peter2000/setfit-vulnerability-groups

SetFit with sentence-transformers/paraphrase-mpnet-base-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-mpnet-base-v2 as the Sentence Transformer embedding model. 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
• Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2
• Classification head: a OneVsRestClassifier instance
• Maximum Sequence Length: 256 tokens
• Number of Classes: 17 classes

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("peter2000/setfit-vulnerability-groups")
# Run inference
preds = model("The infrastructure requirement for collection based on the targets and projections made is presented in Table 12.22. A total of about 149,000 km length and 8,660km length of sewers are required for urban and rural communities, respectively by 2047. In addition, a little over 4 million facilities in urban areas and about 853,000 facilities in rural areas will be required to meet on-site sanitation needs by 2033 nationwide.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	15	71.2316	164

Training Hyperparameters

• batch_size: (16, 16)
• num_epochs: (1, 1)
• max_steps: -1
• sampling_strategy: oversampling
• num_iterations: 20
• 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
0.0011	1	0.3006	-
0.0526	50	0.2232	-
0.1053	100	0.1670	-
0.1579	150	0.1202	-
0.2105	200	0.0935	-
0.2632	250	0.0862	-
0.3158	300	0.0626	-
0.3684	350	0.0664	-
0.4211	400	0.0555	-
0.4737	450	0.0528	-
0.5263	500	0.0543	-
0.5789	550	0.0501	-
0.6316	600	0.0535	-
0.6842	650	0.0465	-
0.7368	700	0.0468	-
0.7895	750	0.0470	-
0.8421	800	0.0421	-
0.8947	850	0.0379	-
0.9474	900	0.0475	-
1.0	950	0.0449	-

Framework Versions

• Python: 3.12.12
• SetFit: 1.2.0
• Sentence Transformers: 6.1.0
• Transformers: 5.17.0
• PyTorch: 2.14.0+cu130
• Datasets: 5.0.1
• Tokenizers: 0.23.2

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}
}