semvec/pypi-packages huggingface dataset:
birddata 0.1.1
A beginner-friendly bird classification dataset
Requires Python: >=3.6

# 🐦 birddata

A beginner-friendly dataset for bird classification – inspired by the classic `load_iris`, but with a fresh and fun twist!

---

## 📌 Description

If you're learning Machine Learning and want a clean, ready-to-use dataset to practice classification, `birddata` is for you!

Just like `load_iris`, it returns a pandas DataFrame – but instead of flowers, it's about **birds** 🐥.

Designed specifically for beginners, this dataset helps you practice:

- Binary & multi-class classification
- Feature encoding
- Data scaling
- Building models using `scikit-learn`

---

## 🔍 Features

| Feature      | Type         | Description                          |
| ------------ | ------------ | ------------------------------------ |
| Wing_Span    | Numerical    | Wingspan of the bird (in cm)         |
| Beak_Length  | Numerical    | Length of the beak (in cm)           |
| Can_Fly      | Binary (0/1) | Can the bird fly?                    |
| Is_Nocturnal | Binary (0/1) | Is the bird active at night?         |
| Habitat_Type | Categorical  | Encoded as numbers (e.g., forest)    |
| Bird_Type    | Categorical  | Target variable (Sparrow, Eagle etc) |

---

## 🚀 Installation

```bash
pip install birddata
```

from birddata import load_bird
import pandas as pd

df = load_bird()
print(df.head())

## 🙌 Acknowledgements

- Inspired by the classic `load_iris` dataset from the `scikit-learn` library
- Thanks to the amazing open-source Python community 💙
- Special thanks to [scikit-learn](https://scikit-learn.org/) and the Python Software Foundation
dbtk-setbert 1.0.2
The official SetBERT implementation using the deepbio-toolkit.
Requires Python: >=3.8

# [SetBERT](https://academic.oup.com/bioinformatics/article/41/7/btaf370/8173948)

The official code repository for [SetBERT: SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing](https://academic.oup.com/bioinformatics/article/41/7/btaf370/8173948)

![Graphical Abstract](
https://raw.githubusercontent.com/DLii-Research/setbert/refs/heads/master/graphics/graphical_abstract.png)

## Quick Start

Installation from [PyPI](https://pypi.org/project/dbtk-setbert/):
```bash
pip install dbtk-setbert
```

Download SetBERT pre-trained on the Qiita 16S platform (see [Available Models](#available-models) for other options):
```py
from setbert import SetBert

# Download the model
model = SetBert.from_pretrained("sirdavidludwig/setbert", revision="qiita-16s")

# Get the tokenizer
tokenizer = model.sequence_encoder.tokenizer
```

Example sample embedding

```py
# Input sample
sequences = [
    "ACTGCAG",
    "TGACGTA",
    "ATGACGA"
]

# Tokenize sequences in the sample
sequence_tokens = torch.stack([tokenizer(s) for s in sequences])

# Compute embeddings
output = model(sequence_tokens)

# Sample level representation
sample_embedding = output["class"]

# Contextualized sequence representations
sequence_embeddings = output["sequences"]
```

## Available Models:
<a name="available-models"></a>

Model Revision | Platform     | Pre-training Dataset Description
---------------|--------------|---------------------------------
`qiita-16s`    | 16S Amplicon | ~280k 16S amplicon samples from the Qiita platform

## Configuration

SetBERT embeds the DNA sequences in chunks using activation checkpointing. This chunk size is specified
by the `sequence_encoder_chunk_size` parameter in the `SetBert.Config` class and adjusted freely at any point.

```py
# Set chunk size
model.config.sequence_encoder_chunk_size = 256 # default

# Remove chunking and embed all sequences in parallel
model.config.sequence_encoder_chunk_size = None
```

## Manual Installation

```bash
git clone https://github.com/DLii-Research/setbert
pip install -e ./new-setbert
```

## Citation

```bibtex
@article{ludwig_setbert_2025,
	title = {{SetBERT}: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing},
	volume = {41},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btaf370},
	number = {7},
	journal = {Bioinformatics},
	author = {Ludwig, II, David W and Guptil, Christopher and Alexander, Nicholas R and Zhalnina, Kateryna and Wipf, Edi M -L and Khasanova, Albina and Barber, Nicholas A and Swingley, Wesley and Walker, Donald M and Phillips, Joshua L},
	month = jul,
	year = {2025},
}
```

## Original Experiment Source Code

The original source code used to produce the models and experiments for the manuscript are available in the bioinformatics branch of this repository.
ipv8-rust-tunnels 0.1.33
IPv8 tunnel performance enhancements
Requires Python: >=3.9

# IPv8-rust-tunnels
[![](https://img.shields.io/pypi/v/ipv8-rust-tunnels.svg?label=PyPI)](https://pypi.org/project/ipv8-rust-tunnels/) &emsp; ![Unit tests](https://github.com/Tribler/ipv8-rust-tunnels/actions/workflows/test.yml/badge.svg)

This module provides a set of performance enhancements to the `TunnelCommunity`, the anonymization layer used in [IPv8](https://github.com/Tribler/py-ipv8) and [Tribler](https://github.com/Tribler/tribler). It works by handling the tunnel data traffic in Rust, while letting the Python anonymization layer handle the tunnel control logic.