rara-text-evaluator 1.1.4

Evaluate the quality of texts from a range of 0-1

RaRa Text Evaluator

rara-text-evaluator is a Python library for evaluating the quality of text using n-gram models.

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✨ Features

• Evaluate text quality with either pre-built models or create your own.
• Build and train n-gram models for text quality evaluation.
• Pre-trained models for Estonian, English, and a language-agnostic fallback.
• Easy to extend for other languages or corpora.

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⚡ Quick Start

Get started with rara-text-evaluator in just a few steps:

1. Install the Package
   Ensure you're using Python 3.10 or above, then run:

   pip install rara-text-evaluator
   

2. Import and Use
   Example usage to evaluate text:

   from rara_text_evaluator.quality_evaluator import QualityEvaluator
   
   evaluator = QualityEvaluator()
   
   example_text = "Some text here that is over 30 characters long."
   score = evaluator.get_probability(example_text)
   is_valid = evaluator.is_valid(example_text)
   
   print(f"Text Quality Score: {score}")
   print(f"Text is valid: {is_valid}")
   

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💡 Important to note

• Texts shorter than 30 characters will result in probability score 0.0 by default. Both the minimum length and the default response can be configured with parameters length_limit and default_response. See documentation to learn more.

⚙️ Installation Guide

Follow the steps below to install the rara-text-evaluator package, either via pip or locally.

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Installation via pip

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1. Set Up Your Python Environment
   Create or activate a Python environment using Python 3.10 or above.

2. Install the Package
   Run the following command:

   pip install rara-text-evaluator
   

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Local Installation

Follow these steps to install the rara-text-evaluator package locally:

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1. Clone the Repository
   Clone the repository and navigate into it:

   git clone <repository-url>
   cd <repository-directory>
   

2. Install Git LFS
   Ensure you have Git LFS installed and initialized:

   git lfs install
   

3. Pull Git LFS Files
   Retrieve the large files tracked by Git LFS:

   git lfs pull
   

4. Set Up Python Environment
   Create or activate a Python environment using Python 3.10 or above. E.g:

   conda create -n py310 python==3.10
   conda activate py310
   

5. Install Build Package
   Install the build package to enable local builds:

   pip install build
   

6. Build the Package
   Run the following command inside the repository:

   python -m build
   

7. Install the Package
   Install the built package locally:

   pip install .
   

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🚀 Testing Guide

Follow these steps to test the rara-text-evaluator package.

How to Test

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1. Clone the Repository
   Clone the repository and navigate into it:

   git clone <repository-url>
   cd <repository-directory>
   

2. Install Git LFS
   Ensure Git LFS is installed and initialized:

   git lfs install
   

3. Pull Git LFS Files
   Retrieve the large files tracked by Git LFS:

   git lfs pull
   

4. Set Up Python Environment
   Create or activate a Python environment using Python 3.10 or above.

5. Install Build Package
   Install the build package:

   pip install build
   

6. Build the Package
   Build the package inside the repository:

   python -m build
   

7. Install with Testing Dependencies
   Install the package along with its testing dependencies:

   pip install .[testing]
   

8. Run Tests
   Run the test suite from the repository root:

   python -m pytest -v tests
   

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📝 Documentation

Documentation can be found here.

🌍 Supported Models

The QualityEvaluator class leverages language-specific pre-built models to assess the quality of provided text. It also supports an automatic fallback model for cases where the specified or detected language is not supported.

Built-in Models

The package has built-in support for the following languages:

• Estonian
• English
• Language-agnostic fallback model

The table below provides details on the corpora used for training each model:

Language	Model Name	Corpora	Words	Characters
Estonian	text_validator_ngram_3_et.pkl	DIGAR "born digital" articles	4,164,975	30,630,998
English	text_validator_ngram_3_en.pkl	NLTK corpora (gutenberg, brown, reuters, webtext)	5,900,439	28,649,578
Language-agnostic	text_validator_ngram_3_fallback.pkl	Combined DIGAR and NLTK corpora	10,065,413	59,280,576

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Additional Notes

• Automatic Fallback: If the target language isn't explicitly supported, the language-agnostic fallback model will be used.
• N-Gram: All models currently use a trigrams (n=3) approach to evaluate text quality.

🔍 More Usage Examples

This section provides additional examples of possible usage and highlights the roles of some parameters.

Impact of parameters length_limit and default_response

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from rara_text_evaluator.quality_evaluator import QualityEvaluator

evaluator = QualityEvaluator()

text = "This is a valid text."
text_length = len(text)

score = evaluator.get_probability(text)

print(f"Text length: {text_length} characters")
print(f"Quality score: {score}")

Output:

Text length: 21 characters
Quality score: 0.0

As the default length_limit param is set to 30, the output score is automatically set to the default value of default_response (0.0).

However, we can modify it to allow shorter texts as well:

from rara_text_evaluator.quality_evaluator import QualityEvaluator

evaluator = QualityEvaluator()

text = "This is a valid text."
text_length = len(text)

score = evaluator.get_probability(text, length_limit=20)

print(f"Text length: {text_length} characters")
print(f"Quality score: {score}")

Output:

Text length: 21 characters
Quality score: 0.7611294876594459

As we see, the method now returns a real quality score, it is just a little bit lower than expected considering the text is actually completely valid. This is why the cut-off was added in the first place - so we can distinguish between texts that actually have low quality opposed to just being short.

Setting thresholds for binary evaluation

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Let's first inspect the results with default thresholds:

from rara_text_evaluator.quality_evaluator import QualityEvaluator

evaluator = QualityEvaluator()

text_en = "This is more or lesh valihd text but coneins some mistakes."
text_et = "See tekst sishaldap mõnet väjkeset vead, mis võib-olla on okei."

score_en = evaluator.get_probability(text_en)
score_et = evaluator.get_probability(text_et)

is_valid_en = evaluator.is_valid(text_en)
is_valid_et = evaluator.is_valid(text_et)

print(f"text_en is valid: {is_valid_en} (score = {score_en}.")
print(f"text_et is valid: {is_valid_et} (score = {score_et}.")
print(f"Current thresholds for validity:{evaluator.thresholds}")

Output:

text_en is valid: True (score = 0.8382394549211963.
text_et is valid: True (score = 0.7840549033157463.
Current thresholds for validity:{'et': 0.7, 'en': 0.7, 'fallback': 0.7}

As we can see, both English and Estonian example texts pass the validity check with default thresholds. Let's assume that we want a lot higher quality for our texts in Estonian and set the threshold for that language higher:

from rara_text_evaluator.quality_evaluator import QualityEvaluator

evaluator = QualityEvaluator()

text_en = "This is more or lesh valihd text but coneins some mistakes."
text_et = "See tekst sishaldap mõnet väjkeset vead, mis võib-olla on okei."

# Let's set higher threshold for Estonian
evaluator.set_threshold(lang="et", threshold=0.9)

score_en = evaluator.get_probability(text_en)
score_et = evaluator.get_probability(text_et)

is_valid_en = evaluator.is_valid(text_en)
is_valid_et = evaluator.is_valid(text_et)

print(f"text_en is valid: {is_valid_en} (score = {score_en}.")
print(f"text_et is valid: {is_valid_et} (score = {score_et}.")

print(f"Current thresholds for validity:{evaluator.thresholds}")

Output:

text_en is valid: True (score = 0.8382394549211963.
text_et is valid: False (score = 0.7840549033157463.
Current thresholds for validity:{'et': 0.9, 'en': 0.7, 'fallback': 0.7}

Building and applying a custom language model

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from rara_text_evaluator.ngram_model_builder import NgramModelBuilder
from rara_text_evaluator.quality_evaluator import QualityEvaluator

# Setting class instance parameters
# and creating the class instance
n_gram = 2
language = "klingon"
accepted_chars ="jilnrguhqtmpseybvwaocd.,:;-_!\"()%@1234567890' "

nmb = NgramModelBuilder(n=n_gram, lang=language, accepted_chars=accepted_chars)

# Training and saving the model

#NB! This is just a dummy example! You should use a much bigger corpus!
text_corpus = "'ach cha'logh nItebHa' 'ej mIw vIghoS 'e' vIghoS, 'each vIghoS 'ej cha'logh vIghoS"
model_path = "klingon_ngram.pkl"

nmb.build_model(text_corpus)
nmb.save_model(model_path)

# Using the new model via QualityEvaluator instance

evaluator = QualityEvaluator()
evaluator.add_model(lang="klingon", model_path="klingon_ngram.pkl")

# NB! For custom languages not supported by langdetect,
# it is paramount to pass it along with `lang` parameter!
score = evaluator.get_probability(
    text="vaj nItebHa'vam vIghoS,",
    lang="klingon",
    length_limit=10
)
print(score)

Output:

0.99999999999107