semvec/pypi-packages huggingface dataset:
hallucounter 1.0.0
A tool for reference-free hallucination detection in LLM-generated responses
Requires Python: >=3.9

# Installation
```
pip install hallucounter
```

# Hallucounter Usage

```
### Import

from hallucounter.detector import load_classifier, get_predictions, get_pipeline_output
from hallucounter.nli_scores import load_nli_model, get_nli_scores

### Load Classifier
clf = load_classifier('path_to_classification_model_file')

### Load NLI Cross-Encoder
nli_model, nli_tokenizer = load_nli_model() # takes optional parameter model_name. Unless specified, it uses nli-deberta-v3-large by default

### Define Query and Responses
query = "Which British athlete won the 100 m. at the 1924 Olympics?"
responses = ["Harold Abrahams.", "Harold Abrahams.","Harold Abrahams.","Harold Abrahams.","Harold Abrahams."] # Here, K=5

nli_scores = get_nli_scores(query, responses, nli_model, nli_tokenizer)

### Get the Hallucounter pipeline output
overall_prediction, confidence_score, optimal_response = get_pipeline_output(nli_scores, clf, responses)
print("Overall Prediction:", overall_prediction)
print("Optimal Response:", optimal_response)
print("Confidence Score:", confidence_score)
```
bridge-data-tools 0.1.0
A tools library
Requires Python: >=3.6

# My Library

A simple Python library for basic math operations.

## Installation

```sh
pip install my_library
```

## Usage

```python
from my_library import add, subtract

print(add(5, 3))  # Output: 8
print(subtract(5, 3))  # Output: 2
```
adversarialtorchattacks 1.0.0
A PyTorch package for adversarial attacks (FGSM, PGD, CW, MIFGSM, AutoAttack) with visualization.
Requires Python: >=3.7

## 📦 Installation
Install the package via pip:
```sh
pip install adversarialpytorchattack

## 📦 Or installation using Github 
Or install from source
```sh
git clone https://github.com/santhoshatwork17@gmail.com/adversarialpytorchattackers.git
cd adversarial_pytorch_attack


## 🛠️ Usage
```python
import torch
import torchvision.models as models
from adversarialpytorchattack.attacks import FGSM, PGD, CW
from adversarialpytorchattack.utils import visualize_perturbation, save_image

# pre-trained model
model = models.resnet18(pretrained=True).eval()

# input image
image = torch.rand((1, 3, 224, 224))  # Example input
label = torch.tensor([0])  # Example label

# Apply attack
fgsm_attack = FGSM(model, epsilon=0.03)
adv_image = fgsm_attack(image, label)

# Save the adversarial image
save_image(adv_image, "adv_fgsm.jpg")
retrvtme 0.1.0
retrvTME: Bulk RNA-seq Deconvolution with Tumor MicroEnvironment Retrieval     retrvTME is a deep learning-powered tool designed to decode bulk RNA-seq data using large-scale single-cell atlases.      The tool precisely reconstructs cell-type proportions and cell-type-specific gene expression profiles, with specialized capabilities for resolving intricate tumor microenvironment (TME) biology from bulk transcriptomes.
Requires Python: >=3.11

# retrvtme: Bulk RNA-seq Deconvolution with Tumor MicroEnvironment Retrieval

retrvTME is a deep learning-powered tool designed to decode bulk RNA-seq data using large-scale single-cell atlases. The tool precisely reconstructs cell-type proportions and cell-type-specific gene expression profiles, with specialized capabilities for resolving intricate tumor microenvironment (TME) biology from bulk transcriptomes.
chevron2021 0.0.3
Mustache templating language renderer

# THIS IS A CLONE

[Clone](https://github.com/alexberazouski/chevron) of [original chevron](https://github.com/noahmorrison/chevron) (all credits go there).
Clone was created for a single purpose of releasing a package with the latest merged changes in the original repo.
And to let other people know how to install it.

## Usage
See the [original repo](https://github.com/noahmorrison/chevron)
intelliops-sdk 0.1.0
A Python SDK for IntelliOps
Requires Python: >=3.6

# intelliops-sdk

## Overview

`intelliops-sdk` is a Python package designed to provide a set of tools and functionalities for intelligent operations. This package aims to streamline workflows and enhance productivity through automation and intelligent decision-making.

## Installation

You can install the `intelliops-sdk` package using pip. Run the following command in your terminal:

```
pip install intelliops-sdk
```

## Usage

Here is a simple example of how to use the `intelliops-sdk` package:

```python
from intelliops_sdk import module

# Example usage of a function from the module
result = module.some_function()
print(result)
```

## Contributing

If you would like to contribute to the `intelliops-sdk`, please fork the repository and submit a pull request. We welcome contributions of all kinds!

## License

This project is licensed under the MIT License. See the LICENSE file for more details.