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 | |
|  | |
| ## 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 |