semvec/pypi-packages huggingface dataset:
Base-converter 0.1.0
A code to convert from a numerical base to another
Requires Python: >=3.0

# Numerical-base-converter

This package is to convert a value from a numerical base to another.
It can work with python >= 3.0 and was created under python 3.8.5

Here is the GitHub link: <https://github.com/Leoriem-code/Numerical-base-converter>

The 'base' function use the 'convert' function to convert the numbers given (as a string).
If a list/tuple is provided, the function will do all the string in the list/tuple.
If all the numbers in the are not to be converted with the same base, please provided these values in a list following this:
    base([n_1, n_2, ..., [n_x, new_w_1, new_t_1], n_x+1, ..., [n_z, new_w_2, new_t_2]], base_written_in, base_to_write_to)

Here, 'base_written_in' and 'base_to_write_to' are the main base in wich we have to write in and, 'n_1', 'n_2', 'n_x', 'n_x+1' and 'n_z' are numbers we want to convert.
However, the difference lay in the base in wich we want to write them.
 'n_x' and all the numbers that follow will be considered written in base 'new_w_1'.
They also will be written in base 'new_t_1' until say otherwise (like with [n_z, new_w_2, new_t_2])

So a lengthy explanation to say that in the example: /!\ /!\ read carefully /!\ /!\
        base(['0', '1', ['2', 10, 2], '3', ['4', 10, 3], '5', '6'], 2, 10)

        0 and 1 are considered written in base 2 and will be output in base 10. So the output will be ['0', '1']
        2 is written in base 10 and will be output in base 2, output: '10'
        3 will be considered like 2 (because we have not asked to change the values of the base), output: '11'
        4, 5 and 6 are written in base 10 and will be written in base 3, output: ['11', '12', '20']
question-intimacy 1.1
This package is used to predict intimacy for questions
Requires Python: >=3.6

# Question-Intimacy

## Intro
question-intimacy is a package used to estimate the intimacy of questions. It is released with
EMNLP 2020 paper `Quantifying Intimacy in Language`. 


## Install 

### Use pip
If `pip` is installed, question-intimacy could be installed directly from it:

    pip3 install question-intimacy

### Dependencies
	python>=3.6.0
	torch>=1.6.0
	transformers >= 3.1.0
	numpy
	math
	tqdm
	
	
## Usage and Example

### Notes: During your first usage, the package will download a model file automatically, which is about 500MB.

### `Construct the Predictor Object`
	>>> from question_intimacy.predict_intimacy import IntimacyEstimator
	>>> inti = IntimacyEstimator()
Cuda is disabled by default, to allow GPU calculation, please use

	>>> from question_intimacy.predict_intimacy import IntimacyEstimator
	>>> inti = IntimacyEstimator(cuda=True)

### `predict`
`predict` is the core method of this package, 
which takes a single text of a list of texts, and returns a list of raw values in `[-1,1]` (higher means more intimate, while lower means less).

	# Predict intimacy for one question
	>>> text = 'What is this movie ?''
	>>> inti.predict(text,type='list')
	-0.2737383
	
	# Predict intimacy for a list of questions (less than a batch)
	>>> text = ['What is this movie ?','Why do you hate me ?']
	>>> inti.predict(text,type='list')
	[-0.2737383, 0.3481976]
	
	# Predict intimacy for a long list of questions
	>>> text = [a long list of questions]
	>>> inti.predict(text,type='long_list',tqdm=tqdm)
    [-0.2737383, 0.3481976, ... ,-0.2737383, 0.3481976]



## Contact
Jiaxin Pei (pedropei@umich.edu)