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# AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages |
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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. |
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## Our self-active learning framework |
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![Model](afrolm.png) |
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## Languages Covered |
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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. |
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## Evaluation Results |
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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. |
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Model | MasakhaNER | MasakhaNER2.0* | Text Classification (Yoruba/Hausa) | Sentiment Analysis (YOSM) | OOD Sentiment Analysis (Twitter -> YOSM) | |
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`AfroLM-Large` | **80.13** | **83.26** | **82.90/91.00** | **85.40** | **68.70** | |
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`AfriBERTa` | 79.10 | 81.31 | 83.22/90.86 | 82.70 | 65.90 | |
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`mBERT` | 71.55 | 80.68 | --- | --- | --- | |
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`XLMR-base` | 79.16 | 83.09 | --- | --- | --- | |
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`AfroXLMR-base` | `81.90` | `84.55` | --- | --- | --- | |
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- (*) The evaluation was made on the 11 additional languages of the dataset. |
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## Pretrained Models and Dataset |
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**Models:**: [AfroLM-Large](https://huggingface.co/bonadossou/afrolm_active_learning) and **Dataset**: [AfroLM Dataset](https://huggingface.co/datasets/bonadossou/afrolm_active_learning_dataset) |
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## HuggingFace usage of AfroLM-large |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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model = AutoModelForTokenClassification.from_pretrained("bonadossou/afrolm_active_learning") |
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tokenizer = AutoTokenizer.from_pretrained("bonadossou/afrolm_active_learning") |
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tokenizer.model_max_length = 256 |
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``` |
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## Reproducing our result: Training and Evaluation |
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- To train the network, run `python active_learning.py`. You can also wrap it around a `bash` script. |
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- For the evaluation: |
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- NER Classification: `bash ner_experiments.sh` |
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- Text Classification & Sentiment Analysis: `bash text_classification_all.sh` |
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## Citation |
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We will share the proceeding citation as soon as possible. Stay tuned, and if you have liked our work, give it a star. |
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## Reach out |
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Do you have a question? Please create an issue and we will reach out as soon as possible |