| --- |
| library_name: setfit |
| metrics: |
| - accuracy |
| pipeline_tag: text-classification |
| tags: |
| - setfit |
| - sentence-transformers |
| - text-classification |
| - generated_from_setfit_trainer |
| widget: |
| - text: Now that the baffling, elongated, hyperreal coronation has occurred—no, not |
| that one—and Liz Truss has become Prime Minister, a degree of intervention and |
| action on energy bills has emerged, ahead of the looming socioeconomic catastrophe |
| facing the country this winter. |
| - text: But it needs to go much further. |
| - text: What could possibly go wrong? |
| - text: If you are White you might feel bad about hurting others or you might feel |
| afraid to lose this privilege….Overcoming White privilege is a job that must start |
| with the White community…. |
| - text: '[JF: Obviously, immigration wasn’t stopped: the current population of the |
| United States is 329.5 million—it passed 300 million in 2006.' |
| inference: true |
| --- |
| |
| # SetFit |
|
|
| This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A RandomForestClassifier 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:** [Unknown](https://huggingface.co/unknown) --> |
| - **Classification head:** a RandomForestClassifier instance |
| - **Maximum Sequence Length:** 384 tokens |
| - **Number of Classes:** 2 classes |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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 | |
| |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | 1 | <ul><li>'Gone are the days when they led the world in recession-busting'</li><li>'Who so mean that he will not himself be taxed, who so mindful of wealth that he will not favor increasing the popular taxes, in aid of these defective children?'</li><li>'That state has sixty-two counties and sixty cities … In addition there are 932 towns, 507 villages, and, at the last count, 9,600 school districts … Just try to render efficient service … amid the diffused identities and inevitable jealousies of, roughly, 11,000 independent administrative officers or boards!'</li></ul> | |
| | 0 | <ul><li>'Is this a warning of what’s to come?'</li><li>'This unique set of circumstances has brought PCL back into focus as the safe haven of choice for global players seeking somewhere to stash their cash.'</li><li>'Socialists believe that, if everyone cannot have something, no one shall.'</li></ul> | |
|
|
| ## 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("SOUMYADEEPSAR/Setfit_designed_sample_random_forest_head") |
| # Run inference |
| preds = model("What could possibly go wrong?") |
| ``` |
|
|
| <!-- |
| ### Downstream Use |
|
|
| *List how someone could finetune this model on their own dataset.* |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Set Metrics |
| | Training set | Min | Median | Max | |
| |:-------------|:----|:--------|:----| |
| | Word count | 3 | 36.5327 | 97 | |
|
|
| | Label | Training Sample Count | |
| |:------|:----------------------| |
| | 0 | 100 | |
| | 1 | 114 | |
|
|
| ### Training Hyperparameters |
| - batch_size: (8, 8) |
| - num_epochs: (1, 1) |
| - max_steps: -1 |
| - sampling_strategy: oversampling |
| - 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 | |
| |:------:|:----:|:-------------:|:---------------:| |
| | 0.0003 | 1 | 0.3958 | - | |
| | 0.0172 | 50 | 0.343 | - | |
| | 0.0345 | 100 | 0.2775 | - | |
| | 0.0517 | 150 | 0.2861 | - | |
| | 0.0689 | 200 | 0.1937 | - | |
| | 0.0861 | 250 | 0.0891 | - | |
| | 0.1034 | 300 | 0.0089 | - | |
| | 0.1206 | 350 | 0.0179 | - | |
| | 0.1378 | 400 | 0.0002 | - | |
| | 0.1551 | 450 | 0.0004 | - | |
| | 0.1723 | 500 | 0.0002 | - | |
| | 0.1895 | 550 | 0.0001 | - | |
| | 0.2068 | 600 | 0.0001 | - | |
| | 0.2240 | 650 | 0.0002 | - | |
| | 0.2412 | 700 | 0.0001 | - | |
| | 0.2584 | 750 | 0.0001 | - | |
| | 0.2757 | 800 | 0.0001 | - | |
| | 0.2929 | 850 | 0.0001 | - | |
| | 0.3101 | 900 | 0.0001 | - | |
| | 0.3274 | 950 | 0.0002 | - | |
| | 0.3446 | 1000 | 0.0 | - | |
| | 0.3618 | 1050 | 0.0001 | - | |
| | 0.3790 | 1100 | 0.0001 | - | |
| | 0.3963 | 1150 | 0.0001 | - | |
| | 0.4135 | 1200 | 0.0001 | - | |
| | 0.4307 | 1250 | 0.0001 | - | |
| | 0.4480 | 1300 | 0.0001 | - | |
| | 0.4652 | 1350 | 0.0 | - | |
| | 0.4824 | 1400 | 0.0 | - | |
| | 0.4997 | 1450 | 0.0 | - | |
| | 0.5169 | 1500 | 0.0 | - | |
| | 0.5341 | 1550 | 0.0001 | - | |
| | 0.5513 | 1600 | 0.0 | - | |
| | 0.5686 | 1650 | 0.0 | - | |
| | 0.5858 | 1700 | 0.0 | - | |
| | 0.6030 | 1750 | 0.0 | - | |
| | 0.6203 | 1800 | 0.0 | - | |
| | 0.6375 | 1850 | 0.0 | - | |
| | 0.6547 | 1900 | 0.0 | - | |
| | 0.6720 | 1950 | 0.0 | - | |
| | 0.6892 | 2000 | 0.0 | - | |
| | 0.7064 | 2050 | 0.0 | - | |
| | 0.7236 | 2100 | 0.0 | - | |
| | 0.7409 | 2150 | 0.0 | - | |
| | 0.7581 | 2200 | 0.0 | - | |
| | 0.7753 | 2250 | 0.0 | - | |
| | 0.7926 | 2300 | 0.0001 | - | |
| | 0.8098 | 2350 | 0.0001 | - | |
| | 0.8270 | 2400 | 0.0 | - | |
| | 0.8442 | 2450 | 0.0001 | - | |
| | 0.8615 | 2500 | 0.0 | - | |
| | 0.8787 | 2550 | 0.0 | - | |
| | 0.8959 | 2600 | 0.0 | - | |
| | 0.9132 | 2650 | 0.0 | - | |
| | 0.9304 | 2700 | 0.0 | - | |
| | 0.9476 | 2750 | 0.0 | - | |
| | 0.9649 | 2800 | 0.0 | - | |
| | 0.9821 | 2850 | 0.0 | - | |
| | 0.9993 | 2900 | 0.0 | - | |
|
|
| ### Framework Versions |
| - Python: 3.10.12 |
| - SetFit: 1.0.3 |
| - Sentence Transformers: 3.0.1 |
| - Transformers: 4.39.0 |
| - PyTorch: 2.3.0+cu121 |
| - Datasets: 2.20.0 |
| - Tokenizers: 0.15.2 |
|
|
| ## 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
