| ## Data | |
| JW300 : English-Zulu | |
| ## Model Architecture | |
| ### Text Preprocessing | |
| - Remove blank/empty rows : 9037(0.85 %) samples | |
| - Removed duplicates from source text : 82999(7.88 %) samples | |
| - Removed duplicates from target text : 5045(0.52 %) samples | |
| - Removed all numeric-only text : 182(0.02 %) samples | |
| - Removed rows where text is fewer than orequal to 8 characters long from source text: 6272(0.65 %) samples | |
| - Removed rows where text is fewer than orequal to 8 characters long from target text: 713(0.07 %) samples | |
| - Removed rows where text is in test set: 1068(0.11 %) samples | |
| ### BPE Tokenization | |
| - vocab size : 4000 (superior results than 10X) | |
| ### Model Config | |
| - Details in supplied config file but used fewer transformer layers than in default notebook, with more attention heads and lower embedding size | |
| - Trained for 235000 steps | |
| - Took few hours on a single P100 GPU on Google colab over a three days (stopped training saved best model then reloaded that model the next day) | |
| ## Results | |
| ### Curious analysis of the tokenization | |
| > There are 66255 english tokens in the test set vocab, 2072 are unique | |
| > | |
| > There are 67851 zulu tokens in the test set vocab, 2336 are unique | |
| > | |
| > These results are in the same notebook as used for training. (Could something similar help inform BPE vocab size choices ?) | |
| ### Translation results | |
| > 2019-11-13 07:43:32,728 Hello! This is Joey-NMT. | |
| > | |
| > 2019-11-13 07:44:03,502 dev bleu: 13.64 [Beam search decoding with beam size = 5 and alpha = 1.0] | |
| > | |
| > 2019-11-13 07:44:24,289 test bleu: 4.87 [Beam search decoding with beam size = 5 and alpha = 1.0]` | |
| Download model weights from : [here](https://drive.google.com/open?id=1-QLxP7xLqu-AqDQkm1XaCtDEex1Oseo0) | |