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 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.
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
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:
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)
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
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.