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Tag: training-data

How to label multi-word entities?

I’m quite new to data analysis (and Python in general), and I’m currently a bit stuck in my project. For my NLP-task I need to create training data, i.e. find specific entities in sentences and label them. I have multiple csv files containing the entities I am trying to find, many of them consisting of multiple words. I have tokenized

Predictions become irrational after adding weights to the fit [closed]

Closed. This question needs debugging details. It is not currently accepting answers. Edit the question to include desired behavior, a specific problem or error, and the shortest code necessary to reproduce the problem. This will help others answer the question. Closed last year. Improve this question I have a model with several dense layers that behaves normally in all aspects.

Training a single model jointly over multiple datasets in tensorflow

I want to train a single variational autoencoder model or even a standard autoencoder over many datasets jointly (e.g. mnist, cifar, svhn, etc. where all the images in the datasets are resized to be the same input shape). Here is the VAE tutorial in tensorflow which I am using as a starting point: For training the model, I would

PyTorch DataLoader shuffle

I did an experiment and I did not get the result I was expecting. For the first part, I am using I save trainloader.dataset.targets to the variable a, and to the variable b before training my model. Then, I train the model using trainloader. After the training is finished, I save trainloader.dataset.targets to the variable c, and to