text_classification | text classification models and more with deep learning | Machine Learning library

You should define a threshold that whenever you get a value greater than it it is considered as positive, otherwise it considers it as negative. In your example to get [1,1,0] a threshold of 0.4 for example gives the right predictions.

The vocabulary acts as a lookup table for your data translating str to int. When a given string (in this case "second\team") doesn't appear in the vocabulary, there are two strategies to compensate:

1. Throw an error because you don't know how to handle it. Imagine something like a KeyError when calling {}[1] in Python
2. Assign a default "unknown" token to the missing tokens. Imagine a default value like {}.get(1, "I don't know!") in Python.

Your code is currently doing #1. You seem to want #2 which you can achieve using vocab.set_default_index. When you build your vocab, add the specials=[""] kwarg and then call vocab.set_default_index(vocab['']).

layer.get_vocabulary() does this:

In your codes you trained a MobileBERT model, but saved to the path of average_word_vec? spec = model_spec.get('mobilebert_classifier') model.export(export_dir='average_word_vec')

One posssiblity is: you use the model of average_word_vec, but add MobileBERT metadata, thus the preprocessing doesn't match.

Could you follow the Model Maker tutorial and try again? https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_text_classification.ipynb Make sure change the export path.

This model uses a single activation function at the output (a sigmoid), used for predictions for a binary classification task.

The task to perform often guides the choice of both loss and activation functions. In this case, therefore, the Binary-Cross-Entropy loss function is used, as well as the sigmoid activation function (which is also called the logistic function, and outputs values between 0 and 1 for any real value taken as input). This is quite well explained in this post.

In contrast, you can also have multiple activation functions in a neural network, depending on its architecture; it is very common for instance in convolutional neural networks to have an activation function for each convolutional layer, as shown in this tutorial.

You can start from the official instruction, Deployed the trained model. Also here is a nice instruction step by step, check this out. Deploy Tensorflow Model To Production - Part 3 (Creating REST API). And blog post on this. Creating REST API for TensorFlow models

Your dense layer is linear which means you don't have any activation passed into it. There are several solutions, as you are doing binary_classification.

If you want to use Dense(1) as it is, then you should change loss function into:

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Short answer: Use test_data[0:1] instead of test_data[0].

Long answer: The Keras/TF models works on batch of input samples. Therefore, when you give them only one input sample, it should still have a shape of (1, sample_shape). However, when you slice the test_data array like test_data[0] it would give you the first element with the first axis/dimension removed, i.e. with the shape of (sample_shape,) (in this case (256,)). To resolve this, use test_data[0:1] in order to preserve the first axis/dimension (i.e. shape would be (1, 256)).