--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: is completely right on this. carnildo’s comment is just a waste of space. 176.12.107.140 - text: '" please do not vandalize pages, as you did with this edit to bella swan. if you continue to do so, you will be blocked from editing. (talk) "' - text: ipv6 mirc doesn't natively supports ipv6 protocols. it could be enabled by adding a external dll plugin who will enable a special protocol for dns and connecting to ipv6 servers. - text: '" link thanks for fixing that disambiguation link on usher''s album ) flash; "' - text: '|b-class-1= yes |b-class-2= yes |b-class-3= yes |b-class-4= yes |b-class-5= yes' pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 --- # 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 | | | 1 | | ## 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("waterabbit114/my-setfit-classifier_obscene") # Run inference preds = model("\" link thanks for fixing that disambiguation link on usher's album ) flash; \"") ``` ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 57.2 | 426 | | Label | Training Sample Count | |:------|:----------------------| | 0 | 10 | | 1 | 10 | ### Training Hyperparameters - batch_size: (1, 1) - num_epochs: (10, 10) - 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 | |:------:|:----:|:-------------:|:---------------:| | 0.0013 | 1 | 0.1758 | - | | 0.0625 | 50 | 0.0036 | - | | 0.125 | 100 | 0.1383 | - | | 0.1875 | 150 | 0.0148 | - | | 0.25 | 200 | 0.0216 | - | | 0.3125 | 250 | 0.0001 | - | | 0.375 | 300 | 0.0021 | - | | 0.4375 | 350 | 0.001 | - | | 0.5 | 400 | 0.0015 | - | | 0.5625 | 450 | 0.0004 | - | | 0.625 | 500 | 0.0 | - | | 0.6875 | 550 | 0.0003 | - | | 0.75 | 600 | 0.0 | - | | 0.8125 | 650 | 0.0 | - | | 0.875 | 700 | 0.0 | - | | 0.9375 | 750 | 0.0001 | - | | 1.0 | 800 | 0.0 | - | | 1.0625 | 850 | 0.0 | - | | 1.125 | 900 | 0.0002 | - | | 1.1875 | 950 | 0.0 | - | | 1.25 | 1000 | 0.0008 | - | | 1.3125 | 1050 | 0.0002 | - | | 1.375 | 1100 | 0.0 | - | | 1.4375 | 1150 | 0.0 | - | | 1.5 | 1200 | 0.0 | - | | 1.5625 | 1250 | 0.0001 | - | | 1.625 | 1300 | 0.0 | - | | 1.6875 | 1350 | 0.0 | - | | 1.75 | 1400 | 0.0 | - | | 1.8125 | 1450 | 0.0 | - | | 1.875 | 1500 | 0.0 | - | | 1.9375 | 1550 | 0.0 | - | | 2.0 | 1600 | 0.0 | - | | 2.0625 | 1650 | 0.0001 | - | | 2.125 | 1700 | 0.0001 | - | | 2.1875 | 1750 | 0.0 | - | | 2.25 | 1800 | 0.0001 | - | | 2.3125 | 1850 | 0.0001 | - | | 2.375 | 1900 | 0.0002 | - | | 2.4375 | 1950 | 0.0 | - | | 2.5 | 2000 | 0.0001 | - | | 2.5625 | 2050 | 0.0001 | - | | 2.625 | 2100 | 0.0 | - | | 2.6875 | 2150 | 0.0001 | - | | 2.75 | 2200 | 0.0003 | - | | 2.8125 | 2250 | 0.0001 | - | | 2.875 | 2300 | 0.0 | - | | 2.9375 | 2350 | 0.0 | - | | 3.0 | 2400 | 0.0003 | - | | 3.0625 | 2450 | 0.0 | - | | 3.125 | 2500 | 0.0 | - | | 3.1875 | 2550 | 0.0 | - | | 3.25 | 2600 | 0.0 | - | | 3.3125 | 2650 | 0.0 | - | | 3.375 | 2700 | 0.0001 | - | | 3.4375 | 2750 | 0.0 | - | | 3.5 | 2800 | 0.0 | - | | 3.5625 | 2850 | 0.0 | - | | 3.625 | 2900 | 0.0001 | - | | 3.6875 | 2950 | 0.0 | - | | 3.75 | 3000 | 0.0001 | - | | 3.8125 | 3050 | 0.0 | - | | 3.875 | 3100 | 0.0 | - | | 3.9375 | 3150 | 0.0 | - | | 4.0 | 3200 | 0.0 | - | | 4.0625 | 3250 | 0.0 | - | | 4.125 | 3300 | 0.0 | - | | 4.1875 | 3350 | 0.0 | - | | 4.25 | 3400 | 0.0 | - | | 4.3125 | 3450 | 0.0 | - | | 4.375 | 3500 | 0.0001 | - | | 4.4375 | 3550 | 0.0001 | - | | 4.5 | 3600 | 0.0 | - | | 4.5625 | 3650 | 0.0 | - | | 4.625 | 3700 | 0.0 | - | | 4.6875 | 3750 | 0.0 | - | | 4.75 | 3800 | 0.0001 | - | | 4.8125 | 3850 | 0.0 | - | | 4.875 | 3900 | 0.0 | - | | 4.9375 | 3950 | 0.0 | - | | 5.0 | 4000 | 0.0 | - | | 5.0625 | 4050 | 0.0 | - | | 5.125 | 4100 | 0.0 | - | | 5.1875 | 4150 | 0.0 | - | | 5.25 | 4200 | 0.0 | - | | 5.3125 | 4250 | 0.0 | - | | 5.375 | 4300 | 0.0001 | - | | 5.4375 | 4350 | 0.0 | - | | 5.5 | 4400 | 0.0 | - | | 5.5625 | 4450 | 0.0 | - | | 5.625 | 4500 | 0.0 | - | | 5.6875 | 4550 | 0.0 | - | | 5.75 | 4600 | 0.0 | - | | 5.8125 | 4650 | 0.0 | - | | 5.875 | 4700 | 0.0 | - | | 5.9375 | 4750 | 0.0 | - | | 6.0 | 4800 | 0.0 | - | | 6.0625 | 4850 | 0.0 | - | | 6.125 | 4900 | 0.0 | - | | 6.1875 | 4950 | 0.0 | - | | 6.25 | 5000 | 0.0 | - | | 6.3125 | 5050 | 0.0 | - | | 6.375 | 5100 | 0.0 | - | | 6.4375 | 5150 | 0.0001 | - | | 6.5 | 5200 | 0.0 | - | | 6.5625 | 5250 | 0.0 | - | | 6.625 | 5300 | 0.0 | - | | 6.6875 | 5350 | 0.0 | - | | 6.75 | 5400 | 0.0 | - | | 6.8125 | 5450 | 0.0 | - | | 6.875 | 5500 | 0.0 | - | | 6.9375 | 5550 | 0.0 | - | | 7.0 | 5600 | 0.0001 | - | | 7.0625 | 5650 | 0.0 | - | | 7.125 | 5700 | 0.0 | - | | 7.1875 | 5750 | 0.0 | - | | 7.25 | 5800 | 0.0 | - | | 7.3125 | 5850 | 0.0 | - | | 7.375 | 5900 | 0.0001 | - | | 7.4375 | 5950 | 0.0 | - | | 7.5 | 6000 | 0.0 | - | | 7.5625 | 6050 | 0.0 | - | | 7.625 | 6100 | 0.0 | - | | 7.6875 | 6150 | 0.0 | - | | 7.75 | 6200 | 0.0 | - | | 7.8125 | 6250 | 0.0 | - | | 7.875 | 6300 | 0.0 | - | | 7.9375 | 6350 | 0.0 | - | | 8.0 | 6400 | 0.0 | - | | 8.0625 | 6450 | 0.0 | - | | 8.125 | 6500 | 0.0 | - | | 8.1875 | 6550 | 0.0 | - | | 8.25 | 6600 | 0.0 | - | | 8.3125 | 6650 | 0.0 | - | | 8.375 | 6700 | 0.0 | - | | 8.4375 | 6750 | 0.0 | - | | 8.5 | 6800 | 0.0 | - | | 8.5625 | 6850 | 0.0 | - | | 8.625 | 6900 | 0.0 | - | | 8.6875 | 6950 | 0.0 | - | | 8.75 | 7000 | 0.0 | - | | 8.8125 | 7050 | 0.0 | - | | 8.875 | 7100 | 0.0 | - | | 8.9375 | 7150 | 0.0 | - | | 9.0 | 7200 | 0.0 | - | | 9.0625 | 7250 | 0.0 | - | | 9.125 | 7300 | 0.0 | - | | 9.1875 | 7350 | 0.0 | - | | 9.25 | 7400 | 0.0 | - | | 9.3125 | 7450 | 0.0 | - | | 9.375 | 7500 | 0.0 | - | | 9.4375 | 7550 | 0.0 | - | | 9.5 | 7600 | 0.0 | - | | 9.5625 | 7650 | 0.0 | - | | 9.625 | 7700 | 0.0 | - | | 9.6875 | 7750 | 0.0 | - | | 9.75 | 7800 | 0.0 | - | | 9.8125 | 7850 | 0.0 | - | | 9.875 | 7900 | 0.0 | - | | 9.9375 | 7950 | 0.0 | - | | 10.0 | 8000 | 0.0 | - | ### Framework Versions - Python: 3.11.7 - SetFit: 1.0.3 - Sentence Transformers: 2.2.2 - Transformers: 4.35.2 - PyTorch: 2.1.1+cu121 - Datasets: 2.14.5 - Tokenizers: 0.15.1 ## 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} } ```