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This is the bert-base-multilingual-cased model, fine-tuned using the KenyaCorpus 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.


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
Infrastructure: AWS NVIDIA A100 Tensor Core GPU


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


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:

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

In Transformers

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)


"exact": 51.87029394424324,
"f1": 63.91251169582613,

"total": 445,
"HasAns_exact": 50.93522267206478,
"HasAns_f1": 62.02838248389763,
"HasAns_total": 386,
"NoAns_exact": 49.79983179142137,
"NoAns_f1": 60.79983179142137,
"NoAns_total": 59

Special consideration

The project is still going, hence the model is still updated after training the model in more data, Therefore pull requests are welcome to contribute to increase the performance of the model.


Innocent Charles: contact@innocentcharles.com

About Me

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.

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Evaluation results