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AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages

This repository contains the model for our paper AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages which will appear at the Third Simple and Efficient Natural Language Processing, at EMNLP 2022.

Our self-active learning framework

Model

Languages Covered

AfroLM has been pretrained from scratch on 23 African Languages: Amharic, Afan Oromo, Bambara, Ghomalá, Éwé, Fon, Hausa, Ìgbò, Kinyarwanda, Lingala, Luganda, Luo, Mooré, Chewa, Naija, Shona, Swahili, Setswana, Twi, Wolof, Xhosa, Yorùbá, and Zulu.

Evaluation Results

AfroLM was evaluated on MasakhaNER1.0 (10 African Languages) and MasakhaNER2.0 (21 African Languages) datasets; on text classification and sentiment analysis. AfroLM outperformed AfriBERTa, mBERT, and XLMR-base, and was very competitive with AfroXLMR. AfroLM is also very data efficient because it was pretrained on a dataset 14x+ smaller than its competitors' datasets. Below is the average of performance of various models, across various datasets. Please consult our paper for more language-level performance.

Model MasakhaNER MasakhaNER2.0* Text Classification (Yoruba/Hausa) Sentiment Analysis (YOSM) OOD Sentiment Analysis (Twitter -> YOSM)
AfroLM-Large 80.13 83.26 82.90/91.00 85.40 68.70
AfriBERTa 79.10 81.31 83.22/90.86 82.70 65.90
mBERT 71.55 80.68 --- --- ---
XLMR-base 79.16 83.09 --- --- ---
AfroXLMR-base 81.90 84.55 --- --- ---
  • (*) The evaluation was made on the 11 additional languages of the dataset.

Pretrained Models and Dataset

Models:: AfroLM-Large and Dataset: AfroLM Dataset

HuggingFace usage of AfroLM-large 

from transformers import AutoTokenizer, AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer = AutoTokenizer.from_pretrained("bonadossou/afrolm_active_learning")
tokenizer.model_max_length = 256

Reproducing our result: Training and Evaluation

  • To train the network, run python active_learning.py. You can also wrap it around a bash script.
  • For the evaluation:
    • NER Classification: bash ner_experiments.sh
    • Text Classification & Sentiment Analysis: bash text_classification_all.sh

Citation

We will share the proceeding citation as soon as possible. Stay tuned. If you have liked our work, give it a star.

Reach out

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