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tinytranslate
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Machine translation with tinygrad
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README.md
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tinytranslate
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# tinytranslate Machine translation demo built on [tinygrad](https://github.com/tinygrad/tinygrad) Tinytranslate is a demo of using Transformer neural networks for machine translation. Tinytranslate is very lightweight and can train from scratch on a laptop CPU in a few minutes allowing for fast iteration and experimentation. Tinytranslate is a lightweight tech demo and is not suitable for real world translations; use [Argos Translate](https://github.com/argosopentech/argos-translate) if you need a production translation system. #### Quickstart ``` # Create Virtual Environment (optional) virtualenv env source env/bin/activate # Install tinygrad git clone https://github.com/tinygrad/tinygrad.git cd tinygrad pip install -e . cd .. # Download TinyTranslate source code git clone https://github.com/PJ-Finlay/tinytranslate cd tinytranslate # Download training data ./scripts/download_data.sh # Run training python translate.py ``` #### Example output: ``` loss 2.43 accuracy 0.37: 100%|█████████████████████████████████████████████████████████████████████████████| 100/100 [00:41<00:00, 2.41it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:03<00:00, 4.17it/s] test set accuracy is 0.352500 reducing lr to 0.0017 Source: The provin Target: Las provin Pred: Ea proein ... loss 0.51 accuracy 0.81: 100%|█████████████████████████████████████████████████████████████████████████████| 100/100 [00:36<00:00, 2.72it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:03<00:00, 4.30it/s] test set accuracy is 0.799050 reducing lr to 0.0012 Source: 52+88 Target: 140 Pred: 110 loss 0.58 accuracy 0.80: 100%|█████████████████████████████████████████████████████████████████████████████| 100/100 [00:36<00:00, 2.73it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:03<00:00, 4.36it/s] test set accuracy is 0.823600 reducing lr to 0.0010 Source: 51+43 Target: 94 Pred: 94 loss 0.46 accuracy 0.82: 100%|█████████████████████████████████████████████████████████████████████████████| 100/100 [00:36<00:00, 2.75it/s] 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 16/16 [00:03<00:00, 4.32it/s] test set accuracy is 0.836800 reducing lr to 0.0008 Source: 1+8 Target: 9 Pred: 50 ``` #### Data Sources - [Opus Parallel Corpora](https://opus.nlpl.eu/) - [Argos Data](https://github.com/argosopentech/argos-data/blob/main/builddataset.go)
