README.md

---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Approved PRNG initial seeds and seed keys used for initialization are securely
    stored in flash memory.
- text: ANSI X9.31 PRNG key Triple DES key Generated internally by non-approved RNG
    Volatile memory only (plaintext) Zeroized when the module reboots.
- text: PRNG ANSI X9.31 Key K1, K2 Internal 3DES Key Automatically Generated per seeding
    This is an internal key used for ANSI X9.31 192 bits Internal Key generate from
    the seed and seed key
- text: PRNG Seed Key A new ANSI X9.31 RNG Seed Key is generated from a block of 160
    bits output by the random noise source software library.
- text: FIPS-Approved random number generation ANSI X9.31 PRNG Key AES 128-bit key
    Hard-coded in the module.
inference: true
---

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

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) 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](https://www.sbert.net) 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](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
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### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label    | Examples                                                                                                                                                                                                                                                                                                                                                  |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| negative | <ul><li>'X Seed Key for RNG: Seed created by NDRNG and used as the Triple DES key in the ANSI X9.31 RNG.'</li><li>'The ANSI X9.31 Seed key is held in volatile system memory and destroyed on power cycle.'</li><li>'ANSI X9.31 PRNG key AES-128 Generated internally by the Kernel.'</li></ul>                                                           |
| positive | <ul><li>'The private key component of an ANSI X9.31-compliant PRNG is stored securely in NVRAM.'</li><li>'Secure storage of the RNG seed key in tamper-resistant hardware ensures reliable random number generation.'</li><li>'A FIPS-approved RNG utilizes an ANSI X9.31 PRNG key with an AES 128-bit key that is hard-coded into the module.'</li></ul> |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
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("yasirdemircan/setfit_rng_v5")
# Run inference
preds = model("FIPS-Approved random number generation ANSI X9.31 PRNG Key AES 128-bit key Hard-coded in the module.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 19.1778 | 53  |

| Label    | Training Sample Count |
|:---------|:----------------------|
| negative | 22                    |
| positive | 23                    |

### 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0294 | 1    | 0.2337        | -               |
| 1.0    | 34   | -             | 0.1687          |
| 1.4706 | 50   | 0.1015        | -               |
| 2.0    | 68   | -             | 0.1004          |
| 2.9412 | 100  | 0.0006        | -               |
| 3.0    | 102  | -             | 0.1020          |
| 4.0    | 136  | -             | 0.1021          |

### 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
```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}
}
```

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