semvec/pypi-packages huggingface dataset:
pe-designer 2.0.0
Prime editing tools that consist of a reverse transcriptase linked with Cas9 nickase are capable of generating targeted insertions, deletions,and base conversions without producing DNA double strand breaks or requiring any donor DNA.

# PE-Designer

Prime editing tools that consist of a reverse transcriptase linked with Cas9 nickase are capable of generating targeted insertions, deletions, and base conversions without producing DNA double strand breaks or requiring any donor DNA.


usage

    python3 pe-designer.py reference_fasta induced_fasta PAM_sequence reference_genome_directory


Example

    python3 pe-designer.py example_ref.fasta example_ind.fasta NGG /directory/of/reference/genome/
haloop 0.0.10
speech agent for 100 hours
Requires Python: >=3.10

# haloop

[![PyPI Version](https://img.shields.io/pypi/v/haloop.svg)](https://pypi.python.org/pypi/haloop)

Haloop is a speech agent toolkit. Haloop provides:

- `hai` program to initialize models;
- `hac` program for acoustic model training;
- `hal` for RNN language model training and evaluation;
- `hala` for causal attention model training;
- `hat` for agent testing.

The package can be installed from PyPI:

```
pip install haloop
```

### Pretrained models

`hat` can be used with Ukrainian GPT-2 models from our paper [GPT-2 Metadata Pretraining Towards Instruction Finetuning for Ukrainian](https://github.com/proger/uk4b).

You will need to install and download:

```
pip install bitsandbytes sentencepiece

wget https://a.wilab.org.ua/gpt/wiki.model  # sentencepiece tokenizer
wget https://a.wilab.org.ua/gpt/ckpt10m.pt  # model checkpoint for GPT-2 Large
```

Now, kick off the REPL:

```
hat --spm wiki.model ckpt10m.pt
```

### Citing

Please cite:

```
@inproceedings{kyrylov-chaplynskyi-2023-gpt,
    title = "{GPT}-2 Metadata Pretraining Towards Instruction Finetuning for {U}krainian",
    author = "Kyrylov, Volodymyr  and
      Chaplynskyi, Dmytro",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.unlp-1.4",
    pages = "32--39",
    abstract = "We explore pretraining unidirectional language models on 4B tokens from the largest curated corpus of Ukrainian, UberText 2.0. We enrich document text by surrounding it with weakly structured metadata, such as title, tags, and publication year, enabling metadata-conditioned text generation and text-conditioned metadata prediction at the same time. We pretrain GPT-2 Small, Medium and Large models each on single GPU, reporting training times, BPC on BrUK and BERTScore on titles for 1000 News from the Future. Next, we venture to formatting POS and NER datasets as instructions, and train low-rank attention adapters, performing these tasks as constrained text generation. We release our models for the community at https://github.com/proger/uk4b.",
}
```

### Reading

[Speech Discrimination by Dynamic Programming, T. K. Vintsyuk (1968)](https://link.springer.com/article/10.1007/BF01074755)
artus 0.3.1
A geoAI package to train and predict spatial occurences on rasters or georeferenced images.
Requires Python: >=3.8

<div align="center">

<img src="https://github.com/6tronl/artus/blob/main/docs/logo_artus.png?raw=True" height="130px">

# Predict geospatial data with artificial intelligence

[![Documentation Status](https://readthedocs.org/projects/artus/badge/?version=latest)](https://artus.readthedocs.io/en/latest/?badge=latest)
[![Python package](https://github.com/6tronl/artus/actions/workflows/main.yml/badge.svg)](https://github.com/6tronl/artus/actions/workflows/main.yml)
[![PyPI version](https://badge.fury.io/py/artus.svg)](https://badge.fury.io/py/artus)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7852855.svg)](https://doi.org/10.5281/zenodo.7852855)

</div>


Artus is a python package to automatically produce maps thanks to deep learning models. With artus, you can train deep learning learning models (neural network)
on raster images annotated with vector files. You can then use the trained model to predict spatial occurrences on new unlabeled rasters. Predictions can be exported to a GeoJson format and uploaded in your favourite GIS software.

To handle large raster file, artus provides a way to tile raster into smaller tiles according to different cutting grids.

Artus has already been implemented in three use cases using 3 differents inputs data : satellite images to detect gillnets vessels, orthomosaics to detect corals
species and under water images marked with a georeferenced point to detect marine species.

For example, the following map is generated by automatically detecting dead corals on images associated with a single GPS point:
<div align="center">
<img src="https://github.com/6tronl/artus/blob/main/docs/heatmap_study_area.png?raw=True" height="500px">
</div>

This project is being developed as part of the G2OI project, cofinanced by the European union, the Reunion region, and the French Republic.
<img src="https://github.com/6tronl/artus/blob/main/docs/logos_partenaires.png?raw=True" height="40px">

## Installation

All the installation procedures are available here : [install artus](https://artus.readthedocs.io/en/latest/installation.html)

## Tutorials

If you want to get started with artus you can follow the [notebooks](https://github.com/6tronl/artus-examples). Depending on you requirements, you will find tutorials to train
a new deep learning model, to predict an unlabeled raster or to convert different annotations files (COCO, geojson...).

## Documentation

Documentation is available on [Read the docs](https://artus.readthedocs.io/en/latest/)