Update README.md

README.md

@@ -228,9 +228,9 @@ Data downloading | Downloads datasets, samples sentences | Network, Disk | Time
 Data cleaning | Basic preprocessing, dataset specific, language specific, rule based and other attempts to clean noisy data in parallel and mono datasets | CPU | Good parallelization across CPU cores. To make cleaning of a new language more efficient add it to [clean_parallel.py](/pipeline/clean/tools/clean_parallel.py).
 Bicleaner | Filters noisy sentence pairs in a parallel corpus using [bicleaner](https://github.com/bitextor/bicleaner) or [bicleaner-ai](https://github.com/bitextor/bicleaner-ai) depending on available language packs. | CPU, GPU | If there are no pretrained language packs for bicleaner-ai, it uses bicleaner. If there are no ones for bicleaner either, this step is skipped. Cleaning thresholds are configurable per dataset, see [Dataset cleaning](##Dataset cleaning).
 Merge and dedupe | Merges clean dataset and applies deduplicaiton | CPU, Disk | 
+Training vocabulary | Trains [SentencePiece](https://github.com/google/sentencepiece) vocabulary/tokenizer model on parallel corpus. | CPU |
 Training s2s | Trains a backward shallow s2s model, which is useful for back-translations and ce-filtering | GPU | Inspired by a [marian example](https://github.com/marian-nmt/marian-examples/tree/master/training-basics-sentencepiece).
 Augmentation with back-translations | Translates mono corpus combined from monolingual datasets in target language using shallow s2s model. | GPU | It is more useful for low-resource languages and can be skipped for others.
-Training vocabulary | Trains [SentencePiece](https://github.com/google/sentencepiece) vocabulary/tokenizer model on parallel corpus. | CPU |
 Training teacher | Trains an ensemble of big transformer models on augmented dataset | GPU | You might want to adjust [early stopping](pipeline/train/configs/training/teacher.transformer.train.yml) or `after-epochs` parameters depending on datasets size.
 Fine-tuning teacher | Continue training an ensemble of teachers on parallel data only | GPU | You might want to adjust [early stopping](pipeline/train/configs/training/teacher.transformer.train.yml) parameters depending on datasets size.
 Translation by teacher | Translates a corpus and monolingual data combined from configurable `dataset.mono-src` using the ensemble of teacher models | GPU | The slowest part of the pipeline. Can take days. It is possible to speed it up by using multiple nodes in cluster mode.