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README.md

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+# AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
+
+This repository contains the code 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](afrolm.png)
+
+## 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 this 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