| # 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 | |
|  | |
| ## 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](https://huggingface.co/bonadossou/afrolm_active_learning) and **Dataset**: [AfroLM Dataset](https://huggingface.co/datasets/bonadossou/afrolm_active_learning_dataset) | |
| ## 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 | |
| Do you have a question? Please create an issue and we will reach out as soon as possible |