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

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/all-mpnet-base-v2 as the Sentence Transformer embedding model. A LogisticRegression 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/all-mpnet-base-v2
Classification head: a LogisticRegression instance
Maximum Sequence Length: 384 tokens
Number of Classes: 2 classes

Model Sources

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

Model Labels

Label	Examples
negative	'The seed key is not stored at all, but is generated on demand and immediately zeroized after use.' '128 bits Random Number Key Key value is used by the random number generator. RTC-RAM Zeroize CSPs service.' 'X Seed Key for RNG: Seed created by NDRNG and used as the Triple DES key in the ANSI X9.31 RNG.'
positive	'PRNG seed key is static during the lifetime of the module.' 'A FIPS-approved RNG utilizes an ANSI X9.31 PRNG key with an AES 128-bit key that is hard-coded into the module.' 'Approved PRNG initial seed and seed key used to initialize approved PRNG is stored in flash.'

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("yasirdemircan/setfit_rng_v4")
# Run inference
preds = model("X9.31 PRNG is seeded with urandom.")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Word count	10	19.6667	59

Label	Training Sample Count
negative	21
positive	24

Training Hyperparameters

batch_size: (16, 16)
num_epochs: (4, 4)
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: True

Training Results

Epoch	Step	Training Loss	Validation Loss
0.0149	1	0.2273	-
0.7463	50	0.1704	-
1.0	67	-	0.1468
1.4925	100	0.002	-
2.0	134	-	0.1621
2.2388	150	0.0004	-
2.9851	200	0.0003	-
3.0	201	-	0.1657
3.7313	250	0.0002	-
4.0	268	-	0.1665

Framework Versions

Python: 3.10.15
SetFit: 1.2.0.dev0
Sentence Transformers: 3.3.1
Transformers: 4.45.2
PyTorch: 2.5.1+cu124
Datasets: 2.19.1
Tokenizers: 0.20.1

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}
}

yasirdemircan
/

setfit_rng_v4