Comments (3)
Actually, we do have checkpoints saved after each epoch, but there is no resume capability. This should not be too hard. Each network is able to load a pretrained model. We only have to restructure the entry training method to check if a model was provided via command line and load it after it initializes the network
from nlp-cube.
We need to do the following:
- Keep track of model metrics, and save them to disk
- Load model from the path where it was saved
- Load model metrics from disk
embeddings = WordEmbeddings()
embeddings.read_from_file(params.embeddings, encodings.word_list)
# Load parser from file, instead of using BDRNNParser.
path = params.output_base + '.last'
print('Loading model from path: {}'.format(path))
parser.load(path)
trainer = ParserTrainer(parser, encodings, params.itters, trainset, devset, testset)
trainer.start_training(params.output_base, params.batch_size)
from nlp-cube.
This is a rare use-case. It's not speech/voice processing. Training is a lot faster and it does not require resume capabilities.
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Related Issues (20)
- Adobe-wide pypi deployment credentials HOT 2
- Issues with nn(Norwegian nynorsk) and nb(Norwegian bokmål). HOT 1
- Missing files in sdist
- Does cube support enhanced/collapsed dependency parsing. HOT 2
- license type of models HOT 1
- The future of the Cube project? HOT 2
- Getting stuck at "Configuring tzdata" HOT 5
- The NlpCloud seems to be down HOT 4
- Error while training a model HOT 9
- ERROR in app: Exception on /nlp [GET] HOT 4
- Kazakh language wrong result HOT 3
- Kazakh language wrong result HOT 1
- Russian model of nlpcube 0.3.1.0 does not work HOT 3
- Greek model of nlpcube 0.3.1.0 separates punctuation into other words HOT 3
- Kazakh model of nlpcube 0.3.1.0 does not tokenize well HOT 2
- Additional language models needed HOT 17
- Issues with different language models HOT 10
- ModuleNotFoundError when launching NLP-Cube HOT 2
- numpy<1.20.0 required HOT 3
- Problem with model loading HOT 3
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