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--- |
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language: sw |
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datasets: |
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- kenyacorpus_v2 |
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license: cc-by-4.0 |
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model-index: |
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- name: innocent-charles/Swahili-question-answer-latest-cased |
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results: |
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- task: |
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type: question-answering |
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name: Question Answering |
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dataset: |
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name: kenyacorpus |
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type: kenyacorpus |
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config: kenyacorpus |
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split: validation |
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metrics: |
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- name: Exact Match |
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type: exact_match |
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value: 79.9309 |
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verified: true |
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- name: F1 |
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type: f1 |
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value: 82.9501 |
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verified: true |
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- name: total |
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type: total |
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value: 11869 |
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verified: true |
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--- |
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# SWAHILI QUESTION - ANSWER MODEL |
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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. |
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## Overview |
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**Language model used:** bert-base-multilingual-cased |
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**Language:** Kiswahili |
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**Downstream-task:** Extractive Swahili QA |
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**Training data:** KenyaCorpus |
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**Eval data:** KenyaCorpus |
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**Code:** See [an example QA pipeline on Haystack](https://haystack.deepset.ai) |
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**Infrastructure**: Google Colab GPU |
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## Hyperparameters |
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``` |
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batch_size = 16 |
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n_epochs = 10 |
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base_LM_model = "bert-base-multilingual-cased" |
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max_seq_len = 386 |
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learning_rate = 3e-5 |
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lr_schedule = LinearWarmup |
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warmup_proportion = 0.2 |
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doc_stride=128 |
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max_query_length=64 |
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``` |
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## Usage |
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### In Haystack |
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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/): |
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```python |
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reader = FARMReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased") |
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# or |
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reader = TransformersReader(model_name_or_path="innocent-charles/Swahili-question-answer-latest-cased",tokenizer="innocent-charles/Swahili-question-answer-latest-cased") |
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``` |
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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) |
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### In Transformers |
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```python |
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from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline |
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model_name = "innocent-charles/Swahili-question-answer-latest-cased" |
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# a) Get predictions |
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nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) |
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QA_input = { |
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'question': 'Asubuhi ilitupata pambajioi pa hospitali gani?', |
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'context': 'Asubuhi hiyo ilitupata pambajioni pa hospitali ya Uguzwa.' |
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} |
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res = nlp(QA_input) |
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# b) Load model & tokenizer |
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model = AutoModelForQuestionAnswering.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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``` |
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## Performance |
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``` |
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"exact": 79.87029394424324, |
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"f1": 82.91251169582613, |
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"total": 11873, |
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"HasAns_exact": 77.93522267206478, |
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"HasAns_f1": 84.02838248389763, |
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"HasAns_total": 5928, |
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"NoAns_exact": 81.79983179142137, |
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"NoAns_f1": 81.79983179142137, |
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"NoAns_total": 5945 |
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``` |
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## Authors |
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**Innocent Charles:** contact@innocentcharles.com |
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## About Me |
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<P> |
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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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</P> |
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[Linkedin](https://www.linkedin.com/in/innocent-charles/) | [GitHub](https://github.com/innocent-charles) | [Website](innocentcharles.com) |
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