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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Framing shop providing custom picture framing and art preservation services.
We frame artwork, photographs, and memorabilia with quality materials.
- text: Lymph node pathology reveals metastatic adenocarcinoma with extracapsular
extension. Immunostains are consistent with breast primary tumor origin.
- text: Fluoroscopy-guided placement of central venous catheter for chemotherapy administration.
Port placement performed successfully with proper tip positioning confirmed.
- text: Immunotherapy harnesses the body's immune system to fight cancer. Checkpoint
inhibitors block proteins like PD-1 and CTLA-4 that normally prevent immune cells
from attacking cancer cells, while CAR-T therapy genetically modifies T cells
to better recognize cancer.
- text: Endometrial biopsy shows endometrioid adenocarcinoma, grade 2, with squamous
differentiation. Deep myometrial invasion is present involving the outer half.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: TimKond/S-PubMedBert-MedQuAD
model-index:
- name: SetFit with TimKond/S-PubMedBert-MedQuAD
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 TimKond/S-PubMedBert-MedQuAD
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [TimKond/S-PubMedBert-MedQuAD](https://huggingface.co/TimKond/S-PubMedBert-MedQuAD) 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:** [TimKond/S-PubMedBert-MedQuAD](https://huggingface.co/TimKond/S-PubMedBert-MedQuAD)
- **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
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### 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 |
|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| non_oncology | <ul><li>'Food truck festival coordination service organizing mobile vendor events, permit management, and location logistics.'</li><li>'Traditional Japanese tea ceremony instruction teaching the ancient art of chanoyu. Cultural immersion program exploring Japanese aesthetics, philosophy, and mindfulness practices.'</li><li>'Yoga studio offering various classes including Hatha, Vinyasa, and restorative yoga. We provide a peaceful environment for mindfulness and physical wellness.'</li></ul> |
| oncology | <ul><li>'Combination therapy uses multiple treatment modalities to improve outcomes. For example, neoadjuvant chemotherapy can shrink tumors before surgery, while adjuvant therapy eliminates remaining cancer cells after primary treatment.'</li><li>'Hereditary cancer syndromes account for 5-10% of all cancers. BRCA1 and BRCA2 mutations significantly increase breast and ovarian cancer risk, while Lynch syndrome increases colorectal and endometrial cancer risk.'</li><li>'Endoplasmic Reticulum: The cisternae of the endoplasmic reticulum are distended by fluid in hydropic swelling. In other forms of acute, reversible cell injury, membrane-bound polysomes may undergo disaggregation and detach from the surface of the rough endoplasmic reticulum.'</li></ul> |
## 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("setfit_model_id")
# Run inference
preds = model("Lymph node pathology reveals metastatic adenocarcinoma with extracapsular extension. Immunostains are consistent with breast primary tumor origin.")
```
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 12 | 31.1543 | 929 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| non_oncology | 293 |
| oncology | 180 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: 200
- sampling_strategy: oversampling
- 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 |
|:-----:|:----:|:-------------:|:---------------:|
| 0.005 | 1 | 0.1922 | - |
| 0.25 | 50 | 0.0766 | - |
| 0.5 | 100 | 0.0081 | - |
| 0.75 | 150 | 0.0058 | - |
| 1.0 | 200 | 0.0005 | 0.0074 |
### Framework Versions
- Python: 3.11.10
- SetFit: 1.1.2
- Sentence Transformers: 5.0.0
- Transformers: 4.49.0
- PyTorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}
}
```
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