README.md

metadata

language: pt
license: mit
tags:
  - question-answering
  - bert
  - bert-large
  - pytorch
datasets:
  - autogenerated
metrics:
  - squad
widget:
  - text: Lucas Vázquez Iglesias ana miaka mingapi?
    context: >-
      Lucas Vázquez Iglesias (aliyezaliwa 1 Julai 1991) ni mchezaji wa soka wa
      Hispania ambaye anachezea klabu ya Real Madrid na timu ya taifa ya
      Hispania kama winga wa kulia.
  - text: Emil von Zelewski aliuawa katika vita gani?
    context: >-
      Emil von Zelewski (13 Machi 1854 - 1891) alikuwa afisa wa jeshi la
      Ujerumani. Alipokuwa kamanda ya kwanza wa jeshi la ulinzi la kikoloni
      katika Afrika ya Mashariki ya Kijerumani aliongoza jeshi hilo katika vita
      dhidi ya Wahehe alipouawa.

Swahili MCR & QA: a Swahili Machine Reading Comprehension and Question Answering model

Table of Contents

• Model Details
• Uses
• Training
• Evaluation

Model Details

• Model Description: This is the first Swahili MCR Question Answering Model. It is now available on Hugging Face.
• Developed by: Mohamed Gudle.
• Model Type: Fine-tuned Question Answering
• Language(s): Swahili
• Parent Model: See the bert-base-multilingual-uncased for more information .
• Resources for more information:

Uses

Direct Use

This model can be used for Machine Reading and Question Answering tasks.

Risks, Limitations and Biases

mgudle/bert-finetuned-swahili_qa

This model is a fine-tuned version of bert-base-multilingual-uncased on mgudle/swahili_qa dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.3585
• Epoch: 2

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1023, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: mixed_float16

Training results

Train Loss	Epoch
1.1602	0
0.5513	1
0.3585	2

Framework versions

• Transformers 4.20.1
• TensorFlow 2.8.2
• Datasets 2.3.2
• Tokenizers 0.12.1