semvec/pypi-packages huggingface dataset:
regexbuilder-py 1.0.0
A functional wrapper for RegEx in Python.
Requires Python: >=3.6

# RegexBuilder: A functional wrapper for RegEx in Python.

## Installation
```sh
python -m pip install regexbuilder-py
# or
python -m pip install git+https://github.com/AaravMalani/regexbuilder
```

## Usage
```py
print(RegEx().start() \
       .string('http') \
       .chars('s').modify(Modifiers.ZERO_OR_ONE) \
       .string('://') \
       .group(RegEx() 
              .chars('a-zA-Z0-9') \
              .modify(Modifiers.ONE_OR_MORE) \
              .chars(r'.')) \
       .modify(Modifiers.ZERO_OR_MORE) \
       .chars('A-Za-z0-9').modify(Modifiers.ONE_OR_MORE) \
       .chars('.') \
       .chars('A-Za-z').modify(Modifiers.ONE_OR_MORE) \
       .group(RegEx().string('.')).build())
# $(?:http)[s]?(?:://)([a-zA-Z0-9]+[.])*[A-Za-z0-9]+[.][A-Za-z]+((?:.))
```
checkTAPSpackages 0.5.5
Checks if a document complies with the list of LaTeX packages allowed by TAPS.

[![PyPI Version](https://img.shields.io/pypi/v/checkTAPSpackages)](https://pypi.org/project/checkTAPSpackages/)
[![License](https://img.shields.io/github/license/casiez/checkTAPSpackages)](LICENSE)
[![Downloads](https://static.pepy.tech/badge/checkTAPSpackages)](https://pepy.tech/project/checkTAPSpackages)

# checkTAPSpackages

Gets all packages used in a LaTeX document and checks they are in the [list of packages allowed by TAPS](https://www.acm.org/publications/taps/accepted-latex-packages)

Tested on macOS, should work on Linux, could work on Windows.

Install using ```pip install -U checkTAPSpackages```

1. Compile your document by adding ```\listfiles``` below ```\documentclass```
1. Run ```checkTAPSpackages yourGeneratedLogFile.log```

Use ```--all``` option to show all packages used.

Use ```--expand``` to expand the list of subpackages, which produces something like the following:

```
colortbl.sty         => NOT SUPPORTED
eurosym.sty          => NOT SUPPORTED
flushend.sty         => NOT SUPPORTED
macros.sty           => NOT SUPPORTED
   paralist.sty         => NOT SUPPORTED
mathtools.sty        => NOT SUPPORTED
   mhsetup.sty          => NOT SUPPORTED
mhsetup.sty          => NOT SUPPORTED
paralist.sty         => NOT SUPPORTED
submission.sty       => NOT SUPPORTED
   mathtools.sty        => NOT SUPPORTED
   mhsetup.sty          => NOT SUPPORTED
   wasysym.sty          => NOT SUPPORTED
   colortbl.sty         => NOT SUPPORTED
   ulem.sty             => NOT SUPPORTED
   eurosym.sty          => NOT SUPPORTED
   flushend.sty         => NOT SUPPORTED
ulem.sty             => NOT SUPPORTED
wasysym.sty          => NOT SUPPORTED
```

Use ```--diff``` option to list newly supported or no longer supported packages.
minicpuinfo 2.2.0
minicpu is cpuinfo mini version tool


### mini tool for cpu-info


$ minicpuinfo -h


#### Options

-h, --help      show this help menu
  -c, --cpu       shows how many cpu's available
  -d, --disk      disk partitions
  -D, --dpercent  disk percentage
  -m, --memusage  all memory usages
  -C, --cpuload   cpu load percentage


##### about me

-[sahayavalan](https://github.com/sahayavalan/)


...

...
post-processing-library-llm 0.0.5
A brief description of package

Post-processing-library-llm
===========================

Installation
------------

You can install Post-processing-library-llm library using pip:

.. code-block:: shell

   pip install Post-processing-library-llm

Named Entity Recognition (NER)
-------------------------------

In this example, we'll use the Post-processing-library-llm to perform NER on a set of documents and evaluate the results.

.. code-block:: python

   from post_processing_library_llm.ner import ner

   # Define ground truth and prediction dictionaries
   truth_dict = {
       'airplane': ["Boeing B-52 Stratofortress", "predator drone"]
   }
   prediction_dict = {
       'airplane': ["drone", "sparrow"]
   }

   # Perform NER and get results
   ner_results = ner.named_entity_recognition(truth_dict, prediction_dict, fuzzy_threshold)

   # Print the results
   print(ner_results)

NER Output:
   {'Correct Entities': 1, 'Missed Entities': 1, 'False Positive Entities': 1}

Text Matching Example
----------------------

Here's an example of how to perform Text Matching using the `Post-processing-library-llm` library:

Text Matching involves comparing two text strings and determining how similar or different they are. The post_processing_library_llm provides various text matching algorithms, including direct matching, token overlap, and cosine similarity, and gives all the metrics as output.

In this example, we'll use the Text Matcher to compare a list of ground truth texts with predicted texts and calculate matching metrics.

.. code-block:: python

   from post_processing_library_llm.get_metrics import TextMatcher

   # Define ground truth and prediction lists
   truth_list = ["this is a ball", "cat", "Toyota Camry"]
   pred_list = ["this is a ball", "Ford Mustang", "Toyota Corolla"]

   # Perform text matching with cosine similarity
   metrics = text_matcher.match_texts(truth_list, pred_list, match_type='cosine_similarity')

   # Print the metrics
   print(metrics)

Text Matching Output:
   {'Precision': 1.0, 'Recall': 0.3333333333333333, 'Specificity': nan, 'F1 Score': 0.5, 'Negative Predictive Value (NPV)': 0.0, 'False Positive Rate (FPR)': nan, 'False Negative Rate (FNR)': 0.6666666666666666, 'False Discovery Rate (FDR)': 0.0, 'Accuracy': 0.3333333333333333, 'True Positive Rate (TPR)': 0.3333333333333333, 'Positive Predictive Value (PPV)': 1.0, 'True Negative Rate (TNR)': nan, 'Macro Average Precision': 0.5, 'Macro Average Recall': 0.16666666666666666, 'Macro Average F1 Score': 0.25, 'Weighted Average Precision': 1.0, 'Weighted Average Recall': 0.3333333333333333, 'Weighted Average F1 Score': 0.5}