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leavoigt/
vulnerability_multilabel_v2

Name: vulnerability_multilabel_v2
Availability: InStock

setfit

safetensors

xlm-roberta

sentence-transformers

text-classification

generated_from_setfit_trainer

2209.11055

sentence-transformers/paraphrase-multilingual-mpnet-base-v2

finetune

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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-multilingual-mpnet-base-v2 as the Sentence Transformer embedding model. A SetFitHead instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

Fine-tuning a Sentence Transformer with contrastive learning.
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-multilingual-mpnet-base-v2
Classification head: a SetFitHead instance
Maximum Sequence Length: 128 tokens
Number of Classes: 18 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.

python

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("leavoigt/vulnerability_multilabel_v2")
# Run inference
preds = model("Seek to increase urban resilience by developing master plans for rainwater drainage, improving and extending drainage infrastructure, and implementing flood management systems in vulnerable areas. Adaptive capacity of agro- silvo- pastoral production and promotion of blue economy.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	1	61.3809	164

Training Hyperparameters

batch_size: (16, 2)
num_epochs: (1, 0)
max_steps: -1
sampling_strategy: undersampling
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: 0.01
seed: 42
eval_max_steps: -1
load_best_model_at_end: False

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0002	1	0.25	-
0.2084	1000	0.0461	0.1223
0.4168	2000	0.0169	0.1294
0.6251	3000	0.032	0.121
0.8335	4000	0.023	0.1172

Framework Versions

Python: 3.10.12
SetFit: 1.0.3
Sentence Transformers: 3.0.1
Transformers: 4.36.2
PyTorch: 2.4.0+cu121
Datasets: 2.10.0
Tokenizers: 0.15.2

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}
}