bert-base-multilingual-cased-finetuned-igbo
| Property | Value |
|---|---|
| Author | Davlan |
| Model Type | Fine-tuned BERT |
| Base Model | bert-base-multilingual-cased |
| Training Hardware | NVIDIA V100 GPU |
| Performance | 86.75% F1 Score on MasakhaNER |
What is bert-base-multilingual-cased-finetuned-igbo?
This is a specialized BERT model specifically fine-tuned for the Igbo language, based on the bert-base-multilingual-cased architecture. It has been optimized using a comprehensive dataset including JW300, OPUS CC-Align, IGBO NLP Corpus, and Igbo CC-100, making it particularly effective for Igbo language processing tasks.
Implementation Details
The model was trained on a single NVIDIA V100 GPU and demonstrates superior performance compared to the standard multilingual BERT, particularly in text classification and named entity recognition tasks. It achieved an impressive F1 score of 86.75% on the MasakhaNER dataset, surpassing the base mBERT's score of 85.11%.
- Fine-tuned on diverse Igbo language datasets
- Optimized for masked token prediction
- Supports case-sensitive text processing
- Enhanced performance for NER tasks
Core Capabilities
- Named Entity Recognition in Igbo text
- Text classification for Igbo language
- Masked token prediction
- Context-aware language understanding
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on the Igbo language and its improved performance over the standard multilingual BERT model, particularly in NER tasks. It's fine-tuned on a comprehensive collection of Igbo language datasets, making it particularly effective for Igbo-specific applications.
Q: What are the recommended use cases?
The model is best suited for tasks involving Igbo language processing, particularly named entity recognition and text classification. It can be effectively used with the Transformers pipeline for masked token prediction and various NLP tasks specific to Igbo language content.
