2257 | - | | 0.2229 | 150 | 0.0567 | - | | 0.2972 | 200 | 0.0063 | - | | 0.3715 | 250 | 0.0015 | - | | 0.4458 | 300 | 0.0034 | - | | 0.5201 | 350 | 0.0026 | - | | 0.5944 | 400 | 0.0036 | - | | 0.6686 | 450 | 0.0005 | - | | 0.7429 | 500 | 0.0021 | - | | 0.8172 | 550 | 0.0021 | - | | 0.8915 | 600 | 0.0003 | - | | 0.9658 | 650 | 0.0002 | - | | 1.0401 | 700 | 0.0002 | - | | 1.1144 | 750 | 0.0018 | - | | 1.1887 | 800 | 0.0003 | - | | 1.2630 | 850 | 0.0002 | - | | 1.3373 | 900 | 0.0001 | - | | 1.4116 | 950 | 0.0015 | - | | 1.4859 | 1000 | 0.0004 | - | | 1.5602 | 1050 | 0.0001 | - | | 1.6345 | 1100 | 0.0001 | - | | 1.7088 | 1150 | 0.0019 | - | | 1.7831 | 1200 | 0.0001 | - | | 1.8574 | 1250 | 0.0001 | - | | 1.9316 | 1300 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0 - Sentence Transformers: 3.2.1 - Transformers: 4.42.2 - PyTorch: 2.5.1+cu121 - Datasets: 3.1.0 - 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} } ```