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