File size: 1,402 Bytes
e641602 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
Hugging Face's logo
---
language: luo
datasets:
---
# bert-base-multilingual-cased-finetuned-luo
## Model description
**bert-base-multilingual-cased-finetuned-luo** is a **Luo BERT** model obtained by fine-tuning **bert-base-multilingual-cased** model on Luo language texts. It provides **better performance** than the multilingual BERT on named entity recognition datasets.
Specifically, this model is a *bert-base-multilingual-cased* model that was fine-tuned on Luo corpus.
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/bert-base-multilingual-cased-finetuned-luo')
>>> unmasker("Obila ma Changamwe [MASK] pedho achije angwen mag njore")
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on JW300
## Training procedure
This model was trained on a single NVIDIA V100 GPU
## Eval results on Test set (F-score, average over 5 runs)
Dataset| mBERT F1 | luo_bert F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 74.22 | 75.59
### BibTeX entry and citation info
By David Adelani
```
```
|