evalnlp 0.1.2

Enhanced evaluation metrics for NLP tasks

evalNLP

evalNLP is a Python library offering advanced metrics for evaluating Automatic Speech Recognition (ASR) and translation models. This library is designed to meet the needs of researchers and developers working on Natural Language Processing tasks.

Features

• UWER: Universal Word Error Rate, an enhanced metric for fair transcription evaluation, adapted to morphologically rich and low-resource languages. It uses a revised version of the Levenshtein algorithm with weighted substitutions through a cost_func and segmentation operations with a u_cost. For more details, see the paper: Universal-WER: Enhancing WER with Segmentation and Weighted Substitution for Varied Linguistic Contexts.

• UTER: (Coming Soon) Universal Translation Error Rate, a robust metric for translation evaluation. It provides scores that closely align with human Direct Assessment (DA) evaluations, even for low-resource languages.

Installation

Install evalNLP using pip:

pip install evalnlp

Usage

The uwer function is defined as:

def uwer(ref: str, hyp: str, cost_func=None, u_cost=None):

By default:

• The substitution cost function (cost_func) uses the Sorensen-Dice similarity measure.
• You can also use an average-based function that combines CER, similarity_rate_lcs, jaccard_coefficient_lcs, fuzzy_ratio, and Sorensen-Dice by passing "moyenne" as the cost_func parameter.
• The union cost (u_cost) is defined as described in the paper.

The uwer function works with both single strings and lists of strings.

Example with single strings:

from evalnlp import uwer

reference = "This is a reference"
hypothesis = "This is a hypothesis"
score = uwer(reference, hypothesis)
print(f"UWER Score: {score}")

Example with lists:

from evalnlp import uwer

references = ["This is a reference", "Another reference"]
hypotheses = ["This is a hypothesis", "Another hypothesis"]
scores = uwer(references, hypotheses)
print(f"UWER Scores: {scores}")