README.md

---
multilinguality: mulyilingual
language: 
- ny
- kg
- kmb
- rw
- ln
- lua
- lg
- nso
- rn
- st
- sw
- ss
- ts
- tn
- tum
- umb
- xh
- zu
license: apache-2.0
widget:
  - text: "gari langu lilipata ajali jana usiku"
  - text: "ndamcela ukuba ahambe nam"
  - text: "tango nafungolaki porte azalaki déjà te"
---

### Scores
```python
{'eval_accuracy': 0.87955,
 'eval_f1_score': 0.8794755507923356,
 'eval_recall': 0.8797246969797138,
 'eval_precision': 0.881040811800798}
```

### How to use 
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained('nairaxo/bantu-language-identification')
model = AutoModelForSequenceClassification.from_pretrained("nairaxo/bantu-language-identification")
nlp = pipeline('text-classification', model=model, tokenizer=tokenizer)

dic = {
    'chichewa' : 0,
    'kikongo' : 1,
    'kimbundu' : 2,
    'kinyarwanda' : 3,
    'lingala' : 4,
    'lubakasai' : 5,
    'luganda' : 6,
    'northernsotho' : 7,
    'rundi' : 8,
    'southernsotho' : 9,
    'swahili' : 10,
    'swati' : 11,
    'tsonga' : 12,
    'tswana' : 13,
    'tumbuka' : 14,
    'umbundu' : 15,
    'xhosa' : 16,
    'zulu' : 17
    }

dic = {v: k for k, v in dic.items()}

sentences = [
    "gari langu lilipata ajali jana usiku",
    "ndamcela ukuba ahambe nam",
    "tango nafungolaki porte azalaki déjà te"
]

results = nlp(sentences)

for i in range(len(results)):
  results[i]['label'] = dic[int(results[i]['label'].replace('LABEL_', ''))]

print(results)


```


Output: 

```
[{'label': 'swahili', 'score': 0.9996045231819153},
 {'label': 'xhosa', 'score': 0.9882974028587341},
 {'label': 'lingala', 'score': 0.9983460903167725}]

```