semvec/pypi-packages huggingface dataset:
grouped-sampling 1.0.20
A faster algorithm for generating texts with cuasal language models
Requires Python: >=3.6
Keywords: GPT,NLP,cuasal-language-model,cuasal-text-generation,gpt,language-model,natural-language-processing,nlp,text-generation

# GROUPED SAMPLING LIBRARY

This is a library for generating text using a causal language model.

This library is using the grouped sampling algorithm.

This library supports models from huggingface hub that follow the causal language model architecture.

## Installation

```pip install grouped-sampling```

## Usage

1. Make sure You have python 3 (3.9+ is recommended), fast internet connection and the grouped sampling library.
2. Choose a Causal Language Model from huggingface hub
3. Choose a group size, which should be upper limit for the length of the generated texts.
A higher group size will cause unnecessary computations.
A lower group size will cause lower performance both in runtime and text quality.
4. `pipe = GroupedSamplingPipeLine(model_name=YOUR_MODEL_NAME, group_size=YOUR_GROUP_SIZE)`
5. `answer = pipe(YOUR_TEXT)["generated_text"]`
percent-max-diff 0.99.1
Percent Maximum Difference

# README #

### Tutorials ###

Blahh

### What is this repository for? ###

* PMD

### How do I get set up? ###

PyMINEr is now pip installable:
`python3 -m pip pmd`
Note that there was another package called pyminer for mining bit-coin - this is definitely not that, so be sure to install bio-pyminer instead!

You can also install using the setup.py script in the distribution like so:
`python3 setup.py install`


### How do I run PyMINEr? ###
#### Video Tutorials ####
Blahhh

```
import numpy as np
from percent_max_diff.percent_max_diff import pmd
pmd_res = pmd(np.array([[100,0,0],[0,100,0],[0,0,100]]))
print(pmd_res.pmd)
```

### License ###
For non-commercial use, PyMINEr is available via the AGPLv3 license. Commercial entities should inquire with scottyler89@gmail.com

### Who do I talk to? ###

* Repo owner/admin: scottyler89+bitbucket@gmail.com
lumnisfactors 0.0.5
Lumnis Alternative Data
Requires Python: >=3.6

### Hi there 👋


# Lumnis Crypto Alternative Data API

The LUMNIS Crypto API provides the most comprehensive factor dataset for crypto assets. We provide the following group of factors:

- **Micro-structural Factors** - These are factors derived from **order book data and trade data**. We process terabytes of data to extract the most relevant order book and trade factors that are delivered in real-time
- **Regime Factors** - We provide **regime data** that enables users to observe how strategies perform in different conditions of the market. For example, some strategies work better during high volatile regimes. We use proprietary algorithms for effectively determining regimes.


This data can be used for backtesting and implementing trading strategies.

### Documentation
Click [here](https://docs.lumnis.io) to view documentation on API usage.

### Receiving API Token
You can sign up [here](https://docs.lumnis.io) to retreive a lumnis API Key 

Please [e-mail me](mailto:ajaye@lumnis.org) if there is an issue with setting up or retreiving an API Key.

## Getting Started
#### Prerequisites
- Python version 3 installed

#### Installation
The package can easily be installed by running the following command 
```python
pip install lumnisfactors
```


### Usage
```python
#Import the package
from lumnisfactors import LumnisFactors

#Add your API KEY
API_KEY = ""
lumnis = LumnisFactors(API_KEY)

#Retrieve a single days worth of data
df_single_date  = lumnis.get_single_date_data("vpin", "coinbase", "ethusd",  "hour", "2022-01-01")

#Retrieve multiple days worth of data
historical_data = lumnis.get_historical_data("vpin", "coinbase", "ethusd",  "hour", "2022-01-01", "2022-02-01")
```