---
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: The NDRNG is used to generate seed & seed key values to feed the DRNG.
- text: With the exception of DHSK and the RNG seed, all CSPs are loaded at factory.
- text: ANSI X9.31 PRNG seed key AES 256-bit key Initialized by the module from an
    external NDRNG Never Plaintext in volatile memory Power cycle or host reboot Generate
    random number and resides in volatile memory in plaintext.
- text: The ANSI X9.31 RNG is seeded using a 128-bit AES seed key generated external
    to the module.
- text: A FIPS-approved RNG utilizes an ANSI X9.31 PRNG key with an AES 128-bit key
    that is hard-coded into 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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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                                                                                                                                                                                                                                                                                                                                                                                                                    |
|:---------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| positive | <ul><li>'A 256-bit seed key for the ANSI X9.31 RNG function using AES-256 is stored in plaintext in RAM, generated securely at the factory, and embedded in flash memory.'</li><li>'The ANSI X9.31 RNG is initialized using a 128-bit AES seed key generated outside the module.'</li><li>'PRNG compliant to ANSI X9.31 private key portion stored in secured NVRAM.'</li></ul>                                             |
| negative | <ul><li>'ANSI X9.31 RNG Seed Key 168-bit TDES keys/ 256-bit AES key Generated by the TRNG.'</li><li>'128 bits Random Number Key Key value is used by the random number generator. RTC-RAM Zeroize CSPs service.'</li><li>'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'</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_v2")
# Run inference
preds = model("The NDRNG is used to generate seed & seed key values to feed the DRNG.")
```

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## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 19.8667 | 59  |

| 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 |
|:------:|:----:|:-------------:|:---------------:|
| 0.0149 | 1    | 0.2065        | -               |
| 0.7463 | 50   | 0.1335        | -               |
| 1.0    | 67   | -             | 0.2640          |
| 1.4925 | 100  | 0.0016        | -               |
| 2.0    | 134  | -             | 0.2100          |
| 2.2388 | 150  | 0.0003        | -               |
| 2.9851 | 200  | 0.0002        | -               |
| 3.0    | 201  | -             | 0.2100          |
| 3.7313 | 250  | 0.0002        | -               |
| 4.0    | 268  | -             | 0.2095          |

### 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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