semvec/pypi-packages huggingface dataset:
py-entity-linking-el 0.1.8
A lightweight library for entity linking in Greek.
Requires Python: >=3.10

Lightweight entity linking solution for Greek language.

**Please consider citing our works if you use code from this repository.**
**Also, we recommend using a Colab T4 GPU for faster results.**

## Main dependencies
* python>=3.10
* numpy==1.26.4
* SPARQLWrapper==2.0.0
* sentence_transformers==3.1.1
* aiohttp==3.9.5
* openai==1.55.3
* httpx==0.28.1
* beautifulsoup4==4.12.2
* nest_asyncio==1.5.8

## Example & Usage
```
from linking import main

# Your API token which can be found here (https://github.com/marketplace/models/azure-openai/gpt-4o)
api_token = "YOUR_API_TOKEN"

main.EL(api_token=api_token,
	sentence="Χάρη στην ΕΦΓ, μια μηχανή μπορεί να 'καταλάβει' το περιεχόμενο των εγγράφων, συμπεριλαμβανομένων των αποχρώσεων του πλαισίου των της γλώσσας σε αυτά.",
	mention="ΕΦΓ",
	single="No",
	combination="No",
	embedding_model="intfloat/multilingual-e5-large-instruct")
```

```
The correct entity for 'ΕΦΓ' is:

Wikipedia: https://el.wikipedia.org/wiki/Επεξεργασία_φυσικής_γλώσσας

Wikidata: https://www.wikidata.org/wiki/Q30642

DBpedia: http://dbpedia.org/resource/Natural_language_processing


Execution Time: 00:00:29
```

## Parameters
* **api_token**: Your API token from [here](https://github.com/marketplace/models/azure-openai/gpt-4o). **(Required)**  
* **sentence**: A Greek text. **(Required)**  
* **mention**: The mention you want to perform the linking, the mention should be from inside the provided sentence. **(Required)**  
* **single**: Usually used for difficult mentions, it searches each word of the mention individually, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*  
* **combination**: Usually used for difficult mentions, it makes combinations for each word of the mention, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*  
* **embedding_model**: A [sentence-transformers](https://sbert.net/) model to perform text similarity, *(deafault="intfloat/multilingual-e5-large-instruct")*, *(Values: str of the name of any sentence-transformers model that supports Greek)*. *(Optional)*

## Licence
This library is licensed under the [CC-BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/).
ai-helpers-jax-utils 0.1.0a2
Common jax utils
Requires Python: <3.11,>=3.10
Keywords: machine-learning, jax, utils

# AI Helpers - Jax Utils

[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=ai-helpers_jax-utils&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=ai-helpers_jax-utils)
[![Bugs](https://sonarcloud.io/api/project_badges/measure?project=ai-helpers_jax-utils&metric=bugs)](https://sonarcloud.io/summary/new_code?id=ai-helpers_jax-utils)
[![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=ai-helpers_jax-utils&metric=code_smells)](https://sonarcloud.io/summary/new_code?id=ai-helpers_jax-utils)

`jax-utils` is a Python module that provides a collection of utilities to simplify and enhance the use of JAX. These utilities are designed to make working with JAX more efficient and to reduce boilerplate code.

## Table of Contents

- [AI Helpers - Jax Utils](#ai-helpers---jax-utils)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Getting Started](#getting-started)
  - [Utilities](#utilities)
  - [Examples](#examples)
  - [Contributing](#contributing)

## Installation

You can install the `jax-utils` module via pip:

```bash
pip install ai-helpers-jax-utils
```

## Getting Started

First, import the module in your Python script:

```python
import jax_utils as ju
```

Now you can use the utilities provided by `jax-utils`.

## Utilities
 > TODO

## Examples
> TODO

## Contributing

We welcome contributions to `jax-utils`. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes.
4. Commit your changes (`git commit -am 'Add some feature'`).
5. Push to the branch (`git push origin feature-branch`).
6. Create a new Pull Request.

Please ensure your code follows the project's coding standards and includes appropriate tests.
py-entity-linking 0.1.8
A lightweight library for entity linking in English
Requires Python: >=3.10

Lightweight entity linking solution for the English language.

**Please consider citing our works if you use code from this repository.**
**Also, we recommend using a Colab T4 GPU for faster results.**

## Main dependencies
* python>=3.10
* numpy==1.26.4
* SPARQLWrapper==2.0.0
* sentence_transformers==3.1.1
* aiohttp==3.9.5
* openai==1.55.3
* httpsx==0.28.1
* beautifulsoup4==4.12.2
* nest_asyncio==1.5.8

## Example & Usage
```
from linking import main

# Your API token which can be found here (https://github.com/marketplace/models/azure-openai/gpt-4o)
api_token = "YOUR_API_TOKEN"

main.EL(api_token=api_token,
	sentence="We used PCA and FA for our experiments.",
	mention="PCA and FA",
	single="No",
	combination="No",
	embedding_model="Lajavaness/bilingual-embedding-large")
```

```
The correct entity for 'PCA' is:

Wikipedia: https://en.wikipedia.org/wiki/Principal_component_analysis

Wikidata: https://www.wikidata.org/wiki/Q2873

DBpedia: http://dbpedia.org/resource/Principal_component_analysis



The correct entity for 'FA' is:

Wikipedia: https://en.wikipedia.org/wiki/Factor_analysis

Wikidata: https://www.wikidata.org/wiki/Q726474

DBpedia: http://dbpedia.org/resource/Factor_analysis

Execution Time: 00:00:35
```

## Parameters
* **api_token**: Your API token from [here](https://github.com/marketplace/models/azure-openai/gpt-4o). **(Required)**  
* **sentence**: An English text. **(Required)**  
* **mention**: The mention you want to perform the linking, the mention should be from inside the provided sentence. **(Required)**  
* **single**: Usually used for difficult mentions, it searches each word of the mention individually, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*  
* **combination**: Usually used for difficult mentions, it makes combinations for each word of the mention, *(deafult="No")*, *(Values: "Yes", "No")*. *(Optional)*  
* **embedding_model**: A [sentence-transformers](https://sbert.net/) model to perform text similarity, *(deafault="Lajavaness/bilingual-embedding-large")*, *(Values: str of the name of any sentence-transformers model)*. *(Optional)*

## Licence
This library is licensed under the [CC-BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/).
coreali 0.1.0
Convenient Register Access Library
Requires Python: >=3.10

# coreali - Convenient Register Access Library
Generates Python code from a register model. This python code can the be used to access the registers of an FPGA, a MCU or another device which has a register map.
## Installation
Coreali can be installed with pip by executing the following command

    pip install coreali

## How to use
The typical use case for coreali is:
1. Make a register description of your system in [systemRDL](https://www.accellera.org/activities/working-groups/systemrdl) or any other supported format (ipxact) of the [systemrdl-compiler](https://github.com/SystemRDL/systemrdl-compiler)
2. Compile the register description with the [systemrdl-compiler](https://github.com/SystemRDL/systemrdl-compiler)
3. Generate the hierarchical register model class and objects with the **coreali.RegisterModel**
4. Access the registers of your FPGA device with **coreali.registerio**

## Quickstart - Logger example
1. Create a register description of your system or module
```systemRDL
mem log_mem {
    mementries = 64;
    memwidth = 8;
};

addrmap logger { 
	reg  {	
		desc = "Control register";
		field {sw = rw; hw = r;} ENABLE;
		field {sw = rw; hw = r;} TRIGGERED;
	} Ctrl;
	reg  {	
		desc = "Trigger configuration for channel 0 and 1";
		field {sw = rw; hw = r;} SW_TRIG;
		field {sw = rw; hw = r;} LEVEL[8];
	} Trig[2];
  	external log_mem LogMem[2];
};

```
1. Import dependencies and compile register model with systemrdl-compiler

```python
import sys
from systemrdl import RDLCompiler
import coreali 
import numpy as np
rdlc = RDLCompiler()
rdlc.compile_file("../systemrdl/logger.rdl")
root = rdlc.elaborate()
```

2. Create the register model object
```python
rio = coreali.registerio.RegIoNoHW(np.zeros([256], np.uint8()))
logger = coreali.RegisterModel(root, rio)
```

In this step the rio register input/output obkect is created. This object handles the low level access. The RegIoNoHW class allows to access virtual registers without having a hardware at hand. The XXXXX example shows how to create your own RegIo class to access your own hardware.**`TODO`**

3. Use the generated register model
```python
logger.Ctrl.read()
logger.LogMem.write(0,[1,2,3])
logger.LogMem.read()
logger.LogMem[1].write(0,[11,12,13])
print(logger)
```
The registers can now be read and written by hierarchically accessing them. With code completion of your python IDE the registers can be accessed conveniently. 

When using the print function on a register, register node or address map all registers and field will be readout and printed as a string.

The full source code of this example can be found in
[examples/quickstart/](examples/quickstart/)