ROmanian Deep Neural networks Architectures (RODNA) is a Python 3/Tensorflow/Keras project with the declared goal of obtaining better results at Romanian text processing through the use of Romanian-specific features than generic, language-independent ML toolkits.
The project is under development. Check back regularly for a stable version.
All training is done on a machine with two NVIDIA cards with 10421 MB memory each: name: GeForce GTX 1080 Ti, compute capability: 6.1
A RNN neural network that does sentence splitting.
10 epochs
loss: 3.0740e-04 - categorical_accuracy: 0.9999
on_epoch_end: SENTEND dev precision at epoch 10 is P = 0.9961
on_epoch_end: SENTEND dev recall at epoch 10 is R = 0.99
on_epoch_end: SENTEND dev f-measure at epoch 10 is F1 = 0.993
_train_keras_model: SENTEND test Precision is P = 0.9976
_train_keras_model: SENTEND test Recall is R = 0.9904
_train_keras_model: SENTEND test F-measure is F1 = 0.994
real 2m42.815s user 2m51.797s sys 0m5.996s
A RNN neural network than learns the mapping from a word form to its possible morpho-syntactic labels.
50 epochs loss: 2.0031e-04 - true_positives: 1013132.0000 - precision: 0.9798 - recall: 0.9741
val_loss: 7.8529e-04 - val_true_positives: 107739.0000 - val_precision: 0.9409 - val_recall: 0.9335
real 14m45.532s user 21m7.556s sys 1m48.665s
To be continued...