README.md

metadata

tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Multiple non-calcified nodules in left lower lobe, ranging from 3-6mm.
      Follow-up CT scan recommended.
  - text: >-
      Heart size and mediastinal contours normal. Lungs clear, no consolidation
      or acute abnormality. No pleural effusion or pneumothorax. No acute
      osseous abnormalities. Small 0.5 cm calcified granuloma in right upper
      lobe, likely benign from prior infection. No acute cardiopulmonary
      disease. Incidental right upper lobe granuloma. No follow-up needed unless
      clinically indicated. nan
  - text: >-
      Patchy consolidation in RLL consistent with pneumonia. Recommend clinical
      follow-up.
  - text: >-
      Calcified granuloma in the left lower lobe, incidental finding.  Findings
      consistent with COPD exacerbation.
  - text: >-
      Moderate pleural effusion and patchy consolidation in bilateral lung
      bases.
metrics:
  - accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false

SetFit

This is a SetFit model that can be used for Text Classification. A OneVsRestClassifier 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 with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

• Model Type: SetFit
• Classification head: a OneVsRestClassifier instance
• Maximum Sequence Length: 512 tokens

Model Sources

• Repository: SetFit on GitHub
• Paper: Efficient Few-Shot Learning Without Prompts
• Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Moderate pleural effusion and patchy consolidation in bilateral lung bases.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	7	17.5143	53

Training Hyperparameters

• batch_size: (8, 8)
• num_epochs: (10, 10)
• max_steps: -1
• 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
0.0096	1	0.1417	-
0.4808	50	0.1824	-
0.9615	100	0.1083	-
1.4423	150	0.0817	-
1.9231	200	0.0777	-
2.4038	250	0.0636	-
2.8846	300	0.0649	-
3.3654	350	0.0603	-
3.8462	400	0.0713	-
4.3269	450	0.0507	-
4.8077	500	0.0569	-
5.2885	550	0.0553	-
5.7692	600	0.0614	-
6.25	650	0.0512	-
6.7308	700	0.0559	-
7.2115	750	0.0512	-
7.6923	800	0.0464	-
8.1731	850	0.0547	-
8.6538	900	0.0455	-
9.1346	950	0.0524	-
9.6154	1000	0.0526	-

Framework Versions

• Python: 3.11.11
• SetFit: 1.1.2
• Sentence Transformers: 4.0.2
• Transformers: 4.51.2
• PyTorch: 2.6.0
• Datasets: 3.5.0
• Tokenizers: 0.21.1

Citation

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}
}