| # English to Sesotho | |
| Author: [Jason Webster](https://github.com/jasonrobwebster/) | |
| A `en_st_process.ipynb` and `en_st_train_from_drive.ipynb` notebook are present in order to be able to run the processing and training steps at two different times. The `fuzzywuzzy` preprocessing step took long enough to consume a full Google Colab instance's time allocation, so a seperate notebook was needed in order to train the model from the preprocessed data. The `en_st_train_from_drive.ipynb` notebook also contains code for testing on the Autshumato dataset, though these results are reported in a seperate folder. | |
| ## Training Data | |
| - The JW300 dataset. | |
| ## Preprocessing | |
| - Removed duplicate sentences | |
| - Removed similar sentences with fuzzywuzzy and a similarity score above 0.95 | |
| - 40 000 BPE codes used | |
| ## Model | |
| - Default Masakhane Transformer translation model (see `en_st_train_from_drive.ipynb` for detailed config) | |
| - Link to google drive folder with model (https://drive.google.com/drive/folders/1--C5IwyI_P0B4_-nAsBDaBgrGqm5ABvY?usp=sharing) | |
| - Trained for approx 9h30min | |
| ## Analysis | |
| - TODO | |
| # Results | |
| - JW300 `test.en` and `test.st` | |
| - BLEU dev: 46.15 | |
| - BLEU test: 41.23 | |
| - Note: It is probably best to train this model for longer, as it was close to timing out on Google Colab (was manually stopped) | |
| - Note: Will likely benefit from optimising the number of BPE codes |