--- tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: We are awaiting payment for the project completed in June. Please confirm when this will be processed. - text: Hello, Good morning, would you mind cancelling this rental car? - text: 'Kindly book accommodation for Lindelani Mkhize as follows: Establishment: City Lodge Lynwood Date checked in : 04 October 2023 Time checked in: 19h00pm Date checked out: 06 October 2023 Time checked out: 07h00am' - text: You've been selected for a free energy audit. Click here to schedule your appointment. - text: 'Please can you provide with the invoices for my stays this month as follows: 1. Premier Splendid Inn Bayshore (07 Aug - 08 Aug) 2. Port Nolloth Beach Shack (14 Aug - 17 Aug)' metrics: - silhouette_score pipeline_tag: text-classification library_name: setfit inference: true base_model: sentence-transformers/paraphrase-MiniLM-L6-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: silhouette_score value: 0.4196937375508804 name: Silhouette_Score --- # SetFit with sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-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:** 14 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 | | | 1 | | | 2 | | | 3 | | | 4 | | | 5 | | | 6 | | | 7 | | | 8 | | | 9 | | | 10 | | | 11 | | | 12 | | | 13 | | ## Evaluation ### Metrics | Label | Silhouette_Score | |:--------|:-----------------| | **all** | 0.4197 | ## 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("mann2107/BCMPIIRAB_MiniLM_HTTest") # Run inference preds = model("Hello, Good morning, would you mind cancelling this rental car?") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 1 | 25.6577 | 136 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 24 | | 1 | 24 | | 2 | 24 | | 3 | 24 | | 4 | 24 | | 5 | 24 | | 6 | 24 | | 7 | 24 | | 8 | 24 | | 9 | 24 | | 10 | 24 | | 11 | 24 | | 12 | 24 | | 13 | 24 | ### Training Hyperparameters - batch_size: (32, 32) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 1 - body_learning_rate: (3e-05, 3e-05) - head_learning_rate: 3e-05 - loss: MultipleNegativesRankingLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: True - 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0476 | 1 | 5.5459 | - | ### Framework Versions - Python: 3.12.0 - SetFit: 1.2.0.dev0 - Sentence Transformers: 3.2.1 - Transformers: 4.45.2 - PyTorch: 2.5.0+cpu - Datasets: 3.0.2 - Tokenizers: 0.20.1 ## 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} } ```