semvec/pypi-packages huggingface dataset:
farstail 1.0.5
Persian Natural Language Inference DataSet
Requires Python: >=3.6

# FarsTail: A Persian Natural Language Inference Dataset
<br>
<br>
Natural Language Inference (NLI), also called Textual Entailment, is an important task in NLP with the goal of determining the inference relationship between a premise p and a hypothesis h. It is a three-class problem, where each pair (p, h) is assigned to one of these classes: "ENTAILMENT" if the hypothesis can be inferred from the premise, "CONTRADICTION" if the hypothesis contradicts the premise, and "NEUTRAL" if none of the above holds.
<br>There are large datasets such as SNLI, MNLI, and SciTail for NLI in English, but there are few datasets for poor-data languages like Persian.
<br>Persian (Farsi) language is a pluricentric language spoken by around 110 million people in countries like Iran, Afghanistan, and Tajikistan. Here, we present the first relatively large-scale Persian dataset for NLI task, called FarsTail. A total of 10,367 samples are generated from a collection of 3,539 multiple-choice questions. The train, validation, and test portions include 7,266, 1,537, and 1,564 instances, respectively.
<br>
<br>


## Getting started with package
We have provided an API in the form of a python package to read and use FarsTail easier for persian and non-persian language researchers. In the following, we will explain how to use this package.
<br>
<br>You'll need Python 3.6 or higher.
### Installation
```
pip install farstail
```
### using
* Loading the original FarsTail dataset.
```python
from farstail.datasets import farstail
train_data, val_data, test_data = farstail.load_original_data()
```


* Loading the indexed FarsTail dataset.
```python
from farstail.datasets import farstail
train_ind, val_ind, test_ind, dictionary = farstail.load_indexed_data()
```
Logria 0.9.14
A powerful CLI tool that puts log analytics at your fingertips.
Requires Python: >=3.6

![Logria Logo](/resources/branding/logria.svg)

# Logria Python Proof of Concept

A powerful CLI tool that puts log analytics at your fingertips. This is the Python Proof of Concept repository; the [main version](https://github.com/ReagentX/Logria/) is written in Rust.

## tl;dr

- Live filtering of data from other processes, replacing `grep`
- Use shell commands or files as input, save sessions and come back later
- Replace regex/filter without killing the process or losing the stream's history
- Parse logs using user-defined rules, apply analytics methods on top
- Pure Python, fully type hinted, zero dependencies

## Installation

There are several options to install this app.

### Normal Usage

`pip install logria`

### Development

See [Advanced Installation](docs/README.md#advanced-installation).

## Usage

There are a few main ways to invoke Logria:

- Directly:
  - `logria`
  - Opens to the setup screen
- With args:
  - `logria -e 'tail -f log.txt'`
  - Opens a pipe to `tail -f log.txt` and skips setup
  - `logria -h` will show the help page with all possible options

For more details, see [Sample Usage Session](docs/README.md#sample-usage-session).

## Key Commands

| Key | Command |
|--|--|
| `:` | [command mode](docs/commands.md) |
| `/` | regex search |
| `h` | if regex active, toggle highlighting of matches |
| `i` | toggle insert mode (default off) |
| `s` | swap reading `stdin` and `stdout` |
| `p` | activate parser |
| `a` | toggle analytics mode when parser is active |
| `z` | deactivate parser |
| ↑ | scroll buffer up one line |
| ↓ | scroll buffer down one line |
| → | skip and stick to end of buffer |
| ← | skip and stick to beginning of buffer |

## Features

Here are some of the ways you can leverage Logria:

### Live stream of log data

![logria](/resources/screenshots/logria.png)

### Interactive, live, editable grep

![regex](/resources/screenshots/regex.png)

### Live log message parsing

![parser](/resources/screenshots/parser.png)

### Live analytics/statistics tracking

![analytics](/resources/screenshots/analytics.png)

### User-defined saved sessions

See [session](/docs/sessions.md) docs.

### User-defined saved log parsing methods

See [patterns](/docs/patterns.md) docs.