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--- |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: Remote work is allowed provided the candidate has adequate home systems to |
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support the high internet data demands required for this position |
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- text: · High School degree required, though we will consider candidates with equivalent |
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education or experience · Experience and verifiable competence in building systems |
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including HVAC, steam, gas, electrical, plumbing, repair work and/or equivalent |
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training are required |
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- text: 'Beyond a light and engaging work environment, team members receive the following |
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benefits: Competitive salary PPO, HSA, and life insurance options 401k plan Open |
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vacation policy (discretionary time-off) DIY schedule for balancing personal and |
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professional responsibilities Equipment and tools for you to do your job Tracker |
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is an equal opportunity employer' |
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- text: Job descriptionA leading real estate firm in New Jersey is seeking an administrative |
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Marketing Coordinator with some experience in graphic design |
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- text: QualificationsPortfolio of published articles (electronic and print)Excellent |
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writing and editing skills in EnglishEvidence of collaboration with clients and |
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within an office environmentHands-on experience with MailChimp, WordPress, SEO |
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tools, Microsoft Suite, and social mediaFamiliarity with web publicationsPhotography |
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skills preferred |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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library_name: setfit |
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inference: true |
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base_model: sentence-transformers/paraphrase-mpnet-base-v2 |
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--- |
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# SetFit with sentence-transformers/paraphrase-mpnet-base-v2 |
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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. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 512 tokens |
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- **Number of Classes:** 2 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| skip | <ul><li>'Additional tasks may be assigned based on organizationalneeds and priorities.Culture:At the Kids’ Book Bank, we are a small but mighty team dedicated to getting more books to more children and fostering a love of reading'</li><li>'Founded in Sweden in 1907, today SKF is publicly traded on the Nasdaq Stockholm with annual sales in 2020 of approximately $10 billion'</li><li>'Compensation:$55,000-$75,000/year'</li></ul> | |
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| keep | <ul><li>'Requirements Must have at least 2 years Arizona or Colorado civil litigation experience, knowledge of both state and federal procedural rules, superior organizational skills, strong attention to detail and the ability to provide secretarial/administrative support to experienced trial attorneys'</li><li>'Experience with EMR systems Knowledge of Microsoft products (Word, Excel, Outlook)'</li><li>"Requirements:A bachelor's degree in exercise science, kinesiology, sports science, or a related field preferred but not required"</li></ul> | |
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## Uses |
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### Direct Use for Inference |
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First install the SetFit library: |
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```bash |
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pip install setfit |
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``` |
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Then you can load this model and run inference. |
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```python |
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from setfit import SetFitModel |
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("setfit_model_id") |
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# Run inference |
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preds = model("Remote work is allowed provided the candidate has adequate home systems to support the high internet data demands required for this position") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:-------------|:----|:--------|:----| |
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| Word count | 1 | 24.1078 | 84 | |
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| Label | Training Sample Count | |
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|:------|:----------------------| |
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| skip | 52 | |
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| keep | 50 | |
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### Training Hyperparameters |
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- batch_size: (16, 16) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- num_iterations: 20 |
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- body_learning_rate: (2e-05, 2e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
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|:------:|:----:|:-------------:|:---------------:| |
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| 0.0039 | 1 | 0.3866 | - | |
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| 0.1961 | 50 | 0.2075 | - | |
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| 0.3922 | 100 | 0.0179 | - | |
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| 0.5882 | 150 | 0.0005 | - | |
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| 0.7843 | 200 | 0.0003 | - | |
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| 0.9804 | 250 | 0.0002 | - | |
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| 1.0 | 255 | - | 0.1885 | |
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| 1.1765 | 300 | 0.0002 | - | |
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| 1.3725 | 350 | 0.0001 | - | |
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| 1.5686 | 400 | 0.0001 | - | |
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| 1.7647 | 450 | 0.0001 | - | |
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| 1.9608 | 500 | 0.0001 | - | |
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| 2.0 | 510 | - | 0.1909 | |
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| 2.1569 | 550 | 0.0001 | - | |
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| 2.3529 | 600 | 0.0001 | - | |
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| 2.5490 | 650 | 0.0001 | - | |
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| 2.7451 | 700 | 0.0001 | - | |
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| 2.9412 | 750 | 0.0001 | - | |
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| 3.0 | 765 | - | 0.1904 | |
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| 3.1373 | 800 | 0.0001 | - | |
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| 3.3333 | 850 | 0.0001 | - | |
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| 3.5294 | 900 | 0.0001 | - | |
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| 3.7255 | 950 | 0.0001 | - | |
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| 3.9216 | 1000 | 0.0001 | - | |
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| 4.0 | 1020 | - | 0.1910 | |
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### Framework Versions |
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- Python: 3.12.3 |
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- SetFit: 1.1.3 |
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- Sentence Transformers: 5.2.0 |
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- Transformers: 4.57.3 |
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- PyTorch: 2.5.1+cu124 |
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- Datasets: 4.4.2 |
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- Tokenizers: 0.22.2 |
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## Citation |
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### BibTeX |
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```bibtex |
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@article{https://doi.org/10.48550/arxiv.2209.11055, |
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doi = {10.48550/ARXIV.2209.11055}, |
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url = {https://arxiv.org/abs/2209.11055}, |
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author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Efficient Few-Shot Learning Without Prompts}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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