Multi-Label-Text-Classification | Label Text Classification Based on Neural Network | Machine Learning library
kandi X-RAY | Multi-Label-Text-Classification Summary
kandi X-RAY | Multi-Label-Text-Classification Summary
This repository is my research project, and it is also a study of TensorFlow, Deep Learning (Fasttext, CNN, LSTM, etc.). The main objective of the project is to solve the multi-label text classification problem based on Deep Neural Networks. Thus, the format of the data label is like [0, 1, 0, ..., 1, 1] according to the characteristics of such a problem.
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Top functions reviewed by kandi - BETA
- Train CRNN model
- Store the best checkpoint
- Sort the best checkpoints
- Update the internal state of the internal store
- Load the best checkpoints file
- Remove outdated checkpoint files
- Train sann model
- Train a CNN model
- Train RNN model
- Train HAN
- Train fasttext model
- Train the model
- Load the data from a JSON file
- Augment data
Multi-Label-Text-Classification Key Features
Multi-Label-Text-Classification Examples and Code Snippets
Community Discussions
Trending Discussions on Multi-Label-Text-Classification
QUESTION
I developed a script that predicts probable tags for some text, based on previously manually tagged feedback. I used several online articles to help me (namely: https://towardsdatascience.com/multi-label-text-classification-with-scikit-learn-30714b7819c5).
Because I want the probability for each tag, here's the code I used:
...ANSWER
Answered 2020-Jul-14 at 12:02You could always use pickle to serialize any python object including yours. So the simplest and fastest way to save your model is to just serialize it to a file, say model.pickle. This is done in the first part after you train your model. After that, all you have to do is to check if the file exists and deserialize it using pickle again.
This is a function that serializes python objects to files:
QUESTION
I am using this excellent article to learn Machine learning.
https://stackabuse.com/python-for-nlp-multi-label-text-classification-with-keras/
The author has tokenized the X and y data after splitting it up.
...ANSWER
Answered 2019-Aug-28 at 13:25Both approaches will work in practice. But fitting the tokenizer on the train set and the applied it to both train and test set is better than fitting on the whole dataset. Indeed with the first method you are mimicking the fact that unseen words by the model will appear at some point after deploying your model. Thus your model evaluation will be closer to what will happen in a production environnement.
QUESTION
I want to use BERT model to do multi-label classification with Tensorflow.
To do so, I want to adapt the example run_classifier.py from BERT github repository, which is an example on how to use BERT to do simple classification, using the pre-trained weights given by Google Research. (For example with BERT-Base, Cased)
I have X different labels which have value of either 0 or 1, so I want to add to the original BERT model a new Dense layer of size X and using the sigmoid_cross_entropy_with_logits activation function.
So, for the theorical part I think I am OK.
The problem is that I don't know how I can append a new output layer and retrain only this new layer with my dataset, using the existing BertModel class.
Here is the original create_model() function from run_classifier.py where I guess I have to do my modifications. But I am a bit lost on what to do.
ANSWER
Answered 2019-May-07 at 10:03You want to replace the softmax that models a single distribution over possible outputs (all scores sum up to one) with sigmoid which models an independent distribution for each class (there is yes/no distribution for each output).
So, you correctly change the loss function, but you also need to change how you compute the probabilities. It should be:
QUESTION
I am trying to create a Multiclass Text Classifier as explained here. However, my code is breaking at line:
...ANSWER
Answered 2018-Oct-08 at 11:43This is one of the most over-looked issue.
The reason for this error is that the "column" script is looking for is not available in the dataframe. All the 5 categories you have, should be columns in the input dataframe and rows will take 1/0 if one of the categories is applicable for the feedback/comment. Ideally, Your input dataframe should look like this.
QUESTION
I'm going crazy in this project. This is multi-label text-classification with lstm in keras. My model is this:
...ANSWER
Answered 2018-Aug-22 at 07:50change:
QUESTION
I have written a code based on this site and made different multi-label classifiers.
I would like to evaluate my model based on accuracy per class and F1 measurement per class.
The problem is that I am getting the same number for both accuracy and f1 measurement in all models.
I am suspicious I have done something wrong. I would like to know in which circumstances this may happen.
the code is exactly the same as the site and I calculated the f1 measurement like this:
...ANSWER
Answered 2018-Aug-13 at 07:13By doing this:
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Install Multi-Label-Text-Classification
You can use Multi-Label-Text-Classification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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