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: All critical security parameters are injected into the system during production.
- text: 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.
- text: Random number generator obtains its seed key by reading bytes from the /dev/urandom
    device. The seed key is stored in SDRAM in plaintext while in use and is deleted
    from memory on power-down, reboot, or any command that is followed by a reboot,
    such as switching between non-approved and approved modes, zeroization, restore
    factory settings, and reset shared key.
- text: X9.31 PRNG seed keys Triple-DES (112 bit) Generated by gathering entropy.
- text: 'X Seed Key for RNG: Seed created by NDRNG and used as the Triple DES key
    in the ANSI X9.31 RNG.'
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>'The private key component of an ANSI X9.31-compliant PRNG is stored securely in NVRAM.'</li><li>'It is generated in the factory (a secure environment) using the hardware RNG Embedded in FLASH.'</li><li>'The internal DRBG state value of the RNG is stored in NVRAM for persistent use.'</li></ul> |
| negative | <ul><li>'The NDRNG is used to generate seed & seed key values to feed the DRNG.'</li><li>'module stores RNG and DRBG state values only in RAM.'</li><li>'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.'</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_v6")
# Run inference
preds = model("X9.31 PRNG seed keys Triple-DES (112 bit) Generated by gathering entropy.")
```

<!--
### Downstream Use

*List how someone could finetune this model on their own dataset.*
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set | Min | Median  | Max |
|:-------------|:----|:--------|:----|
| Word count   | 6   | 18.8889 | 49  |

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

### 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    | 0.2114        | -               |
| 1.0    | 34   | -             | 0.0933          |
| 1.4706 | 50   | 0.1015        | -               |
| 2.0    | 68   | -             | 0.0967          |
| 2.9412 | 100  | 0.0008        | -               |
| 3.0    | 102  | -             | 0.1039          |
| 4.0    | 136  | -             | 0.1055          |

### Framework Versions
- Python: 3.10.16
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Datasets: 3.2.0
- Tokenizers: 0.20.3

## 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->