Datasets:
Tasks:
Fill-Mask
Formats:
csv
Sub-tasks:
masked-language-modeling
Size:
1M - 10M
ArXiv:
Tags:
afrolm
active learning
language modeling
research papers
natural language processing
self-active learning
License:
annotations_creators: | |
- crowdsourced | |
language: | |
- amh | |
- orm | |
- lin | |
- hau | |
- ibo | |
- kin | |
- lug | |
- luo | |
- pcm | |
- swa | |
- wol | |
- yor | |
- bam | |
- bbj | |
- ewe | |
- fon | |
- mos | |
- nya | |
- sna | |
- tsn | |
- twi | |
- xho | |
- zul | |
language_creators: | |
- crowdsourced | |
license: | |
- cc-by-4.0 | |
multilinguality: | |
- monolingual | |
pretty_name: afrolm-dataset | |
size_categories: | |
- 1M<n<10M | |
source_datasets: | |
- original | |
tags: | |
- afrolm | |
- active learning | |
- language modeling | |
- research papers | |
- natural language processing | |
- self-active learning | |
task_categories: | |
- fill-mask | |
task_ids: | |
- masked-language-modeling | |
# AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages | |
- [GitHub Repository of the Paper](https://github.com/bonaventuredossou/MLM_AL) | |
This repository contains the dataset for our paper [`AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages`](https://arxiv.org/pdf/2211.03263.pdf) 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 the average F1-score performances 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. | |
- Bold numbers represent the performance of the model with the **smallest pretrained data**. | |
## 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) | |
## HuggingFace usage of AfroLM-large | |
```python | |
from transformers import XLMRobertaModel, XLMRobertaTokenizer | |
model = XLMRobertaModel.from_pretrained("bonadossou/afrolm_active_learning") | |
tokenizer = XLMRobertaTokenizer.from_pretrained("bonadossou/afrolm_active_learning") | |
tokenizer.model_max_length = 256 | |
``` | |
`Autotokenizer` class does not successfully load our tokenizer. So we recommend using directly the `XLMRobertaTokenizer` class. Depending on your task, you will load the according mode of the model. Read the [XLMRoberta Documentation](https://huggingface.co/docs/transformers/model_doc/xlm-roberta) | |
## 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 | |
``@inproceedings{dossou-etal-2022-afrolm, | |
title = "{A}fro{LM}: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 {A}frican Languages", | |
author = "Dossou, Bonaventure F. P. and | |
Tonja, Atnafu Lambebo and | |
Yousuf, Oreen and | |
Osei, Salomey and | |
Oppong, Abigail and | |
Shode, Iyanuoluwa and | |
Awoyomi, Oluwabusayo Olufunke and | |
Emezue, Chris", | |
booktitle = "Proceedings of The Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)", | |
month = dec, | |
year = "2022", | |
address = "Abu Dhabi, United Arab Emirates (Hybrid)", | |
publisher = "Association for Computational Linguistics", | |
url = "https://aclanthology.org/2022.sustainlp-1.11", | |
pages = "52--64",}`` | |
## Reach out | |
Do you have a question? Please create an issue and we will reach out as soon as possible |