BarryRL/test_rad_bert

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}
}