README.md

---
language: sw
datasets:
- kenyacorpus_v2
license: cc-by-4.0
model-index:
- name: innocent-charles/Swahili-question-answer-latest-cased
  results:
  - task:
      type: question-answering
      name: Question Answering
    dataset:
      name: kenyacorpus
      type: kenyacorpus
      config: kenyacorpus
      split: validation
    metrics:
    - name: Exact Match
      type: exact_match
      value: 79.9309
      verified: true
    - name: F1
      type: f1
      value: 82.9501
      verified: true
    - name: total
      type: total
      value: 11869
      verified: true
---

# SWAHILI QUESTION - ANSWER MODEL

This is the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model, fine-tuned using the [KenyaCorpus](https://github.com/Neurotech-HQ/Swahili-QA-dataset) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering in Swahili Language.

Question answering (QA) is a computer science discipline within the fields of information retrieval and NLP that help in the development of systems in such a way that, given a question in natural language, can extract relevant information from provided data and present it in the form of natural language answers.


## Overview
**Language model used:** bert-base-multilingual-cased  
**Language:** Kiswahili 
**Downstream-task:** Extractive Swahili QA  
**Training data:** KenyaCorpus 
**Eval data:** KenyaCorpus 
**Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai)  
**Infrastructure**: AWS NVIDIA A100 Tensor Core GPU 

## Hyperparameters

```
batch_size = 16
n_epochs = 10
base_LM_model = "bert-base-multilingual-cased"
max_seq_len = 386
learning_rate = 3e-5
lr_schedule = LinearWarmup
warmup_proportion = 0.2
doc_stride=128
max_query_length=64
``` 

## Usage

### In Haystack
Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
```python
reader = FARMReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased")
# or 
reader = TransformersReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased",tokenizer="innocent-charles/Swahili-question-answer-latest-cased")
```
For a complete example of ``Swahili-question-answer-latest-cased`` being used for Swahili Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai)

### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "innocent-charles/Swahili-question-answer-latest-cased"

# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
    'question': 'Asubuhi ilitupata pambajioi pa hospitali gani?',
    'context': 'Asubuhi hiyo ilitupata pambajioni pa hospitali ya Uguzwa.'
}
res = nlp(QA_input)

# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Performance

```
"exact": 79.87029394424324,
"f1": 82.91251169582613,

"total": 11873,
"HasAns_exact": 77.93522267206478,
"HasAns_f1": 84.02838248389763,
"HasAns_total": 5928,
"NoAns_exact": 81.79983179142137,
"NoAns_f1": 81.79983179142137,
"NoAns_total": 5945
```

## Authors
**Innocent Charles:** [email protected]  

## About Me

<P>
I build good things using Artificial Intelligence ,Data and Analytics , with over 3 Years of Experience as Applied AI Engineer & Data scientist from a strong background in Software Engineering ,with passion and extensive experience in Data and Businesses.
</P>


[Linkedin](https://www.linkedin.com/in/innocent-charles/) | [GitHub](https://github.com/innocent-charles) | [Website](innocentcharles.com)