wolof 0.0.3

wolof is a python library for the Wolof language

Library, built on PyTorch and Transformers, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks in wolof.

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Your Library for Wolof Language

Wolof is a language spoken in Senegal in neighboring countries, many works are written in Wolof or the need to have a tool that allows us to know better this language.

Wolof library allows us to do several specific tasks in Wolof languages such as text classification, translation, automatic speech recognition.

Why Wolof library ?:

• simple and easy to use
• customizable
• clean code

Installation

Requirements

• Python >= 3.6
• Torch
• Transformers

With pip

wolof can be installed using pip as follows:

pip install wolof 

From source

pip install git+https://github.com/abdouaziz/wolof.git

Usage

from wolof.asr import Speech2Text

asr = Speech2Text(model_name="abdouaziiz/wav2vec2-xls-r-300m-wolof")

audio_file = "audio.wav"

prediction = asr.predict(audio_file)

Pipeline

The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of the complex code from the library,offering a simple API dedicated to several tasks Masked Language Modeling, Sentiment Analysis .

bert-base-wolof is pretrained bert-base model on wolof language . sora-wolof is pretrained roberta model on wolof language .

Models in Wolof library

Model name	Number of layers	Attention Heads	Embedding Dimension	Total Parameters
bert-base-wolof	6	12	514	56931622 M
soraberta-base	6	12	514	83 M

Using Soraberta or BERT-base-wolof

>>> from wolof import Pipeline
>>> unmasker = Pipeline(task='fill-mask', model_name='abdouaziiz/bert-base-wolof')
>>> unmasker("kuy yoot du [MASK].")

[{'sequence': '[CLS] kuy yoot du seqet. [SEP]',
	'score': 0.09505125880241394,
	'token': 13578},
	{'sequence': '[CLS] kuy yoot du daw. [SEP]',
	'score': 0.08882280439138412,
	'token': 679},
	{'sequence': '[CLS] kuy yoot du yoot. [SEP]',
	'score': 0.057790059596300125,
	'token': 5117},
	{'sequence': '[CLS] kuy yoot du seqat. [SEP]',
	'score': 0.05671025067567825,
	'token': 4992},
	{'sequence': '[CLS] kuy yoot du yaqu. [SEP]',
	'score': 0.0469999685883522,
	'token': 1735}]

for task we can have the following values: 'fill-mask', 'sentiment-analysis'

You can checkout examples in examples/

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Author

• Abdou Aziz DIOP @abdouaziz
• email : [e-mail]
• linkedin : https://www.linkedin.com/in/abdouaziiz/